digital banking research articles

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• Published: 18 June 2021

Financial technology and the future of banking

• Daniel Broby   ORCID: orcid.org/0000-0001-5482-0766 1  

Financial Innovation volume  7 , Article number:  47 ( 2021 ) Cite this article

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This paper presents an analytical framework that describes the business model of banks. It draws on the classical theory of banking and the literature on digital transformation. It provides an explanation for existing trends and, by extending the theory of the banking firm, it illustrates how financial intermediation will be impacted by innovative financial technology applications. It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability. Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks. The paper contributes to an understanding of the future of banking, providing a framework for scholarly empirical investigation. In the discussion, four possible strategies are proposed for market participants, (1) customer retention, (2) customer acquisition, (3) banking as a service and (4) social media payment platforms. It is concluded that, in an increasingly digital world, trust will remain at the core of banking. That said, liquidity transformation will still have an important role to play. The nature of banking and financial services, however, will change dramatically.

Introduction

The bank of the future will have several different manifestations. This paper extends theory to explain the impact of financial technology and the Internet on the nature of banking. It provides an analytical framework for academic investigation, highlighting the trends that are shaping scholarly research into these dynamics. To do this, it re-examines the nature of financial intermediation and transactions. It explains how digital banking will be structurally, as well as physically, different from the banks described in the literature to date. It does this by extending the contribution of Klein ( 1971 ), on the theory of the banking firm. It presents suggested strategies for incumbent, and challenger banks, and how banking as a service and social media payment will reshape the competitive landscape.

The banking industry has been evolving since Banca Monte dei Paschi di Siena opened its doors in 1472. Its leveraged business model has proved very scalable over time, but it is now facing new challenges. Firstly, its book to capital ratios, as documented by Berger et al ( 1995 ), have been consistently falling since 1840. This trend continues as competition has increased. In the past decade, the industry has experienced declines in profitability as measured by return on tangible equity. This is partly the result of falling leverage and fee income and partly due to the net interest margin (connected to traditional lending activity). These trends accelerated following the 2008 financial crisis. At the same time, technology has made banks more competitive. Advances in digital technology are changing the very nature of banking. Banks are now distributing services via mobile technology. A prolonged period of very low interest rates is also having an impact. To sustain their profitability, Brei et al. ( 2020 ) note that many banks have increased their emphasis on fee-generating services.

As Fama ( 1980 ) explains, a bank is an intermediary. The Internet is, however, changing the way financial service providers conduct their role. It is fundamentally changing the nature of the banking. This in turn is changing the nature of banking services, and the way those services are delivered. As a consequence, in order to compete in the changing digital landscape, banks have to adapt. The banks of the future, both incumbents and challengers, need to address liquidity transformation, data, trust, competition, and the digitalization of financial services. Against this backdrop, incumbent banks are focused on reinventing themselves. The challenger banks are, however, starting with a blank canvas. The research questions that these dynamics pose need to be investigated within the context of the theory of banking, hence the need to revise the existing analytical framework.

Banks perform payment and transfer functions for an economy. The Internet can now facilitate and even perform these functions. It is changing the way that transactions are recorded on ledgers and is facilitating both public and private digital currencies. In the past, banks operated in a world of information asymmetry between themselves and their borrowers (clients), but this is changing. This differential gave one bank an advantage over another due to its knowledge about its clients. The digital transformation that financial technology brings reduces this advantage, as this information can be digitally analyzed.

Even the nature of deposits is being transformed. Banks in the future will have to accept deposits and process transactions made in digital form, either Central Bank Digital Currencies (CBDC) or cryptocurrencies. This presents a number of issues: (1) it changes the way financial services will be delivered, (2) it requires a discussion on resilience, security and competition in payments, (3) it provides a building block for better cross border money transfers and (4) it raises the question of private and public issuance of money. Braggion et al ( 2018 ) consider whether these represent a threat to financial stability.

The academic study of banking began with Edgeworth ( 1888 ). He postulated that it is based on probability. In this respect, the nature of the business model depends on the probability that a bank will not be called upon to meet all its liabilities at the same time. This allows banks to lend more than they have in deposits. Because of the resultant mismatch between long term assets and short-term liabilities, a bank’s capital structure is very sensitive to liquidity trade-offs. This is explained by Diamond and Rajan ( 2000 ). They explain that this makes a bank a’relationship lender’. In effect, they suggest a bank is an intermediary that has borrowed from other investors.

Diamond and Rajan ( 2000 ) argue a lender can negotiate repayment obligations and that a bank benefits from its knowledge of the customer. As shall be shown, the new generation of digital challenger banks do not have the same tradeoffs or knowledge of the customer. They operate more like a broker providing a platform for banking services. This suggests that there will be more than one type of bank in the future and several different payment protocols. It also suggests that banks will have to data mine customer information to improve their understanding of a client’s financial needs.

The key focus of Diamond and Rajan ( 2000 ), however, was to position a traditional bank is an intermediary. Gurley and Shaw ( 1956 ) describe how the customer relationship means a bank can borrow funds by way of deposits (liabilities) and subsequently use them to lend or invest (assets). In facilitating this mediation, they provide a service whereby they store money and provide a mechanism to transmit money. With improvements in financial technology, however, money can be stored digitally, lenders and investors can source funds directly over the internet, and money transfer can be done digitally.

A review of financial technology and banking literature is provided by Thakor ( 2020 ). He highlights that financial service companies are now being provided by non-deposit taking contenders. This paper addresses one of the four research questions raised by his review, namely how theories of financial intermediation can be modified to accommodate banks, shadow banks, and non-intermediated solutions.

To be a bank, an entity must be authorized to accept retail deposits. A challenger bank is, therefore, still a bank in the traditional sense. It does not, however, have the costs of a branch network. A peer-to-peer lender, meanwhile, does not have a deposit base and therefore acts more like a broker. This leads to the issue that this paper addresses, namely how the banks of the future will conduct their intermediation.

In order to understand what the bank of the future will look like, it is necessary to understand the nature of the aforementioned intermediation, and the way it is changing. In this respect, there are two key types of intermediation. These are (1) quantitative asset transformation and, (2) brokerage. The latter is a common model adopted by challenger banks. Figure  1 depicts how these two types of financial intermediation match savers with borrowers. To avoid nuanced distinction between these two types of intermediation, it is common to classify banks by the services they perform. These can be grouped as either private, investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury management, brokerage, and other agency services.

How banks act as intermediaries between lenders and borrowers. This function call also be conducted by intermediaries as brokers, for example by shadow banks. Disintermediation occurs over the internet where peer-to-peer lenders match savers to lenders

Financial technology has the ability to disintermediate the banking sector. The competitive pressures this results in will shape the banks of the future. The channels that will facilitate this are shown in Fig.  2 , namely the Internet and/or mobile devices. Challengers can participate in this by, (1) directly matching borrows with savers over the Internet and, (2) distributing white labels products. The later enables banking as a service and avoids the aforementioned liquidity mismatch.

The strategic options banks have to match lenders with borrowers. The traditional and challenger banks are in the same space, competing for business. The distributed banks use the traditional and challenger banks to white label banking services. These banks compete with payment platforms on social media. The Internet heralds an era of banking as a service

There are also physical changes that are being made in the delivery of services. Bricks and mortar branches are in decline. Mobile banking, or m-banking as Liu et al ( 2020 ) describe it, is an increasingly important distribution channel. Robotics are increasingly being used to automate customer interaction. As explained by Vishnu et al ( 2017 ), these improve efficiency and the quality of execution. They allow for increased oversight and can be built on legacy systems as well as from a blank canvas. Application programming interfaces (APIs) are bringing the same type of functionality to m-banking. They can be used to authorize third party use of banking data. How banks evolve over time is important because, according to the OECD, the activity in the financial sector represents between 20 and 30 percent of developed countries Gross Domestic Product.

In summary, financial technology has evolved to a level where online banks and banking as a service are challenging incumbents and the nature of banking mediation. Banking is rapidly transforming because of changes in such technology. At the same time, the solving of the double spending problem, whereby digital money can be cryptographically protected, has led to the possibility that paper money will become redundant at some point in the future. A theoretical framework is required to understand this evolving landscape. This is discussed next.

The theory of the banking firm: a revision

In financial theory, as eloquently explained by Fama ( 1980 ), banking provides an accounting system for transactions and a portfolio system for the storage of assets. That will not change for the banks of the future. Fama ( 1980 ) explains that their activities, in an unregulated state, fulfil the Modigliani–Miller ( 1959 ) theorem of the irrelevance of the financing decision. In practice, traditional banks compete for deposits through the interest rate they offer. This makes the transactional element dependent on the resulting debits and credits that they process, essentially making banks into bookkeeping entities fulfilling the intermediation function. Since this is done in response to competitive forces, the general equilibrium is a passive one. As such, the banking business model is vulnerable to disruption, particularly by innovation in financial technology.

A bank is an idiosyncratic corporate entity due to its ability to generate credit by leveraging its balance sheet. That balance sheet has assets on one side and liabilities on the other, like any corporate entity. The assets consist of cash, lending, financial and fixed assets. On the other side of the balance sheet are its liabilities, deposits, and debt. In this respect, a bank’s equity and its liabilities are its source of funds, and its assets are its use of funds. This is explained by Klein ( 1971 ), who notes that a bank’s equity W , borrowed funds and its deposits B is equal to its total funds F . This is the same for incumbents and challengers. This can be depicted algebraically if we let incumbents be represented by Φ and challengers represented by Γ:

Klein ( 1971 ) further explains that a bank’s equity is therefore made up of its share capital and unimpaired reserves. The latter are held by a bank to protect the bank’s deposit clients. This part is also mandated by regulation, so as to protect customers and indeed the entire banking system from systemic failure. These protective measures include other prudential requirements to hold cash reserves or other liquid assets. As shall be shown, banking services can be performed over the Internet without these protections. Banking as a service, as this phenomenon known, is expected to increase in the future. This will change the nature of the protection available to clients. It will change the way banks transform assets, explained next.

A bank’s deposits are said to be a function of the proportion of total funds obtained through the issuance of the ith deposit type and its total funds F , represented by α i . Where deposits, represented by Bs , are made in the form of Bs (i  =  1 *s n) , they generate a rate of interest. It follows that Si Bs  =  B . As such,

Therefor it can be said that,

The importance of Eq. 3 is that the balance sheet can be leveraged by the issuance of loans. It should be noted, however, that not all loans are returned to the bank in whole or part. Non-performing loans reduce the asset side of a bank’s balance sheet and act as a constraint on capital, and therefore new lending. Clearly, this is not the case with banking as a service. In that model, loans are brokered. That said, with the traditional model, an advantage of financial technology is that it facilitates the data mining of clients’ accounts. Lending can therefore be more targeted to borrowers that are more likely to repay, thereby reducing non-performing loans. Pari passu, the incumbent bank of the future will therefore have a higher risk-adjusted return on capital. In practice, however, banking as a service will bring greater competition from challengers and possible further erosion of margins. Alternatively, some banks will proactively engage in partnerships and acquisitions to maintain their customer base and address the competition.

A bank must have reserves to meet the demand of customers demanding their deposits back. The amount of these reserves is a key function of banking regulation. The Basel Committee on Banking Supervision mandates a requirement to hold various tiers of capital, so that banks have sufficient reserves to protect depositors. The Committee also imposes a framework for mitigating excessive liquidity risk and maturity transformation, through a set Liquidity Coverage Ratio and Net Stable Funding Ratio.

Recent revisions of theory, because of financial technology advances, have altered our understanding of banking intermediation. This will impact the competitive landscape and therefor shape the nature of the bank of the future. In this respect, the threat to incumbent banks comes from peer-to-peer Internet lending platforms. These perform the brokerage function of financial intermediation without the use of the aforementioned banking balance sheet. Unlike regulated deposit takers, such lending platforms do not create assets and do not perform risk and asset transformation. That said, they are reliant on investors who do not always behave in a counter cyclical way.

Financial technology in banking is not new. It has been used to facilitate electronic markets since the 1980’s. Thakor ( 2020 ) refers to three waves of application of financial innovation in banking. The advent of institutional futures markets and the changing nature of financial contracts fundamentally changed the role of banks. In response to this, academics extended the concept of a bank into an entity that either fulfills the aforementioned functions of a broker or a qualitative asset transformer. In this respect, they connect the providers and users of capital without changing the nature of the transformation of the various claims to that capital. This transformation can be in the form risk transfer or the application of leverage. The nature of trading of financial assets, however, is changing. Price discovery can now be done over the Internet and that is moving liquidity from central marketplaces (like the stock exchange) to decentralized ones.

Alongside these trends, in considering what the bank of the future will look like, it is necessary to understand the unregulated lending market that competes with traditional banks. In this part of the lending market, there has been a rise in shadow banks. The literature on these entities is covered by Adrian and Ashcraft ( 2016 ). Shadow banks have taken substantial market share from the traditional banks. They fulfil the brokerage function of banks, but regulators have only partial oversight of their risk transformation or leverage. The rise of shadow banks has been facilitated by financial technology and the originate to distribute model documented by Bord and Santos ( 2012 ). They use alternative trading systems that function as electronic communication networks. These facilitate dark pools of liquidity whereby buyers and sellers of bonds and securities trade off-exchange. Since the credit crisis of 2008, total broker dealer assets have diverged from banking assets. This illustrates the changed lending environment.

In the disintermediated market, banking as a service providers must rely on their equity and what access to funding they can attract from their online network. Without this they are unable to drive lending growth. To explain this, let I represent the online network. Extending Klein ( 1971 ), further let Ψ represent banking as a service and their total funds by F . This state is depicted as,

Theoretically, it can be shown that,

Shadow banks, and those disintermediators who bypass the banking system, have an advantage in a world where technology is ubiquitous. This becomes more apparent when costs are considered. Buchak et al. ( 2018 ) point out that shadow banks finance their originations almost entirely through securitization and what they term the originate to distribute business model. Diversifying risk in this way is good for individual banks, as banking risks can be transferred away from traditional banking balance sheets to institutional balance sheets. That said, the rise of securitization has introduced systemic risk into the banking sector.

Thus, we can see that the nature of banking capital is changing and at the same time technology is replacing labor. Let A denote the number of transactions per account at a period in time, and C denote the total cost per account per time period of providing the services of the payment mechanism. Klein ( 1971 ) points out that, if capital and labor are assumed to be part of the traditional banking model, it can be observed that,

It can therefore be observed that the total service charge per account at a period in time, represented by S, has a linear and proportional relationship to bank account activity. This is another variable that financial technology can impact. According to Klein ( 1971 ) this can be summed up in the following way,

where d is the basic bank decision variable, the service charge per transaction. Once again, in an automated and digital environment, financial technology greatly reduces d for the challenger banks. Swankie and Broby ( 2019 ) examine the impact of Artificial Intelligence on the evaluation of banking risk and conclude that it improves such variables.

Meanwhile, the traditional banking model can be expressed as a product of the number of accounts, M , and the average size of an account, N . This suggests a banks implicit yield is it rate of interest on deposits adjusted by its operating loss in each time period. This yield is generated by payment and loan services. Let R 1 depict this. These can be expressed as a fraction of total demand deposits. This is depicted by Klein ( 1971 ), if one assumes activity per account is constant, as,

As a result, whether a bank is structured with traditional labor overheads or built digitally, is extremely relevant to its profitability. The capital and labor of tradition banks, depicted as Φ i , is greater than online networks, depicted as I i . As such, the later have an advantage. This can be shown as,

What Klein (1972) failed to highlight is that the banking inherently involves leverage. Diamond and Dybving (1983) show that leverage makes bank susceptible to run on their liquidity. The literature divides these between adverse shock events, as explained by Bernanke et al ( 1996 ) or moral hazard events as explained by Demirgu¨¸c-Kunt and Detragiache ( 2002 ). This leverage builds on the balance sheet mismatch of short-term assets with long term liabilities. As such, capital and liquidity are intrinsically linked to viability and solvency.

The way capital and liquidity are managed is through credit and default management. This is done at a bank level and a supervisory level. The Basel Committee on Banking Supervision applies capital and leverage ratios, and central banks manage interest rates and other counter-cyclical measures. The various iterations of the prudential regulation of banks have moved the microeconomic theory of banking from the modeling of risk to the modeling of imperfect information. As mentioned, shadow and disintermediated services do not fall under this form or prudential regulation.

The relationship between leverage and insolvency risk crucially depends on the degree of banks total funds F and their liability structure L . In this respect, the liability structure of traditional banks is also greater than online networks which do not have the same level of available funds, depicted as,

Diamond and Dybvig ( 1983 ) observe that this liability structure is intimately tied to a traditional bank’s assets. In this respect, a bank’s ability to finance its lending at low cost and its ability to achieve repayment are key to its avoidance of insolvency. Online networks and/or brokers do not have to finance their lending, simply source it. Similarly, as brokers they do not face capital loss in the event of a default. This disintermediates the bank through the use of a peer-to-peer environment. These lenders and borrowers are introduced in digital way over the internet. Regulators have taken notice and the digital broker advantage might not last forever. As a result, the future may well see greater cooperation between these competing parties. This also because banks have valuable operational experience compared to new entrants.

It should also be observed that bank lending is either secured or unsecured. Interest on an unsecured loan is typically higher than the interest on a secured loan. In this respect, incumbent banks have an advantage as their closeness to the customer allows them to better understand the security of the assets. Berger et al ( 2005 ) further differentiate lending into transaction lending, relationship lending and credit scoring.

The evolution of the business model in a digital world

As has been demonstrated, the bank of the future in its various manifestations will be a consequence of the evolution of the current banking business model. There has been considerable scholarly investigation into the uniqueness of this business model, but less so on its changing nature. Song and Thakor ( 2010 ) are helpful in this respect and suggest that there are three aspects to this evolution, namely competition, complementary and co-evolution. Although liquidity transformation is evolving, it remains central to a bank’s role.

All the dynamics mentioned are relevant to the economy. There is considerable evidence, as outlined by Levine ( 2001 ), that market liberalization has a causal impact on economic growth. The impact of technology on productivity should prove positive and enhance the functioning of the domestic financial system. Indeed, market liberalization has already reshaped banking by increasing competition. New fee based ancillary financial services have become widespread, as has the proprietorial use of balance sheets. Risk has been securitized and even packaged into trade-able products.

Challenger banks are developing in a complementary way with the incumbents. The latter have an advantage over new entrants because they have information on their customers. The liquidity insurance model, proposed by Diamond and Dybvig ( 1983 ), explains how such banks have informational advantages over exchange markets. That said, financial technology changes these dynamics. It if facilitating the processing of financial data by third parties, explained in greater detail in the section on Open Banking.

At the same time, financial technology is facilitating banking as a service. This is where financial services are delivered by a broker over the Internet without resort to the balance sheet. This includes roboadvisory asset management, peer to peer lending, and crowd funding. Its growth will be facilitated by Open Banking as it becomes more geographically adopted. Figure  3 illustrates how these business models are disintermediating the traditional banking role and matching burrowers and savers.

The traditional view of banks ecosystem between savers and borrowers, atop the Internet which is matching savers and borrowers directly in a peer-to-peer way. The Klein ( 1971 ) theory of the banking firm does not incorporate the mirrored dynamics, and as such needs to be extended to reflect the digital innovation that impacts both borrowers and severs in a peer-to-peer environment

Meanwhile, the banking sector is co-evolving alongside a shadow banking phenomenon. Lenders and borrowers are interacting, but outside of the banking sector. This is a concern for central banks and banking regulators, as the lending is taking place in an unregulated environment. Shadow banking has grown because of financial technology, market liberalization and excess liquidity in the asset management ecosystem. Pozsar and Singh ( 2011 ) detail the non-bank/bank intersection of shadow banking. They point out that shadow banking results in reverse maturity transformation. Incumbent banks have blurred the distinction between their use of traditional (M2) liabilities and market-based shadow banking (non-M2) liabilities. This impacts the inter-generational transfers that enable a bank to achieve interest rate smoothing.

Securitization has transformed the risk in the banking sector, transferring it to asset management institutions. These include structured investment vehicles, securities lenders, asset backed commercial paper investors, credit focused hedge and money market funds. This in turn has led to greater systemic risk, the result of the nature of the non-traded liabilities of securitized pooling arrangements. This increased risk manifested itself in the 2008 credit crisis.

Commercial pressures are also shaping the banking industry. The drive for cost efficiency has made incumbent banks address their personally costs. Bank branches have been closed as technology has evolved. Branches make it easier to withdraw or transfer deposits and challenger banks are not as easily able to attract new deposits. The banking sector is therefore looking for new point of customer contact, such as supermarkets, post offices and social media platforms. These structural issues are occurring at the same time as the retail high street is also evolving. Banks have had an aggressive roll out of automated telling machines and a reduction in branches and headcount. Online digital transactions have now become the norm in most developed countries.

The financing of banks is also evolving. Traditional banks have tended to fund illiquid assets with short term and unstable liquid liabilities. This is one of the key contributors to the rise to the credit crisis of 2008. The provision of liquidity as a last resort is central to the asset transformation process. In this respect, the banking sector experienced a shock in 2008 in what is termed the credit crisis. The aforementioned liquidity mismatch resulted in the system not being able to absorb all the risks associated with subprime lending. Central banks had to resort to quantitative easing as a result of the failure of overnight funding mechanisms. The image of the entire banking sector was tarnished, and the banks of the future will have to address this.

The future must learn from the mistakes of the past. The structural weakness of the banking business model cannot be solved. That said, the latest Basel rules introduce further risk mitigation, improved leverage ratios and increased levels of capital reserve. Another lesson of the credit crisis was that there should be greater emphasis on risk culture, governance, and oversight. The independence and performance of the board, the experience and the skill set of senior management are now a greater focus of regulators. Internal controls and data analysis are increasingly more robust and efficient, with a greater focus on a banks stable funding ratio.

Meanwhile, the very nature of money is changing. A digital wallet for crypto-currencies fulfills much the same storage and transmission functions of a bank; and crypto-currencies are increasing being used for payment. Meanwhile, in Sweden, stores have the right to refuse cash and the majority of transactions are card based. This move to credit and debit cards, and the solving of the double spending problem, whereby digital money can be crypto-graphically protected, has led to the possibility that paper money could be replaced at some point in the future. Whether this might be by replacement by a CBDC, or decentralized digital offering, is of secondary importance to the requirement of banks to adapt. Whether accommodating crytpo-currencies or CBDC’s, Kou et al. ( 2021 ) recommend that banks keep focused on alternative payment and money transferring technologies.

Central banks also have to adapt. To limit disintermediation, they have to ensure that the economic design of their sponsored digital currencies focus on access for banks, interest payment relative to bank policy rate, banking holding limits and convertibility with bank deposits. All these developments have implications for banks, particularly in respect of funding, the secure storage of deposits and how digital currency interacts with traditional fiat money.

Open banking

Against the backdrop of all these trends and changes, a new dynamic is shaping the future of the banking sector. This is termed Open Banking, already briefly mentioned. This new way of handling banking data protocols introduces a secure way to give financial service companies consensual access to a bank’s customer financial information. Figure  4 illustrates how this works. Although a fairly simple concept, the implications are important for the banking industry. Essentially, a bank customer gives a regulated API permission to securely access his/her banking website. That is then used by a banking as a service entity to make direct payments and/or download financial data in order to provide a solution. It heralds an era of customer centric banking.

How Open Banking operates. The customer generates data by using his bank account. A third party provider is authorized to access that data through an API request. The bank confirms digitally that the customer has authorized the exchange of data and then fulfills the request

Open Banking was a response to the documented inertia around individual’s willingness to change bank accounts. Following the Retail Banking Review in the UK, this was addressed by lawmakers through the European Union’s Payment Services Directive II. The legislation was designed to make it easier to change banks by allowing customers to delegate authority to transfer their financial data to other parties. As a result of this, a whole host of data centric applications were conceived. Open banking adds further momentum to reshaping the future of banking.

Open Banking has a number of quite revolutionary implications. It was started so customers could change banks easily, but it resulted in some secondary considerations which are going to change the future of banking itself. It gives a clear view of bank financing. It allows aggregation of finances in one place. It also allows can give access to attractive offerings by allowing price comparisons. Open Banking API’s build a secure online financial marketplace based on data. They also allow access to a larger market in a faster way but the third-party providers for the new entrants. Open Banking allows developers to build single solutions on an API addressing very specific problems, like for example, a cash flow based credit rating.

Romānova et al. ( 2018 ) undertook a questionnaire on the Payment Services Directive II. The results suggest that Open Banking will promote competitiveness, innovation, and new product development. The initiative is associated with low costs and customer satisfaction, but that some concerns about security, privacy and risk are present. These can be mitigated, to some extent, by secure protocols and layered permission access.

Discussion: strategic options

Faced with these disruptive trends, there are four strategic options for market participants to con- sider. There are (1) a defensive customer retention strategy for incumbents, (2) an aggressive customer acquisition strategy for challenger banks (3) a banking as a service strategy for new entrants, and (4) a payments strategy for social media platforms.

Each of these strategies has to be conducted in a competitive marketplace for money demand by potential customers. Figure  5 illustrates where the first three strategies lie on the tradeoff between money demand and interest rates. The payment strategy can’t be modeled based on the supply of money. In the figure, the market settles at a rate L 2 . The incumbent banks have the capacity to meet the largest supply of these loans. The challenger banks have a constrained function but due to a lower cost base can gain excess rent through higher rates of interest. The peer-to-peer bank as a service brokers must settle for the market rate and a constrained supply offering.

The money demand M by lenders on the y axis. Interest rates on the y axis are labeled as r I and r II . The challenger banks are represented by the line labeled Γ. They have a price and technology advantage and so can lend at higher interest rates. The brokers are represented by the line labeled Ω. They are price takers, accepting the interest rate determined by the market. The same is true for the incumbents, represented by the line labeled Φ but they have a greater market share due to their customer relationships. Note that payments strategy for social media platforms is not shown on this figure as it is not affected by interest rates

Figure  5 illustrates that having a niche strategy is not counterproductive. Liu et al ( 2020 ) found that banks performing niche activities exhibit higher profitability and have lower risk. The syndication market now means that a bank making a loan does not have to be the entity that services it. This means banks in the future can better shape their risk profile and manage their lending books accordingly.

An interesting question for central banks is what the future Deposit Supply function will look like. If all three forms: open banking, traditional banking and challenger banks develop together, will the bank of the future have the same Deposit Supply function? The Klein ( 1971 ) general formulation assumes that deposits are increasing functions of implicit and explicit yields. As such, the very nature of central bank directed monetary policy may have to be revisited, as alluded to in the earlier discussion on digital money.

The client retention strategy (incumbents)

The competitive pressures suggest that incumbent banks need to focus on customer retention. Reichheld and Kenny ( 1990 ) found that the best way to do this was to focus on the retention of branch deposit customers. Obviously, another way is to provide a unique digital experience that matches the challengers.

Incumbent banks have a competitive advantage based on the information they have about their customers. Allen ( 1990 ) argues that where risk aversion is observable, information markets are viable. In other words, both bank and customer benefit from this. The strategic issue for them, therefore, becomes the retention of these customers when faced with greater competition.

Open Banking changes the dynamics of the banking information advantage. Borgogno and Colangelo ( 2020 ) suggest that the access to account (XS2A) rule that it introduced will increase competition and reduce information asymmetry. XS2A requires banks to grant access to bank account data to authorized third payment service providers.

The incumbent banks have a high-cost base and legacy IT systems. This makes it harder for them to migrate to a digital world. There are, however, also benefits from financial technology for the incumbents. These include reduced cost and greater efficiency. Financial technology can also now support platforms that allow incumbent banks to sell NPL’s. These platforms do not require the ownership of assets, they act as consolidators. The use of technology to monitor the transactions make the processing cost efficient. The unique selling point of such platforms is their centralized point of contact which results in a reduction in information asymmetry.

Incumbent banks must adapt a number of areas they got to adapt in terms of their liquidity transformation. They have to adapt the way they handle data. They must get customers to trust them in a digital world and the way that they trust them in a bricks and mortar world. It is no coincidence. When you go into a bank branch that is a great big solid building great big facade and so forth that is done deliberately so that you trust that bank with your deposit.

The risk of having rising non-performing loans needs to be managed, so customer retention should be selective. One of the puzzles in banking is why customers are regularly denied credit, rather than simply being charged a higher price for it. This credit rationing is often alleviated by collateral, but finance theory suggests value is based on the discounted sum of future cash flows. As such, it is conceivable that the bank of the future will use financial technology to provide innovative credit allocation solutions. That said, the dual risks of moral hazard and information asymmetries from the adoption of such solutions must be addressed.

Customer retention is especially important as bank competition is intensifying, as is the digitalization of financial services. Customer retention requires innovation, and that innovation has been moving at a very fast rate. Until now, banks have traditionally been hesitant about technology. More recently, mergers and acquisitions have increased quite substantially, initiated by a need to address actual or perceived weaknesses in financial technology.

The client acquisition strategy (challengers)

As intermediaries, the challenger banks are the same as incumbent banks, but designed from the outset to be digital. This gives them a cost and efficiency advantage. Anagnostopoulos ( 2018 ) suggests that the difference between challenger and traditional banks is that the former address its customers problems more directly. The challenge for such banks is customer acquisition.

Open Banking is a major advantage to challenger banks as it facilitates the changing of accounts. There is widespread dissatisfaction with many incumbent banks. Open Banking makes it easier to change accounts and also easier to get a transaction history on the client.

Customer acquisition can be improved by building trust in a brand. Historically, a bank was physically built in a very robust manner, hence the heavy architecture and grand banking halls. This was done deliberately to engender a sense of confidence in the deposit taking institution. Pure internet banks are not able to do this. As such, they must employ different strategies to convey stability. To do this, some communicate their sustainability credentials, whilst others use generational values-based advertising. Customer acquisition in a banking context is traditionally done by offering more attractive rates of interest. This is illustrated in Fig.  5 by the intersect of traditional banks with the market rate of interest, depicted where the line Γ crosses L 2 . As a result of the relationship with banking yield, teaser rates and introductory rates are common. A customer acquisition strategy has risks, as consumers with good credit can game different challenger banks by frequently changing accounts.

Most customer acquisition, however, is done based on superior service offering. The functionality of challenger banking accounts is often superior to incumbents, largely because the latter are built on legacy databases that have inter-operability issues. Having an open platform of services is a popular customer acquisition technique. The unrestricted provision of third-party products is viewed more favorably than a restricted range of products.

The banking as a service strategy (new entrants)

Banking from a customer’s perspective is the provision of a service. Customers don’t care about the maturity transformation of banking balance sheets. Banking as a service can be performed without recourse to these balance sheets. Banking products are brokered, mostly by new entrants, to individuals as services that can be subscribed to or paid on a fee basis.

There are a number banking as a service solutions including pre-paid and credit cards, lending and leasing. The banking as a service brokers are effectively those that are aggregating services from others using open banking to enable banking as a service.

The rise of banking as a service needs to be understood as these compete directly with traditional banks. As explained, some of these do this through peer-to-peer lending over the internet, others by matching borrows and sellers, conducting mediation as a loan broker. Such entities do not transform assets and do not have banking licenses. They do not have a branch network and often don not have access to deposits. This means that they have no insurance protection and can be subject to interest rate controls.

The new genre of financial technology, banking as a service provider, conduct financial services transformation without access to central bank liquidity. In a distributed digital asset world, the assets are stored on a distributed ledger rather than a traditional banking ledger. Financial technology has automated credit evaluation, savings, investments, insurance, trading, banking payments and risk management. These banking as a service offering are only as secure as the technology on which they are built.

The social media payment strategy (disintermediators and disruptors)

An intermediation bank is a conceptual idea, one created solely on a social networking site. Social media has developed a market for online goods and services. Williams ( 2018 ) estimates that there are 2.46 billion social media users. These all make and receive payments of some kind. They demand security and functionality. Importantly, they have often more clients than most banks. As such, a strategy to monetize the payments infrastructure makes sense.

All social media platforms are rich repositories of data. Such platforms are used to buy and sell things and that requires payments. Some platforms are considering evolving their own digital payment, cutting out the banks as middlemen. These include Facebook’s Diem (formerly Libra), a digital currency, and similar developments at some of the biggest technology companies. The risk with social media payment platform is that there is systemic counter-party protection. Regulators need to address this. One way to do this would be to extend payment service insurance to such platforms.

Social media as a platform moves the payment relationship from a transaction to a customer experience. The ability to use consumer desires in combination with financial data has the potential to deliver a number of new revenue opportunities. These will compete directly with the banks of the future. This will have implications for (1) the money supply, (2) the market share of traditional banks and, (3) the services that payment providers offer.

Further research

Several recommendations for research derive from both the impact of disintermediation and the four proposed strategies that will shape banking in the future. The recommendations and suggestions are based on the mentioned papers and the conclusions drawn from them.

As discussed, the nature of intermediation is changing, and this has implications for the pricing of risk. The role of interest rates in banking will have to be further reviewed. In a decentralized world based on crypto currencies the central banks do not have the same control over the money supply, This suggest the quantity theory of money and the liquidity preference theory need to be revisited. As explained, the Internet reduces much of the friction costs of intermediation. Researchers should ask how this will impact maturity transformation. It is also fair to ask whether at some point in the future there will just be one big bank. This question has already been addressed in the literature but the Internet facilities the possibility. Diamond ( 1984 ) and Ramakrishnan and Thakor ( 1984 ) suggested the answer was due to diversification and its impact on reducing monitoring costs.

Attention should be given by academics to the changing nature of banking risk. How should regulators, for example, address the moral hazard posed by challenger banks with weak balance sheets? What about deposit insurance? Should it be priced to include unregulated entities? Also, what criteria do borrowers use to choose non-banking intermediaries? The changing risk environment also poses two interesting practical questions. What will an online bank run look like, and how can it be averted? How can you establish trust in digital services?

There are also research questions related to the nature of competition. What, for example, will be the nature of cross border competition in a decentralized world? Is the credit rationing that generates competition a static or dynamic phenomena online? What is the value of combining consumer utility with banking services?

Financial intermediaries, like banks, thrive in a world of deficits and surpluses supported by information asymmetries and disconnectedness. The connectivity of the internet changes this dynamic. In this respect, the view of Schumpeter ( 1911 ) on the role of financial intermediaries needs revisiting. Lenders and borrows can be connected peer to peer via the internet.

All the dynamics mentioned change the nature of moral hazard. This needs further investigation. There has been much scholarly research on the intrinsic riskiness of the mismatch between banking assets and liabilities. This mismatch not only results in potential insolvency for a single bank but potentially for the whole system. There has, for example, been much debate on the whether a bank can be too big to fail. As a result of the riskiness of the banking model, the banks of the future will be just a liable to fail as the banks of the past.

This paper presented a revision of the theory of banking in a digital world. In this respect, it built on the work of Klein ( 1971 ). It provided an overview of the changing nature of banking intermediation, a result of the Internet and new digital business models. It presented the traditional academic view of banking and how it is evolving. It showed how this is adapted to explain digital driven disintermediation.

It was shown that the banking industry is facing several documented challenges. Risk is being taken of balance sheet, securitized, and brokered. Financial technology is digitalizing service delivery. At the same time, the very nature of intermediation is being changed due to digital currency. It is argued that the bank of the future not only has to face these competitive issues, but that technology will enhance the delivery of banking services and reduce the cost of their delivery.

The paper further presented the importance of the Open Banking revolution and how that facilitates banking as a service. Open Banking is increasing client churn and driving banking as a service. That in turn is changing the way products are delivered.

Four strategies were proposed to navigate the evolving competitive landscape. These are for incumbents to address customer retention; for challengers to peruse a low-cost digital experience; for niche players to provide banking as a service; and for social media platforms to develop payment platforms. In all these scenarios, the banks of the future will have to have digital strategies for both payments and service delivery.

It was shown that both incumbents and challengers are dependent on capital availability and borrowers credit concerns. Nothing has changed in that respect. The risks remain credit and default risk. What is clear, however, is the bank has become intrinsically linked with technology. The Internet is changing the nature of mediation. It is allowing peer to peer matching of borrowers and savers. It is facilitating new payment protocols and digital currencies. Banks need to evolve and adapt to accommodate these. Most of these questions are empirical in nature. The aim of this paper, however, was to demonstrate that an understanding of the banking model is a prerequisite to understanding how to address these and how to develop hypotheses connected with them.

In conclusion, financial technology is changing the future of banking and the way banks intermediate. It is facilitating digital money and the online transmission of financial assets. It is making banks more customer enteric and more competitive. Scholarly investigation into banking has to adapt. That said, whatever the future, trust will remain at the core of banking. Similarly, deposits and lending will continue to attract regulatory oversight.

Availability of data and materials

Diagrams are my own and the code to reproduce them is available in the supplied Latex files.

Adrian T, Ashcraft AB (2016) Shadow banking: a review of the literature. In: Banking crises. Palgrave Macmillan, London, pp 282–315

Allen F (1990) The market for information and the origin of financial intermediation. J Financ Intermed 1(1):3–30

Article   Google Scholar  

Anagnostopoulos I (2018) Fintech and regtech: impact on regulators and banks. J Econ Bus 100:7–25

Berger AN, Herring RJ, Szegö GP (1995) The role of capital in financial institutions. J Bank Finance 19(3–4):393–430

Berger AN, Miller NH, Petersen MA, Rajan RG, Stein JC (2005) Does function follow organizational form? Evidence from the lending practices of large and small banks. J Financ Econ 76(2):237–269

Bernanke B, Gertler M, Gilchrist S (1996) The financial accelerator and the flight to quality. The review of economics and statistics, pp1–15

Bord V, Santos JC (2012) The rise of the originate-to-distribute model and the role of banks in financial intermediation. Federal Reserve Bank N Y Econ Policy Rev 18(2):21–34

Google Scholar  

Borgogno O, Colangelo G (2020) Data, innovation and competition in finance: the case of the access to account rule. Eur Bus Law Rev 31(4)

Braggion F, Manconi A, Zhu H (2018) Is Fintech a threat to financial stability? Evidence from peer-to-Peer lending in China, November 10

Brei M, Borio C, Gambacorta L (2020) Bank intermediation activity in a low-interest-rate environment. Econ Notes 49(2):12164

Buchak G, Matvos G, Piskorski T, Seru A (2018) Fintech, regulatory arbitrage, and the rise of shadow banks. J Financ Econ 130(3):453–483

Demirgüç-Kunt A, Detragiache E (2002) Does deposit insurance increase banking system stability? An empirical investigation. J Monet Econ 49(7):1373–1406

Diamond DW (1984) Financial intermediation and delegated monitoring. Rev Econ Stud 51(3):393–414

Diamond DW, Dybvig PH (1983) Bank runs, deposit insurance, and liquidity. J Polit Econ 91(3):401–419

Diamond DW, Rajan RG (2000) A theory of bank capital. J Finance 55(6):2431–2465

Edgeworth FY (1888) The mathematical theory of banking. J Roy Stat Soc 51(1):113–127

Fama EF (1980) Banking in the theory of finance. J Monet Econ 6(1):39–57

Gurley JG, Shaw ES (1956) Financial intermediaries and the saving-investment process. J Finance 11(2):257–276

Klein MA (1971) A theory of the banking firm. J Money Credit Bank 3(2):205–218

Kou G, Akdeniz ÖO, Dinçer H, Yüksel S (2021) Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach. Financ Innov 7(1):1–28

Levine R (2001) International financial liberalization and economic growth. Rev Interna Tional Econ 9(4):688–702

Liu FH, Norden L, Spargoli F (2020) Does uniqueness in banking matter? J Bank Finance 120:105941

Pozsar Z, Singh M (2011) The nonbank-bank nexus and the shadow banking system. IMF working papers, pp 1–18

Ramakrishnan RT, Thakor AV (1984) Information reliability and a theory of financial intermediation. Rev Econ Stud 51(3):415–432

Reichheld FF, Kenny DW (1990) The hidden advantages of customer retention. J Retail Bank 12(4):19–24

Romānova I, Grima S, Spiteri J, Kudinska M (2018) The payment services directive 2 and competitiveness: the perspective of European Fintech companies. Eur Res Stud J 21(2):5–24

Modigliani F, Miller MH (1959) The cost of capital, corporation finance, and the theory of investment: reply. Am Econ Rev 49(4):655–669

Schumpeter J (1911) The theory of economic development. Harvard Econ Stud XLVI

Song F, Thakor AV (2010) Financial system architecture and the co-evolution of banks and capital markets. Econ J 120(547):1021–1055

Swankie GDB, Broby D (2019) Examining the impact of artificial intelligence on the evaluation of banking risk. Centre for Financial Regulation and Innovation, white paper

Thakor AV (2020) Fintech and banking: What do we know? J Financ Intermed 41:100833

Vishnu S, Agochiya V, Palkar R (2017) Data-centered dependencies and opportunities for robotics process automation in banking. J Financ Transf 45(1):68–76

Williams MD (2018) Social commerce and the mobile platform: payment and security perceptions of potential users. Comput Hum Behav 115:105557

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Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend

• Lambert Kofi Osei   ORCID: orcid.org/0000-0001-7461-4839 1 ,
• Yuliya Cherkasova 2 &
• Kofi Mintah Oware 1  

Future Business Journal volume  9 , Article number:  30 ( 2023 ) Cite this article

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Metrics details

Every aspect of life has been affected by digitization, and the use of digital technologies to deliver banking services has increased significantly. The purpose of this study was to give a thorough review and pinpoint the intellectual framework of the field of research of the digital banking transformation (DBT).

Methodology

This study employed bibliometric and network analysis to map a network in a single study, and a total of 268 publications published between 1989 and 2022 were used.

Our findings demonstrate that the UK, USA, Germany, and China are the countries that have conducted most of the studies on the digital banking transformation. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. Additional research reveals that papers rated with A* and A grades frequently publish studies on digital banking transformation. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. The current research trend points toward FinTech, block chain, mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models. The importance of emphasizing the need for additional research in these fields of study cannot be stressed, given the expanding popularity of blockchain technology and digital currency in the literature.

Originality

It appears that this is the first study that examines the theoretical studies of digital banking transformation using bibliometric analysis. The second element of originality is about the multiple dimensions of the impact of technology in the banking sector, which includes customer, company, bank, regulation authority and society.

Introduction

The advent of information communication technology (ICT) is believed to have caused a paradigm shift in all aspects of human life. Technology has therefore become a necessary, unavoidable demand for society and the business environment, from work automation to service digitalization, from cloud computing to data analytics, from virtual collaboration to smart homes. Almost every industry is undergoing constant transformation because to technology. In the past 20 years, digitalization has had an impact on a variety of sectors, presenting fresh business prospects and encouraging new systems of innovation [ 1 ].

The finance sector is actively experimenting and inventing with the power of technology's digitization. It is also one of the industries that have successfully embraced digitization. One of the most laudable digital developments of the finance sector is the widespread adoption of digital banking over traditional banking methods. Recently, potentially disruptive technological breakthroughs and Internet-based solutions appear to have been introduced to the banking industry, one of the most established and conservative sectors of the economy. Digital transformation in banking is essential to enhance how banks and other financial organizations learn about, communicate with and satisfy the needs of customers. An effective digital transformation starts with understanding digital client behavior, preferences, choices, likes, dislikes, and stated and unstated expectations, to be more precise. Many academics are interested in how information and communications technology is advancing and how it can affect the banking industry [ 2 ]. However, the bibliometric analysis conducted by academics utilizing VOS viewer is assumed to be the first to look at the digital banking transformation (DBT) studies from a performance analysis and science mapping perspective.

Large data sets from databases like Web of Science, Scopus index or Dimension are permitted for bibliometric study. The bibliometric analysis moves the banks' digital transformation survey from single to multi-dimensional outcomes. A quick search of DBT studies shows that the first journal was published in 1989, despite the earliest forms of digital banking being traced back to the advent of ATMs and cards in the 1960s. The quantum of increase after 2014, amounting to 203 articles, representing 76% of all published articles on the topic, compels this study to focus on this field of DBT studies. We contend that establishing the area's intellectual framework is more crucial than ever. As a result, we make a contribution by offering a relevant, distinctive and significant intellectual map of the literature on digital banking studies through quantitative and bibliometric analysis. In mapping the intellectual structure of DBT, our study sets out to address the following critical research questions:

Who are the predominant contributors (publication by year, journals, publishers, authors, publication, journal quality, country, and universities) to the DBT theory?

What are the country's collaboration and citation analysis of the impact of digitalization on banks?

What is digital banking theory's intellectual foundation (co-citation)?

What are emerging research themes/trends and future direction (bibliography coupling

and keywords analysis) to digital banking theory?

In response to the above four questions, this study has at least four significant additions to the literature on digital banking. First, we extend and build upon prior assessments of digital banking by offering a factual, quantitative perspective on the theory's historical development across time. Of course, this study considers notable contributors, the intellectual framework and theoretical groundwork of the discipline, the degree to which individuals are connected, and thematic subdomains. We show how digital banking has advanced by evaluating the significant offshoots from the original work by [ 3 ]. Second, we objectively assess how faithfully emerging subtopic literature streams acknowledge and build upon Burk and Pfitzmann’s seminal works. As a result, our paper is uniquely suited to detect significant gaps that might exist in subtopic areas, and we offer suggestions for improving literature unification. Thirdly, we show how scholars of digital banking have historically changed their study goals over time in response to gaps between theory and practice in order to determine how faithfully they have addressed these gaps. Finally, we contribute to the digital banking literature by identifying emerging digital banking research and study trends. Overall, we think that our research exposes chances to grow more effectively and collaboratively in the future by highlighting well-traveled roads that previous researchers have taken, identifying potential cracks that may leave the literature in a state of disarray, and so forth [ 4 ].

This study used bibliometric and network analysis to map a network that comprises authors, co-authors, keyword occurrences, journal citations and author names in a single study. The approach can give a thorough overview and pinpoint the field's intellectual hierarchy [ 5 ]. Furthermore, according to [ 6 ], bibliometric approaches are suitable for mapping the academic structure of a certain area because doing so enables researchers to recognize "'what,' 'where' and 'by whom' founded the field. We carry out a thorough bibliometric evaluation to meet the research objectives by carefully extracting the sample literature using the proper inclusion and exclusion criteria and selecting the search string. The first stage involved a descriptive analysis, while the second stage involved a comprehensive bibliometric analysis. Utilizing VOSviewer and Rstudio assistance, citation and co-citation analyses were carried out to determine the intellectual structure of the study on digital banking studies. Weighted citation measures were used to identify the lead publications from the clusters.

The format of our paper is as follows: A brief theoretical overview of the DBT literature, including its core principles, significant developments and limits, is given in section " Theoretical background ." Section " Methods " describes the research approach in depth, and section " Results " shows the results of our investigation. The limitations of our study and their consequences for theory and practice are discussed in section " Discussions and future research agenda ." Finally, we provide our final observations in section " Conclusion ."

Theoretical background

Society, economics, banks and banking are changing as a result of technological advancement. Banks are an unneeded remnant whose purpose is best provided by alternate arrangements, even though we still need banking. The value chain of traditional banking has been disintermediated by technology, and its business model has been severely altered. As a result, Fin-Tech adoption and digital technology collaboration are widespread, constant and profoundly changing company structures [ 7 ]. Nearly 90% of banks fear losing business to Fin-Tech, which has replaced traditional value chains with shorter multi-modal and multi-directional nodes, according to KPMG's 2017 annual reports. Digitalization permeates the contemporary world, and the banking industry is no different. Our lives seemed to have grown so ingrained with digital technology that we would feel empty without it. Banks of all sizes are investing a lot in digital initiatives to maintain their uniqueness and meet as many of their customers' needs as possible. Digitalization leads to more customization and closer to customers. It is called digital banking when a bank renders its services online, and customers can make transactions and other activities online. Since over 73% of consumers use products from numerous platforms, Lee and Shin [ 8 ] highlight that bank model disruption and ascribe this to ongoing innovation followed by disruptive challenges, with the possibility of losing market share to Fin-Techs omnipresent.Mobile technologies and social media digitize bank value chains simultaneously addressing and influencing client demands and expectations.

However, according to our knowledge, not much research has been done on the banking sector. Nevertheless, it is well known that the banking sector, which is frequently IT-intensive, requires special consideration due to its significance for the whole economy. Berger [ 2 ] emphasizes that the benefits of technology adoption may not convert into improved production, which is consistent with the literature mentioned above. According to Berger, rather than the organization itself, the advantages of technology might be passed on to consumers and other production-related elements. Sharing data allow banks to process information more efficiently while also achieving huge economies of scale in the processing of payments. For instance, banks have reportedly employed information processing to handle deposit and loan client information as well as to more accurately assess risks, according to Berger and Mester. Additionally, they have employed telecommunications technologies to expeditiously process payments and disseminate this data while consuming fewer resources (2003, p. 58). This would imply that cost productivity increased in the 1990s.

Digital transformation has an impact on business processes and alters how banks conduct operations. A contributing aspect to the traditional relationship between customers and banks is digital transformation. Customers in particular have the right to use a variety of communication channels to engage in active and convenient engagement with banks and other customers via online customer support services. Most importantly, digital transformation enables banks to service a variety of consumers simultaneously, enhancing the bank's operational efficiency. In addition, the employee's job procedures are digitalized, reducing time and resources for both human resources and transaction execution. Thus, the bank will benefit from digital transformation by increasing output (raising the number of clients) and decreasing input expenses (reducing the number of employees and the time to make transactions).

The banking and FinTech industries will expand further in joint ventures, mergers and acquisitions toward convergence among banks, FinTech and technology organizations, and social media network providers as the new decade gets underway [ 9 ]. Digital technologies including blockchain, artificial intelligence (AI), data platforms, cybersecurity regulation technology and strategic collaborations will be well positioned to be retained in the banking business in a completely digitally changed financial environment [ 10 ]. Up until the advent of digital banking and the branch-based banking model in the early 1990s, traditional banking remained unaltered and unopposed. In the USA, Stanford Federal Credit Union opened the first online bank in 1994. The number of local bank branches has substantially decreased globally with the advent of online banking. Globally, the number of digital banks has been steadily rising at the same time. The first digital disruptor was ING Direct, which launched as an entirely online bank in 1996 and over the course of a little more than a decade attracted more than 20 million customers in nine countries without having to make any investments in physical infrastructure. In 2013, the FinTech bank "N26" received initial approval for a banking license. Amazon introduced an e-commerce-based checking account feature in 2021, while Facebook developed a social network-based banking service in 2020. By 2020, banking clients have been accustomed to using mobile banking apps, direct deposit to P2P payments and cloud-based banking platforms with AI.

To address our research issues in the present study, we employed two bibliometric analytic techniques. Since bibliometric analysis is quantitative, systematic, transparent and repeatable, it is strongly recommended for mapping the intellectual architecture of a literature stream [ 11 ]. The specifics of our research methodology and key conclusions are shown in Fig.  1 .

Flow chart of searching strategy and data collection process

To achieve its goals, this study suggests using publications and citations to analyze the performance of authors, institutions, countries and journals. Another unique approach used in this study is known as scientific mapping. Co-authorship analysis, clustering, citation analysis and keywords analysis are the approach factors [ 5 ]. Bibliometric approaches have been applied in recent investigations [ 12 , 13 ]. Then, we employ it to start the process of developing a bibliometric investigation [ 5 ]. The following actions are a part of the four-step process: data gathering and analysis, selecting the limiting criteria, data analysis, discussions and conclusions.

Defining the search terms

We started by conducting a methodical keyword search of the current literature on digital banking [ 14 ]. We extracted data from the Scopus index database. According to [ 15 ], Scopus has a larger journal than any other service that conducts data mining. As a result, this study made use of this database to mine data for its bibliometric analysis. To identify digital banking impact articles, we used the keyword methodology outlined by scholars who have recently conducted reviews of DBT. By concentrating primarily on work that has undergone thorough peer review, we aimed to maintain the academic integrity of our sample. Conference transcripts and book chapters were taken out of the analysis. Additionally, we excluded any non-English-language publications; 298 articles make up our final sample, which is deemed adequate for bibliometric study. These articles were published between 1989 and 2022. The keys words are: digital, bank, banking, business model, company, finance, economics and social sciences.

Keyword protocol applied in Scopus for extracting articles.

Data search and collection

As a result of several authors using the Scopus database for bibliometric analysis, it was chosen as the database from which the study's data were extracted [ 12 , 13 ]. In comparison with Web of Science and Dimension, the Scopus database has many indexed journals. The first stage of data extraction involved 295 publications with the titles "effect of digitalization on banks" and "digital transformation of banks" in June 2022. The following stage of the data processing was restricted to 268 English-language journals. The research is restricted to publications in the fields of banking, business management, accounting, economics, econometrics and finance. The last research search turned up 268 papers that were written between 1985 and 2022. Our literature review and bibliometric analysis are built on the foundation of the sample size of 268 articles. The method of data extraction is displayed in Table 1 .

This study raises different research questions covering contributors to DBT or impacts of digitalization on banks and banking, average journals and journal quality citation, digital banking intellectual foundations (co-citation), emerging research themes/trends and future direction (bibliography coupling and keywords analysis) in institutional theory.

Who are the predominant contributors to digital banking theory

This study responds to the first research question by addressing the dominant contributors to the DBT theory by using the following criteria: publication by year, journals, publishers, authors, publication, journal quality, country, and universities.

Publication by year

Figure  2 illustrates the number of DBT publications between 1989 and early 2022, recording 268 scientific publications. DBT received little attention from the scientific community in the early years from 1989 to 2005, recording as little as seven publications. The available data further show that publication increased slightly to sixty-seven (67) over a twenty (20) year period from 2006 to 2016. However, there was a dramatic change in this trend afterwards. Approximately 72 percent of these scientific publications, representing one hundred ninety-four (194) articles, occurred in the last six years. The figure further revealed that the years 2020 and 2021 alone accounted for 43 percent of all scientific publications in the field of DBT. Perhaps the havoc of Covid–19 and the strategic role of banks in successfully influencing the payment system architecture in particular resonated well with researchers to pay much attention to the field around this later period. While the quantity of publications has increased, publications within elite journals continue to grow. As recently as 2017, more over 40% of DBT research was published in prestigious publications. In fact, since 2017, the average annual proportion of publications in the top tier to all publications is 62 percent. As a result, our findings imply that the standard of published research has generally kept up with the volume of publications.

Trends in digital banking publication since 1989

Publication activity by country

Our findings also show that DBT research has a truly global reach, as shown by the participation of authors from 65 different countries. Figure  3 gives a graphic representation of the top countries publishing DBT research. For better clarity, the study limited Fig.  3 to cover countries with more than five publications. Although the publication of digital banking is international, it is interesting to notice that a significant portion of the work originates from a limited group of wealthy nations. More specifically, more than 46% of all published DBT studies come from the USA, UK, India, China, Germany, Netherlands, Hon Kong, Romania, Finland, Poland, Ukraine, Italy and Spain. Only China and India are from emerging economies. Figure  3 illustrates publication activities by country.

Top publishing countries on DBT

Publishing activity by journal

Two hundred thirteen different journals published the 268 articles in our sample. Table 1 lists the top publishing Journals. Based on publication count, we found that the leading journals for DBT include Financial Innovation, Journal of Cleaner Production, Journal of Economics and Business, International Journal of Information Management, Journal of Information Technology and Sustainability Journal. Our observation revealed that even though the Journal of Financial Innovation had only two publications, it claimed the top spot with two hundred and twelve citations total citation, given an average citation of one hundred and six. This study also used Australian Business School Council (ABDC) rating & ranking. Journal quality is rated and ranked by ABDC, with A* being the highest-quality journal, followed by A and B as the second- and third-best journals, respectively. According to the ABDC ranking, journal C is the lowest ranked. The data available to us have shown that the high-quality journals in class A and A* are publishing works on digital transformation. Three of the top five journals in our data are in the A class.

Publishing activity by author and organization

According to [ 16 ], bibliometric methodologies can be used to evaluate the intellectual influence of universities and their research personnel. To determine the sources of digital transformation in banking, we assessed the research output of individual academics and institutions. We found 598 distinct writers from 224 organizations publishing on the subject of banking digital transformation inside our dataset. The top publishing scholars and institutions are listed in Tables 2 and 3 . The descriptive statistics also show that [ 17 , 18 , 19 , 20 ] are the authors with the highest citation. In addition, the Financial University under the government of the Russian Federation, Comsats University—Islamabad, National Chiao Tung University—China and the State University of Management—Russia are the top four.

Country collaboration and citation analysis

Country collaborations of co-authors analysis.

The UK is the most productive nation in terms of publishing changes in digital banking. Australia, Canada, Indonesia and the Russian Federation have the lowest populations. Figure  4 demonstrates that, with seven linkages and 18 times as many co-authorships, the UK has the highest level of collaboration. Countries like China, Hong Kong and the Netherlands, each with six links, tie for second place. The inflow of overseas students completing second and third degrees in the UK and the US may be one reason there are more significant connections between the two countries [ 21 ]. Additionally, the UK and China are two other significant technology superpowers laying the groundwork for digitization. This might have inspired and drawn academics to carry out studies in the area.

Country collaboration of co-authors analysis

Citation analysis

The most read articles in the field of research on DBT were found through citation analysis. Citation analysis examines the connections between publications and finds the most significant publications in a given study area [ 5 ]. Similar studies that used citation analysis based on the Scopus database have also been looked at research [ 21 ]. The authors' and the study's primary focus are analyzed based on their citations in Table 4 . The Financial Innovation Journal and Journal of Cleaner Production publish the most-cited article. Liu et al. [ 22 ] and Yip et al. are the authors of these articles [ 23 ]. Even though publications on the evolution of digital banking began in 1989, the most highly cited papers are in 2016 and 2018, respectively.

Cluster analysis (results of reference co-citation analysis with reference map)

By conducting the co-citation analysis of references as previously described and grouping the references cited by papers on DBT into clusters, we next looked at the intellectual foundation and structure of the DBT to answer the third research question. The 268 papers in our sample used 8720 different references in total. Our examination of co-citations revealed five interconnected clusters with a total of 67 articles. At least 20 of the 268 papers in our sample, which contained all 67 of these reference articles, collectively cited them. In other words, these 67 publications are the quantitatively most significant references in the literature on the shift of banking into the digital age. Similarly, we used the weighted citation count provided by VOS viewer to ensure high-quality articles in cluster analysis. We looked at the top 5 articles in each cluster as presented in Table 5 , to find a common topic, and we labeled each theme accordingly, following [ 24 ]. We summarize the findings of the five most influential studies in each cluster. In the following sections, we give a quick overview of these reference clusters and how they integrate into the larger framework for digital banking (Fig. 5 ).

Co-citation network of the reference map

Cluster 1: Digital banking innovation

A cluster that established its boundaries improved its theoretical relevance and defined it as the first and most noticeable cluster to arise. Therefore, it makes sense that [ 25 ] are the most important tenet of this fundamental research stream. In 2022, digital transformation will continue to be a crucial trend in banking. The financial services sector is slowly changing as a result of technology, just like how it has affected other economic sectors. Physical bank branches have historically served as the primary point of contact for facilitating customer and retail banking transactions, according to [ 25 ]. Customers are continuing to transition from in-person to digital transactions as technology advances because of a complementary influence brought about by more access to digital banking services and an improved experience of new digital access, goods, services and functionality. They have developed a novel mapping technique for FinTech developments that assesses the extent of changes and transformations in four subfields of financial services: operations management, technological advancements, multiple innovations, and blockchain and other FinTech innovations. According to [ 26 ], the current wave of mergers and acquisitions in the financial services sector, combined with the broad availability of sophisticated technology, has increased competitiveness in the sector. Also, Henseler et al. [ 27 ] used discriminant validity assessment analysis to establish relationships between latent variables in business transformation. The digital banking revolution cannot go without challenges. All innovations encounter client resistance, claims [ 28 ] tested hypotheses using binary logit models comparing mobile banking adopters versus non-adopters, mobile banking postponers versus rejecters and Internet banking postponers versus rejecters using data from two comprehensive national surveys conducted in Finland ( n  = 1736 consumers). The value barrier is the main obstacle to the adoption of online and mobile banking, according to the study's findings. He also discovered that age and gender strongly influence decisions to adopt or reject. When [ 29 ] looked at the effect of cognitive age in explaining older people's resistance to mobile banking, they discovered that traditional and image barriers had an impact on usage, value and risk. All impediments, in turn, have an impact on resistance behavior. Furthermore, cognitive age was found to moderate these relationships. In order words, younger elders have limited or no resistance to DBT as opposed to elderly ones. All writers in this cluster agree that technology and evolving customer demands dramatically affect how banks operate in the twenty-first century. Indeed, the coronavirus outbreak has made it clear that banking institutions need to speed up their digital transitions. But the banking sector needs to modify its business models for front-facing and back-office operations to keep up with the changes and avoid potential upheavals. True digital banking and a complete transformation are built on implementing the most recent technology, such as blockchain cloud computing and Internet of Things (IoT).

Cluster 2: FinTech and RegTech in Banking

Scholars in this cluster preoccupied themselves with the concept of FinTech (Financial Technology) and RegTech (Regulatory Technology) thus the application of emerging technology to improve the way businesses manage regulatory compliance). They provided a range of viewpoints to make the disruptive potential of FinTech and its consequences for a more thorough financial ecosystem application in the banking and financial ecosystem easier to understand. Despite the widespread agreement that FinTech will have a big impact on the financial services industry, little academic literature has examined this topic, according to [ 30 ], citing [ 8 ]. Kindly assist with the changes.. Additionally, no accepted definition of FinTech has yet been established. On the other hand, according to Google, the query what is FinTech is presently ranked seventh among the most popular FinTech-related questions (Google, 2016b). He gave the most up-to-date definition of FinTech, which is a new financial business that uses technology to enhance financial activity. Contrarily, RegTech, or regulatory technology, uses cutting-edge tools and methods to assist financial institutions in enhancing their regulatory governance, reporting, compliance and risk management. According to [ 31 ] research, many desirable results might certainly be attained if regulators were willing to implement cultural change and integrate technical improvements with regulation. Such outcomes can include stabilizing the financial system, fostering systemic stability. The disruptive invention by [ 31 ] has the potential to improve consumer welfare, regulatory and supervisory outcomes, and the financial services industry's reputation. According to [ 10 ], the traditional business models of retail banks are seriously threatened by the emergence of digital innovators in the financial services industry. Lee and Shin [ 8 ] who contend that FinTech ushers in a new paradigm in which information technology drives innovation in the financial industry endorse this point of view. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. The corporate world is quickly digitizing, shattering borders between industries, providing new opportunities and eliminating long-successful business models, according to [ 22 ], who added to the literature. They added that, on the plus side, growing digitalization presents opportunities, including the chance to take advantage of a solid customer connection and boost cross-selling. The dangers are typically precise and immediate, which is a drawback.

Cluster 3: The new digital business model of banks and other financial service providers

The papers in this cluster delved into the business model concept and, to a more significant extent, the new banking business model, which is technology-led. According to [ 32 ], business strategists and academics are paying more attention to business models as they try to understand how businesses create value and function well in order to gain a competitive advantage. Additionally, they argued that the digital economy had given businesses the chance to test out novel systems for networked value creation, where value is collaboratively produced by a firm and a big number of partners for a large number of users. The researchers came to the conclusion that four key themes are emerging, largely centered on the idea of the business model: as a new analytical unit, providing a systemic perspective on how to "do business," encompassing boundary-spanning activities (performed by a focal firm or others), and focusing on both value creation and value capture. These ideas are related and reinforce one another. Chesbrough [ 33 ] says that businesses must use their business models to commercialize novel concepts and technology. While businesses may make significant investments and have elaborate systems for investigating novel concepts and technologies, they frequently lack the ability to develop the business models that would be used to implement these inputs. He proposed that organizations should build the capacity to innovate their business models in order to make sound business decisions. Durkin et al. [ 34 ] did an excellent job investigating social media's role in a bank’s new digitally oriented business model. They suggested that social media had the power to profoundly alter customer-bank relationships and improve how the two sides communicate in the future. Their research shows that a wide range of clients regularly use transactional e-banking services. Loebbecke and Picot [ 35 ] presented a position paper that considers the factors driving how digitization and big data analytics drive the change of business and society. There is also discussion of the potential effects of digitalization and big data analytics on banking or employment, particularly in terms of cognitive work. Although several authors have recently proposed definitions of "business model," Shafer et al. [ 36 ] claim that none of them seem to be broadly recognized. This lack of agreement could be ascribed to the concept's interest from a variety of fields, all of which have connected it to something. To develop business models in the age of digital transformation, there must be an exponential shift in corporate culture and leadership concentration. The authors concur that banking is evolving as a result of a new wave of digital-only firms who are fragmenting the industry, componentizing products, and upending established business models. They claimed that switching from the previous business model to the new one is not the only way to succeed in this adaptable, fluid world. Instead, it will shift away from relying on a single, vertically integrated business model and toward a variety of non-linear models and value chain roles. In actuality, the Covid-19 epidemic has accelerated the development of business ecosystems for digital banking. Opportunities to develop, deliver and realize the value in new ways are made possible by digital technologies. The pipeline concept, the foundation of the classic universal bank, allows it to independently manufacture, sell and distribute products using its internal resources. This vertically integrated pipeline business model is disintegrating, making room for value chains that are becoming more fragmented and chances for new business models. A network of diverse business players from backgrounds including banking, insurance, pension, communications, real estate, education, healthcare service providers and IT are part of the new business model that the researchers have found. They work together to benefit each other through coexisting. The result of these developments and transformation is that financial services will continue to function in innovative and distinctive ways from those previously observed.

Cluster 4: Role of IT in banking

The fourth cluster concentrated on the crucial part information technology (IT) plays in the supply of financial services. According to [ 37 ], several banks have used information technology (IT) to provide consumers with a variety of more effective services. They think that in order to gain clients and boost profits in a cutthroat business environment, bank management must simultaneously use a variety of service channels. The majority of earlier research on IT investment in the banking sector has been on implementing cutting-edge IT-based service channels, including Internet banking, from the perspectives of clients [ 37 ]. From the standpoint of the bank, Barkhordari et al. [ 37 ] demonstrate that IT has a beneficial effect on performance by taking into account both the conventional physical and alternative IT-based service channels at once. They came to the conclusion that the purpose of using IT-related tools in banking is to forward a strategic, transformative objective. Due to the advancement of modern IT, the relationship between banks and their customers has changed substantially over the past few decades. They claimed that some of the examples include well-known innovations such as automated teller machines (ATMs), online banking (e-banking), and straight-through processing (STP), as well as others that have not (yet) gained widespread adoption, such as electronic cash (e-cash), or electronic bill presentment and payment (EBPP). At least the first has changed how people and businesses manage their finances and had an impact on the entire sector. They outlined how the aforementioned advances needed structures that took trends into account and might broaden the scope of current bank architectures to include horizontal and vertical integration dimensions. According to [ 38 ], enterprise architecture is typically represented by the following layers and design objects:

Product/services, market segments, corporate strategy goals, strategic plans/projects and interactions with customers and suppliers are all included in the strategic layer.

Organizational layer: Information flows, organizational units, roles/responsibilities, sales channels and business processes.

Applications, application domains, business services, IS functionalities, information objects, and interfaces make up the integration layer.

Software layer: programs, data structures, etc.

Hardware components, network components, and software platforms make up the IT infrastructure layer.

When it comes to transformations, architectures are really useful, because they integrate many layers. Creating new businesses or reorganizing old ones is transformation.

According to [ 32 ], organizations that are successful over the long term have basic principles and purposes that never change while continuously adapting their business strategies and operations to the external environment. IT's penetration of the banking industry falls under this category of business change. Liu et al. [ 22 ] contributed to the conversation by asserting that technological advancements like high-frequency trading systems (HFT) and algorithmic trading systems had altered the financial markets. The point is that information technology (IT) makes it possible to design complex products, improve market infrastructure, apply adequate risk management strategies and aid financial intermediaries in reaching geographically remote and diverse markets. The Internet has considerably impacted the delivery methods used by banks. The Internet has become an essential medium for distributing banking services and goods.

Cluster 5: Response to DBT

This fifth and final cluster considered the attitude of staff and clients toward DBT. If computer systems are not utilized, they cannot increase organizational performance. Unfortunately, managers' and professionals' opposition to end-user technology is a common issue. We need to comprehend why people accept or reject computers in order to better forecast, explain and promote user acceptance. The findings point to the potential for straightforward yet effective models of user acceptance factors, with practical utility for assessing systems and directing managerial actions aimed at addressing the issue of underutilized computer technology. Agarwal and Prasad [ 39 ] assert that a recent lack of user adoption of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and increases in productivity. They continued by saying that the academic and professional sectors had grown concerned about this paradoxical connection between spending on information technology and increases in productivity. The axiom that systems that are not used generate little value is an often proposed explanation for this relationship. Therefore, in order to achieve the expected productivity advantage, it is not enough to simply have the technology available; it must also be accepted and used effectively by its target user group [ 39 ]. The work of DeLone and McLean threw more light on technology acceptance. When [ 32 ] created a thorough taxonomy, they provided a more comprehensive picture of the concept of information system success. Six main characteristics or categories of the success of information systems are proposed by this taxonomy: system quality, information quality, utilization, user satisfaction, individual impact and organizational impact. Meanwhile, further discussions in this cluster have given more insights into customer acceptance or otherwise of IT in banking. Perceived utility, perceived ease of use, trust and perceived enjoyment are discovered to be immediate direct drivers of customers' views toward utilizing Internet banking, according to [ 40 , 41 ] research. This finding is consistent with some of the findings of other studies. The clients' behavioral intentions to utilize Internet banking are determined by attitude, perceived risk, fun, and confidence. Although the perceived website design has a direct impact only on perceived usability, its indirect effects on perceived usefulness, attitude and behavioral intentions are considerable. Perceived enjoyment only has a short-term impact on perceived ease of use, but both a direct and indirect influence on perceived usefulness. Customer experience is at the heart of the digital banking transition. Therefore, banks must continuously innovate products, integrate cutting-edge technology and add value for their clients.

Keywords analysis

The trends in the keywords displayed in multiple studies can be used to determine the main study direction for upcoming investigations [ 42 ]. The VOSviewer r software, which has previously been utilized by other writers, is employed in this study to extract the author's keywords [ 12 , 21 , 43 ]. A co-occurrences network is produced by the VOS viewer program as a dimensional map [ 12 ]. We used bibliographical author keyword analysis to examine our sample and determine whether there was any increasing or declining themes of interest per research question four. We discovered that writers of the 268 publications in our sample employed 829 keywords to indicate their scientific work, meeting the studies' threshold. Only 26 words, or around 3% of the total, were used at least four times. Our findings imply that the literature on DBT is incredibly heterogeneous. Indeed, according to the results of most recent articles, 80 percent of the authors' specified keywords were utilized precisely once. However, there are several keywords that authors frequently utilize to describe their works (Fig.  6 ). FinTech is the most often used keyword, with 25 occurrences and 29 links to other keywords, followed by digitalization, with 18 and 20 links. Reporting on Digital Transformation contains 13 instances and 18 links. The bibliometric map of author keywords is shown in Fig.  6 .

Bibliometric map of author keywords co-occurrence with five minimum occurrences and overlay visualization mode

The theme areas contemporary academics focus on can be seen by closely examining the map. The use of bibliographic coupling is based on the subject the authors are investigating. The digital transformation of financial service delivery was investigated by [ 43 ] from the perspective of Nigeria about chatbot adoption. A moderated mediated model was used by [ 44 ] to examine how blockchain technology was adopted in the financial sector during the fourth industrial revolution. Additionally, Karjaluoto et al. [ 19 ] looked at how users' perceptions of value influence their use of mobile financial services apps. Similarly, Podsakoff et al. [ 16 ] focused on enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Taking the discussion to a different dimension, Teng and Khong [ 45 ] worked on Examining actual consumer usage of E-wallets: A case study of big data analytics. David-West et al. [ 46 ] examined sustainable business models to create mobile financial services in Nigeria. Yip and Bocken [ 23 ] deepened the discussion and, in turn, looked at Sustainable business model archetypes for the banking industry. Finally, Niemand et al. [ 20 ] highlighted digitalization in the financial sector: a backup plan with a strategic focus on digitalization and an entrepreneurial attitude. Future research on financial services provided via e-wallets and mobile banking is the main emphasis of the second cluster. Authors are still studying entrepreneurship and digitalization in the supply of financial services. Future research is required in these areas of study because blockchain technology and digital currency are also gaining traction in the literature. The most popular search terms and the number of times they were used are displayed in Table 6 .

Discussions and future research agenda

The first paper on DBT was published by [ 3 ], and since then, both its audience and popularity have grown. Yet, the rapid rise in total publications across a wide range of specialist areas, notably during the last five years, has made it increasingly difficult for academics to ascertain the intellectual structure of the field. Existing qualitative assessments, which usually only address a small fraction of Digital Transformation in Banking while failing to accurately capture the entire body of work, have in some ways made the problem of theoretical specificity worse. It is rather tricky for a qualitative evaluation to describe more than 260 works over three decades. Thus, our research fills a critical vacuum in the literature by thoroughly (and quantitatively) mapping the digital banking domain, documenting its conceptual structure and suggesting its most likely future orientations. The theoretical underpinnings from which they have been developed, the subtopics and subthemes they have written about, and the notable historical contributors to DBT study (such as scholars, schools, and journals) are all identified in our work over time. Overall, our findings imply a considerable worldwide impact of digitization on banking, making it a truly global study paradigm. Additionally, the high number of citations for recent works shows that there is a great need for more research utilizing the DBT theoretical framework, suggesting that the field of study will continue to advance for a very long period. The study's structure is based on a wide range of goals and inquiries.

The initial research question aimed to characterize the increase in publication (document by year and county) and productivity of journals in terms of citations, top authors and institutions of studies on DBT. According to the data that are currently available, 174 papers, or 72% of all scientific publications, were published in the last six years, from 2016 to 2022. Also, prestigious journals carried out more than 40% of the publications. Therefore, our data imply that the quantity and quality of published research have typically stayed up. Our data also show that the research on the DBT is genuinely global in scope, as seen by the contributions of authors from 65 different countries. China and the UK are split equally, with India coming in second. It is essential to add that the BRIC (Brazil, Russia, India and China) countries perform well with publications. African countries like Ghana and Nigeria are equally showing promising signs of publications in this light. Regarding journal productivity, the study has revealed that articles on the banking industry's digital transformation are published in high-caliber journals in the A and A* classes. In our statistics, three top-five journals fall into the A category. These are the International Journal of Information Management (A*), Journal of Information Technology (A*), and Journal of Cleaner Production (A). We found 598 distinct writers from 224 organizations publishing on the subject of DBT inside our dataset. The descriptive statistics also reveal that Ranti et al. (2020) have the most citations, while the Financial University of the Government of the Russian Federation is the most productive institution in terms of the DBT, with seven publications.

The second research topic analyzes the co-authorship analysis and citation analysis by nation of authorship. Figure  3 shows that the UK has the maximum amount of collaboration, with 16 links and 18 co-authorships. China, Hong Kong and the Netherlands tie for second place with six linkages each. The increase in foreign students seeking second and third degrees in the UK and China may be one factor fostering closer ties between the two countries [ 21 ]. The UK and China are two other critical technological superpowers establishing the foundation for digitization. This might have attracted scholars and prompted them to conduct studies in the area. Future research might study the effects of digitization on banking on enforcing public and private sector regulations in emerging nations like Africa.

The third research question assesses the intellectual structure of the knowledge of DBT. This result was attained through citation analysis. Finding the most important publications in a specific field of study through citation analysis involves looking at the relationships between publications [ 5 ]. The primary point of contact for enabling retail banking and consumer transactions in the past has been actual bank branches. Customers are still transitioning from in-person to digital transactions as technology develops thanks to a complimentary effect brought on by increased access to digital banking services as well as an improved user experience of new digital access products, services and an improved user interface. Further research revealed that the banking sector's transition to digitization had increased competitiveness among service providers. The citation analysis highlighted the impact of FinTech on financial services innovations. According to [ 8 ], FinTech ushers in a new paradigm where information technology drives innovation in the financial sector. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. We discovered that the corporate world is rapidly digitizing, removing industry barriers, opening up new opportunities, and dismantling long-established business structures. The concept of a business model and, to a greater extent, the new banking business model was also included in the analysis. The authors proposed that businesses build the capacity to innovate their business models since it makes good business sense. For instance, it has been seen that social media is significantly influencing the business models of some digitally focused banks. Social media, according to some, has the power to radically alter customer–bank interactions and improve how the two sides communicate in the future. If banks are to have an impact, they must transition from relying on a single, vertically integrated business model to multiple non-linear models and roles in the value chain. As a result of these developments and transformations, financial services will continue to operate in novel and unique ways from those previously observed. The study has proven beneficial for the use of IT in banking. IT-related tools are used in banking to advance a strategic transformational goal. The connection between banks and their customers has altered significantly over the past few decades with the development of contemporary IT. The most prevalent enterprise architecture layers and design items, according to [ 38 ], are the strategic, organizational, integration, software and IT infrastructure. It has been established that information technology (IT) enables the development of complicated products, enhances market infrastructure, implements efficient risk management techniques and enables financial intermediaries to access diverse and geographically dispersed markets. Despite the enormous advantages of digital banking, opinions on the systems are widely divided. Agarwal and Prasad [ 39 ] claim that a recent lack of user acceptance of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and productivity increases. They said that the counterintuitive connection between productivity increases and information technology investments had alarmed academic and professional groups. According to theories advanced by academics, digital technology, in general, and information systems, in particular, must fall under one of the following taxonomies to be accepted and used: system effectiveness, accuracy of the data, usability, user happiness, personal effect and organizational effect. The fourth research question looked at the future directions and emerging research themes and trends in studies of the digital banking transition. Future scholars are still interested in business models, FinTech, and DBT or banking. Additionally, the focus of the conversation is rapidly shifting to emerging and developing economies. Nevertheless, contemporary research areas include blockchain [ 44 ], mobile financial services apps [ 19 ], artificial intelligence and mobile banking service platforms [ 47 ], and sustainable business models [ 46 ]. The importance of highlighting the need for additional research in these fields of study cannot be overstated, given the growing popularity of blockchain technology and digital currency in literature.

Implications for theory

At least four substantial contributions to the body of DBT research, in our opinion, have been made by this study. We contribute primarily by expanding on current DBT reviews. While other reviewers have used qualitative methodologies, we may supplement and expand on such assessments by utilizing a thorough bibliometric study, allowing us to be more explicit about DBT's intellectual progress and structure. This is significant because it gives us a unique opportunity to highlight notable contributors and pinpoint the present and past origins of DBT research. Second, our quantitative analysis of bibliographic data demonstrates how DBT research has developed into its paradigm, which is supported by the original article by Bürk and Pfitzmann [ 3 ]. Third, we make a contribution by detecting rising and negative trends in subtopic areas, so identifying the subjects that are most likely to be studied in the future by academics. Fourth, by conducting a comprehensive assessment of DBT, we pinpoint areas where theory and practice diverge and evaluate the ways in which researchers have aided practitioners by modernizing DBT to comprehend and foresee the difficulties of "real-world" business.

Implications for practice

The banking sector, like other sectors, aspires to embrace contemporary practices and incorporate digital technologies into its operational procedures. This complicated collection of measures necessitates a methodical and considered approach, particularly in financial services where substantial sums of money and severe risks are at stake. DBT in this sense refers to several adjustments made to the banking sector to integrate different FinTech technologies to automate, optimize, and digitize procedures and improve data security. The processes and technologies employed in the financial industry will alter due to several small and significant changes implied by this process. The fundamental tendency of digital transformation, regardless of industry, is the integration of computer technologies, and Statista's analysis indicates that this trend will continue to expand. The challenges posed by introducing new digital innovations must be understood by stakeholders, who must also articulate solutions. Again, embracing digital technologies will involve taking on several tremendous risks; for this reason, bank executives must simultaneously establish and implement a strategy for managing those risks. If regulators utilizing technology to oversee and control the industry want to ensure solid financial stability in the economy, they must constantly be ahead of innovation risk with appropriate countermeasures. Digital banking involves the collection and processing of vast volumes of customer data. This raises the issue of data protection following regulations and international best practices. The DBT's third useful outcome is that it prompts organizational leaders to consider how their personal biases—which are the products of their histories, characteristics and experiences—might influence opinions and, ultimately, bank performance.

Limitations

We know that no study is faultless, and ours has its setbacks. While we made every effort to minimize problems, we nevertheless expect to offer insightful suggestions for future bibliometric and DBT studies. First, we used the Scopus database, a popular database used in bibliometric research, to gather our bibliometric data [ 48 ]. Even though Scopus contains the most data sources, it does not include all research databases on the transformation of digital banking. Furthermore, because this database has so many uses, using Scopus for data collection could likely lead to mistakes that show up when performing bibliometric analysis. To put it another way, errors might have happened if articles were mislabeled, and it is possible that the database completely missed publications important to our study [ 49 ]. To address this potential issue, we followed the best bibliometric analysis methods. For instance, we thoroughly purged duplicates and other forms of incorrect items from our data. Additionally, this research is restricted to English-language publications, and the subject only includes business, management, finance, economics, FinTech and banking digitalization. The data search will be enhanced, and the search restriction will be reduced using several databases.

This article assesses the intellectual landscape and future potential of the field of DBT research, as well as the influence of that research. The approach for this study is based on descriptive analysis, performance analysis and science mapping analysis, and it employs bibliometric analysis. The set was created based on 268 documents from the Scopus database that span the years 1989 to 2022. We demonstrate that DBT has continued to be a hot topic for academic research approximately three decades after its conception. Our findings also indicate that the UK, USA, Germany and China are the countries that have conducted most of the studies on the DBT. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. We further categorize the body of research on DBT into five main clusters, including (1) Digital Banking Innovation, (2) FinTech and RegTech in Banking, (3) The New Digital Business Model of Banks and Other Financial Service Providers, (4) The role of IT in banking, (5) Response to DBT. Due to a significant influx of international students, the UK, China and Hong Kong continue to be the most collaborative countries. Additional research reveals that papers rated with A* and A grades frequently publish studies on DBT. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. FinTech, block chain mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models are currently researched. Given the rising popularity of block chain technology and digital money in the literature, highlighting the need for more research in these areas of study cannot be overstated. This study builds on previous reviews by objectively charting the inception and intellectual growth of the digital banking area and evaluating its future possibilities. In essence, this bibliometric study offers a distinct and original viewpoint on the evolution of DBT by carefully and objectively assessing prior material and concurrently offering a clear road map for future work.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author upon request.

Abbreviations

Digital banking transformation

Financial technology

Regulatory technology

Internet of things

Automatic teller machine

Artificial intelligence

Information technology

Information communication technology

Straight through processing

Electronic banking

Electronic cash

Electronic bill presentment and payment

High-frequency trading system

Electronic wallets

Barrett M, Davidson E, Prabhu J, Vargo SL (2015) Service innovation in the digital age special issue: service innovation in the digital age service innovation in the digital age: key contributions and future directions Source: MIS Q 39:135–154. https://doi.org/10.2307/26628344

Berger AN (2003) The economic effects of technological progress: evidence from the banking industry. https://about.jstor.org/terms

Bürk H, Pfitzmann A (1989) Digital payment systems enabling security and unobservability. Comput Secur 8:399–416. https://doi.org/10.1016/0167-4048(89)90022-9

Article   Google Scholar  

Dharmani P, Das S, Prashar S (2021) A bibliometric analysis of creative industries: current trends and future directions. J Bus Res 135:252–267. https://doi.org/10.1016/J.JBUSRES.2021.06.037

Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/J.JBUSRES.2021.04.070

White JV, Borgholthaus CJ (2022) Who’s in charge here? A bibliometric analysis of upper echelons research. J Bus Res 139:1012–1025. https://doi.org/10.1016/J.JBUSRES.2021.10.028

Omarini A (2017) Current Position: Tenured Researcher at the Department of Finance

Lee I, Shin YJ (2018) Fintech: ecosystem, business models, investment decisions, and challenges. Bus Horiz 61:35–46. https://doi.org/10.1016/J.BUSHOR.2017.09.003

Lee J, Wewege L, Thomsett MC (2020) Disruptions and Digital Banking Trends, (online) Scientific Press International Limited. https://www.researchgate.net/publication/343050625

Dietz M, Härle P, Khanna S (n.d) A digital crack in banking’s business model

Rauch A (2020) Opportunities and threats in reviewing entrepreneurship theory and practice. Entrepreneurship: Theory Pract 44:847–860. https://doi.org/10.1177/1042258719879635

Anand A, Brøns Kringelum L, Øland Madsen C, Selivanovskikh L (2020) Interorganizational learning: a bibliometric review and research agenda. Learn Organ 28:111–136. https://doi.org/10.1108/TLO-02-2020-0023

Kumar S, Pandey N, Kaur J (2023) Fifteen years of the : a retrospective using bibliometric analysis. Soc Respons J 19:377–397. https://doi.org/10.1108/SRJ-02-2020-0047

Short J (2009) The art of writing a review article. J Manage 35:1312–1317. https://doi.org/10.1177/0149206309337489

Block J, Fisch C, Rehan F (2020) Religion and entrepreneurship: a map of the field and a bibliometric analysis. Manag Rev Q 70:591–627. https://doi.org/10.1007/s11301-019-00177-2

Podsakoff PM, MacKenzie SB, Podsakoff NP, Bachrach DG (2008) Scholarly influence in the field of management: a bibliometric analysis of the determinants of University and author impact in the management literature in the past quarter century. J Manage 34:641–720. https://doi.org/10.1177/0149206308319533

Widharto P, Pandesenda AI, Yahya AN, Sukma EA, Shihab MR, Ranti B (2020) Digital Transformation of Indonesia Banking Institution: case study of PT. BRI Syariah. In: 2020 International conference on information technology systems and innovation (ICITSI), 2020, pp 44–50. https://doi.org/10.1109/ICITSI50517.2020.9264935

Harjanti I, Nasution F, Gusmawati N, Jihad M, Shihab MR, Ranti B, Budi I (2019) IT impact on business model changes in banking Era 4.0: case study Jenius. In: 2019 2nd International conference of computer and informatics engineering (IC2IE), pp 53–57. https://doi.org/10.1109/IC2IE47452.2019.8940837

Karjaluoto H, Shaikh AA, Saarijärvi H, Saraniemi S (2019) How perceived value drives the use of mobile financial services apps. Int J Inf Manage 47:252–261. https://doi.org/10.1016/J.IJINFOMGT.2018.08.014

Niemand T, Rigtering JPC, Kallmünzer A, Kraus S, Maalaoui A (2021) Digitalization in the financial industry: a contingency approach of entrepreneurial orientation and strategic vision on digitalization. Eur Manag J 39:317–326. https://doi.org/10.1016/J.EMJ.2020.04.008

Khatib SFA, Abdullah DF, Elamer A, Yahaya IS, Owusu A (2023) Global trends in board diversity research: a bibliometric view. Meditar Account Res 31:441–469. https://doi.org/10.1108/MEDAR-02-2021-1194

Liu Y, Luan L, Wu W, Zhang Z, Hsu Y (2021) Can digital financial inclusion promote China’s economic growth?. Int Rev Financ Anal 78: 101889. https://doi.org/10.1016/J.IRFA.2021.101889

Yip AWH, Bocken NMP (2018) Sustainable business model archetypes for the banking industry. J Clean Prod 174:150–169. https://doi.org/10.1016/j.jclepro.2017.10.190

Kent Baker H, Pandey N, Kumar S, Haldar A (2020) A bibliometric analysis of board diversity: current status, development, and future research directions. J Bus Res 108:232–246. https://doi.org/10.1016/J.JBUSRES.2019.11.025

Gomber P, Kauffman RJ, Parker C, Weber BW (2018) On the Fintech Revolution: interpreting the forces of innovation, disruption, and transformation in financial services. J Manag Inf Syst 35:220–265. https://doi.org/10.1080/07421222.2018.1440766

Fain D, Lou Roberts M (1997) Technology vs. consumer behavior: the battle for the financial services customer. J Direct Market 11:44–54. https://doi.org/10.1002/(sici)1522-7138(199724)11:1<44::aid-dir5>3.0.co;2-z

Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43:115–135. https://doi.org/10.1007/s11747-014-0403-8

Laukkanen T (2016) Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking. J Bus Res 69:2432–2439. https://doi.org/10.1016/J.JBUSRES.2016.01.013

Chaouali W, Souiden N (2019) The role of cognitive age in explaining mobile banking resistance among elderly people. J Retail Consum Serv 50:342–350. https://doi.org/10.1016/J.JRETCONSER.2018.07.009

Schueffel P (2016) Taming the beast: a scientific definition of Fintech. J Innov Manag Schueffel JIM 4:32–54

Google Scholar  

Anagnostopoulos I (2018) Fintech and regtech: impact on regulators and banks. J Econ Bus 100:7–25. https://doi.org/10.1016/J.JECONBUS.2018.07.003

Porter ME (1980) Industry structure and competitive strategy: keys to profitability. Financ Anal J 36:30–41. https://doi.org/10.2469/faj.v36.n4.30

Chesbrough H (2010) Business model innovation: opportunities and barriers. Long Range Plann 43:354–363. https://doi.org/10.1016/J.LRP.2009.07.010

Durkin M, Mulholland G, McCartan A (2015) A socio-technical perspective on social media adoption: a case from retail banking. Int J Bank Market 33:944–962. https://doi.org/10.1108/IJBM-01-2015-0014

Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. J Strateg Inf Syst 24:149–157. https://doi.org/10.1016/J.JSIS.2015.08.002

Shafer SM, Smith HJ, Linder JC (2005) The power of business models. Bus Horiz 48:199–207. https://doi.org/10.1016/J.BUSHOR.2004.10.014

Barkhordari M, Nourollah Z, Mashayekhi H, Mashayekhi Y, Ahangar MS (2017) Factors influencing adoption of e-payment systems: an empirical study on Iranian customers. Inf Syst E-Business Manag 15:89–116. https://doi.org/10.1007/s10257-016-0311-1

Winter R, Fischer R (2006) Essential layers, artifacts, and dependencies of enterprise architecture. In: 2006 10th IEEE international enterprise distributed object computing conference workshops (EDOCW’06), p 30. https://doi.org/10.1109/EDOCW.2006.33

Agarwal R, Prasad J (1997) The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decis Sci 28:557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x

Panetta IC, Leo S, Delle Foglie A (2023) The development of digital payments: past, present, and future—from the literature. Res Int Bus Finance 64: 101855. https://doi.org/10.1016/J.RIBAF.2022.101855

Bashir I, Madhavaiah C (2015) Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research 7:67–102. https://doi.org/10.1108/JIBR-02-2014-0013

Pesta B, Fuerst J, Kirkegaard EOW (2018) Bibliometric keyword analysis across seventeen years (2000–2016) of intelligence articles. J Intell 6:1–12. https://doi.org/10.3390/jintelligence6040046

Abdulquadri A, Mogaji E, Kieu TA, Nguyen NP (2021) Digital transformation in financial services provision: a Nigerian perspective to the adoption of chatbot. J Enterp Commun 15:258–281. https://doi.org/10.1108/JEC-06-2020-0126

Khalil M, Khawaja KF, Sarfraz M (2022) The adoption of blockchain technology in the financial sector during the era of fourth industrial revolution: a moderated mediated model. Qual Quant 56:2435–2452. https://doi.org/10.1007/s11135-021-01229-0

Teng S, Khong KW (2021) Examining actual consumer usage of E-wallet: a case study of big data analytics, Comput Human Behav 121:106778. https://doi.org/10.1016/J.CHB.2021.106778

David-West O, Iheanachor N, Umukoro I (2020) Sustainable business models for the creation of mobile financial services in Nigeria. J Innov Knowl 5:105–116. https://doi.org/10.1016/J.JIK.2019.03.001

Dimitrova I, Öhman P, Yazdanfar D (2022) Barriers to bank customers’ intention to fully adopt digital payment methods. Int J Qual Serv Sci 14:16–36. https://doi.org/10.1108/IJQSS-03-2021-0045

Bhatt Y, Ghuman K, Dhir A (2020) Sustainable manufacturing. Bibliometrics and content analysis, J Clean Prod 260:120988. https://doi.org/10.1016/J.JCLEPRO.2020.120988

Di Vaio A, Palladino R, Hassan R, Escobar O (2020) Artificial intelligence and business models in the sustainable development goals perspective: a systematic literature review. J Bus Res 121:283–314. https://doi.org/10.1016/J.JBUSRES.2020.08.019

Amit R, Zott C (2012) Creating value through business model innovation, MIT Sloan Manag Rev. 48.

Fornell C, Larcker DF (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Market Res. 18:39–50. https://doi.org/10.1177/002224378101800104 .

Porter ME (1996) What Is Strategy?.

Möwes T, Puschmann T, Alt R (2011) Service-based Integration of IT-Innovations in Customer-Bank-Interaction. https://aisel.aisnet.org/wi2011/102 .

DeLone WH, McLean ER (1992) Information Systems Success: The Quest for the Dependent Variable, Info Syst Res. 3:60–95. https://doi.org/10.1287/isre.3.1.60 .

Weill P, Woerner SL (2015) Thriving in an increasing digital ecosystem, MIT Sloan Manag Rev. 15.

Zhao JL, Fan S, Yan J (2016) Overview of business innovations and research opportunities in blockchain and introduction to the special issue, Financial Innovation. 2:28. https://doi.org/10.1186/s40854-016-0049-2

Gassmann O, Enkel E, Chesbrough H (2010) The future of open innovation. R and D Manage 40:213–221. https://doi.org/10.1111/j.1467-9310.2010.00605.x

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology

Zhang S, Riordan R (2011) Association for information systems AIS electronic library (AISeL) technology and market quality: the case of high frequency trading recommended citation. http://aisel.aisnet.org/ecis2011/95

Banker R, Chen P.-Y, Liu F.-C, Ou C.-S (2009) Business value of IT in commercial banks. http://aisel.aisnet.org/icis2009/76

Ende B (2010) Association for information systems IT-driven execution opportunities in securities trading: insights into the innovation adoption of institutional investors recommended citation Ende, Bartholomäus, “it-driven execution opportunities in securities trading: insights into the innovation adoption of institutional investors,”. http://aisel.aisnet.org/ecis2010 ; http://aisel.aisnet.org/ecis2010/118

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Acknowledgements

The authors would like to graciously thank the Editor-in-Chief and the editorial team, and the two anonymous reviewers for their feedback in developing this paper. The writers also acknowledge Prof. Alfred Owusu, Dean of KsTU's Business School, for his guidance, inspiration and support. We appreciate his inventiveness and how it enabled us to clearly define the goal and possibilities of this effort. The authors also appreciate the helpful advice provided by Dr. Thomas Adomah Worae and Prof. Abdul-Aziz Iddrisu as we worked on the first versions of the manuscript. Finally, we would like to thank Riya Sureka, a research scholar at the Malaviya National Institute of Technology in Jaipur, India, for his advice on how to analyze bibliometric data using the ‘R’ and VOS viewer software.

This research received no external funding.

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Lambert Kofi Osei &  Kofi Mintah Oware

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All authors contributed significantly to the development of this article; LK generated the title, wrote the introduction, collection and analysis of the data, interpreted the co-citation analysis and put the manuscript together. YC reviewed the existing to conceptualize the study, reviewed the study and expanded the analysis. KM involved data generation from Scopus data base, software running, data analysis and review of the work. All authors read and approved the final manuscript.

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Lambert Kofi Osei holds a masters of business administration (finance option) degree from the Kwame Nkrumah University of Science and Technology. He is currently a PhD finance and banking student of Siberia Federal University, Russia. He is currently a lecturer at the Department of Banking Technology and Finance—Kumasi Technical University—in Ghana. He also holds an associated charted membership with the Chartered Institute of Securities and Investment—UK. Osei is certified expert in microfinance (CEMF) from the Frankfurt School of Finance—Germany. Osei has had considerable level of industry experience, with over 12 years managerial experience in the banking industry in Ghana including been the chief executive officer of Eman Capital. Prior to joining Kumasi Technical University, he was the National Chairman of Ghana Association of Microfinance Companies (GAMC)—an umbrella body of all microfinance companies in Ghana. Despite joining academia recently, Osei has made two publications of his work and a lot more articles are under completion stage to be sent for review. It is the goal of him to be an authority in the field of digital banking to impact businesses and societies.

Yuliya Cherkasova holds Ph.D. in economics and is a associate professor, School of Economics, Finance and Public Administration, Siberian Federal University. She is the chair of Digital Financial Technologies of Sberbank of Russia. Her research interests include banking prudential regulation of banks, digital economy and public finance. As a researcher, she has published more than 70 articles, 10 textbooks on topics, related finance and banking aria.

Kofi Mintah Oware has a Ph.D. in business administration (sustainability finance and management) from Mangalore University, India, and an MBA degree from Aberdeen Business School (Robert Gordon University—UK). He is currently a senior lecturer in the department of banking technology and finance. He is also a chartered accountant with membership from the Institute of Chartered Accountants (ICA), Ghana, and Institute of Cost Executive & Accountants (ICEA)—UK. Before joining academia, he worked in blue-chip companies for 12 years in various capacities, including chief accountant, head of finance and general manager for finance & administration in Ghana and research consultant to Aberdeen Businesswomen network in the UK. Among his key roles during industry experience include representing management in union negotiations and presenting the firm's financial reports in the corporate board meeting. In academia, he has 34 publications in various journal, including two "A" s under ABDC (Meditari Accountancy Research), three "B" s under ABDC (Social Responsibility Journal & Society and Business Review) and one C (South Asian Journal of Business Studies) all with Emerald publications. Also, he has 10 academic papers in various journals under review.

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Osei, L.K., Cherkasova, Y. & Oware, K.M. Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend. Futur Bus J 9 , 30 (2023). https://doi.org/10.1186/s43093-023-00207-2

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DOI : https://doi.org/10.1186/s43093-023-00207-2

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International Journal of Quality and Service Sciences

ISSN : 1756-669X

Article publication date: 4 February 2022

Issue publication date: 3 May 2022

This study aims to demonstrate digital banking’s influence on customers’ evaluation of service experience and develop a framework identifying the most significant variables of digital banking that influence the financial performance of banks.

Design/methodology/approach

This structured review of literature, guided with the preferred reporting items for systematic reviews and meta-analyses framework, takes a digital banking perspective to identify 88 articles published between 2001 and 2021, examining distinct aspects of digital banking and their impact on financial performance.

Customer experience (CE) is determined by functional clues (functional quality, trust and convenience), mechanic clues (website attributes, website design, perceived usability) and humanic clues (customer complaint handling). The study is furthered to combine CE with the service profit chain model. This study also fills the gap to understand the use of “gamification” in technology-driven banking services to enhance CE. Finally, an integrative framework is proposed to link technology-related factors (digital banking clues and gamification), customer-related factors (CE, customer satisfaction and customer loyalty) and performance-related factors (financial performance).

Practical implications

The study conceptualises a “total” CE framework that banks can use to enhance their online presence. Banking service providers could also analyse their financial results based on digital banking’s impact on customers. Besides, banks can use this framework to strategically place “game-like features” in their digital platforms.

Originality/value

This study attempts to significantly contribute to the digital marketing literature related to CE with banks. It is one of the first studies to determine gamification explicitly in banking literature.

• Customer experience
• Digital banking
• Customer satisfaction
• Customer loyalty
• Financial performance
• Gamification
• Internet banking

Acknowledgements

Funding: Authors have not received any funding support to complete this work.

Conflict of interest statement: The authors declare that they have no conflict of interest.

Chauhan, S. , Akhtar, A. and Gupta, A. (2022), "Customer experience in digital banking: a review and future research directions", International Journal of Quality and Service Sciences , Vol. 14 No. 2, pp. 311-348. https://doi.org/10.1108/IJQSS-02-2021-0027

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Research Article

A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations University of Granada, Granada, Spain, Bangor University, Hen Goleg, Bangor, United Kingdom, Funcas, Madrid, Spain, CUNEF, Madrid, Spain

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Funcas, Madrid, Spain, CUNEF, Madrid, Spain

Affiliations University of Granada, Granada, Spain, Funcas, Madrid, Spain

• Santiago Carbo-Valverde, 
• Pedro Cuadros-Solas, 
• Francisco Rodríguez-Fernández

• Published: October 28, 2020
• https://doi.org/10.1371/journal.pone.0240362
• Reader Comments

Understanding the digital jump of bank customers is key to design strategies to bring on board and keep online users, as well as to explain the increasing competition from new providers of financial services (such as BigTech and FinTech). This paper employs a machine learning approach to examine the digitalization process of bank customers using a comprehensive consumer finance survey. By employing a set of algorithms (random forests, conditional inference trees and causal forests) this paper identities the features predicting bank customers’ digitalization process, illustrates the sequence of consumers’ decision-making actions and explores the existence of causal relationships in the digitalization process. Random forests are found to provide the highest performance–they accurately predict 88.41% of bank customers’ online banking adoption and usage decisions. We find that the adoption of digital banking services begins with information-based services (e.g., checking account balance), conditional on the awareness of the range of online services by customers, and then is followed by transactional services (e.g., online/mobile money transfer). The diversification of the use of online channels is explained by the consciousness about the range of services available and the safety perception. A certain degree of complementarity between bank and non-bank digital channels is also found. The treatment effect estimations of the causal forest algorithms confirm causality of the identified explanatory factors. These results suggest that banks should address the digital transformation of their customers by segmenting them according to their revealed preferences and offering them personalized digital services. Additionally, policymakers should promote financial digitalization, designing policies oriented towards making consumers aware of the range of online services available.

Citation: Carbo-Valverde S, Cuadros-Solas P, Rodríguez-Fernández F (2020) A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests. PLoS ONE 15(10): e0240362. https://doi.org/10.1371/journal.pone.0240362

Editor: Baogui Xin, Shandong University of Science and Technology, CHINA

Received: April 8, 2020; Accepted: September 24, 2020; Published: October 28, 2020

Copyright: © 2020 Carbo-Valverde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data can be accessed by request to Funcas Foundation. Data are made available upon request -not shared publicly – because data are owned by Funcas (a third-party organization). Funcas is a non-profit organization focused on fostering academic research that aims to ensure that data are solely used for academic purposes. Then, in order to ensure that data are not going to be used for non-academic purposes (e.g. marketing or commercial) Funcas’s Ethics committee requires to verify that researchers are going to use them for only for academic purposes. Authors are not responsible for ensuring data access. Data access is granted by request to Funcas Foundation (Caballero de Gracia, 28 28013 Madrid (Spain) or directly via email to [email protected] ).

Funding: Financial support from the FUNCAS Foundation, PGC2018 – 099415 – B – 100 MICINN/FEDER/UE, and Junta de Andalucía P18-RT-3571 Project and P12.SEJ.2463 (Excellence Groups) is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

At the end of 2019, 53.6% of the global population, or 4.1 billion people, used online digital devices, according to Information and Communications Technology (ICT). Digitalization is changing the shape of many industries and the way companies and clients interact. This digital revolution has been particularly relevant in the banking industry where the use of digital banking (online and mobile) has become one of the most strategic channels used by bank customers. The Organization for Economic Co-operation and Development (OECD) has identified some of the core properties and crosscutting effects of the digital transformation [ 1 ] as the most important business challenge currently underway. Furthermore, the OECD recognizes banking as one of the sectors where such transformation is more relevant in economic, organizational, and social terms.

On the supply side, financial institutions have gradually reacted to these changes. Banks are particularly sensitive to the transformation of information systems, the treatment of personal data, and the emergence of new (fully digital) competitors and delivery channels. Despite incorporating online distribution channels two decades ago, and in spite of the renewed digitalization wave, banks continue to develop more information and systems-oriented business models. Digitalization is not only focused on cost savings, but also includes process improvements to enhance customer experiences [ 2 – 5 ]. This effort is driven by both rival precedence [ 6 , 7 ] and changes in demand [ 8 ].

A large number of studies on banking organization and technology have addressed the adoption of the most basic electronic banking services developed over the last few decades including debit and credit cards and more recently online banking (although partially covered). Prior literature has revealed a variety of mechanisms—motivations, attitudes, behavioral intention, social systems, and associations—involved in technology adoption. These studies have found that perceived security, usefulness, quality, and convenience drive consumer adoption of online services [ 9 – 14 ]. However, the relevance of each of these factors depends on the stage of the adoption. This is an important lesson for new digital services given the heterogeneous penetration they have both geographically and demographically [ 15 ]. This is particularly relevant considering that socio-demographic characteristics—age, gender, income, and location—[ 11 , 16 , 17 ], cultural characteristics [ 18 ], and customer experience (with other products with varying levels of technological sophistication) are strongly related to the demand for online banking services [ 19 ].

However, while the initial adoption of digital services could be examined using standard parametric statistical methods, examining customers’ digital journey is more complex. Digitalization is a challenging endeavor where several factors drive digitalization decisions [ 5 , 20 ]. Machine learning methods have emerged as powerful tools for data mining [ 21 – 24 ]. Instead of being limited to making strong assumptions about the structure of the data, machine learning allows researchers to identify and display complex patterns in a data-driven form [ 25 ]. In this sense, a machine learning approach is gaining ground in examining consumer behavior such as consumer preferences for technology products Chen, Honda, & Yang [ 26 ], travel choices [ 27 ] or to model consumer response [ 21 ].

This paper aims to benefit from the advantages of following a machine learning approach in order to examine the bank customers’ digitalization process. The use of machine and causal machine algorithms in our research context allows us to reveal the process that individuals follow to make their financial digitalization choices. Unlike prior studies, we are not focused on a single dimension of the digitalization process but on several dimensions (adoption, diversity of use and bank and non-bank’s payment choices).

Methodologically, instead of ex-ante selecting a machine learning technique, we consider a number of machine learning techniques that have proved their value in this field: random forest, extreme gradient boosting, k-nearest neighbor, support vector machine, Bayesian networks and extreme learning machine (see among others [ 28 – 32 ]). After selecting the machine learning with the best performance (in terms of predicted accuracy) we use this algorithm to identify the main features predicting bank customers’ digitalization process. Then, we build a set of classification trees to illustrate the sequence of consumers’ decision-making, and, finally, we make use of causal forests (a causal machine learning technique) to estimate the existence of causal relationships in the digitalization process.

The empirical analysis relies on extensive data collected from a survey—following the structure of the Survey of Consumer Payment Choice (SCPC) [ 33 ]—about digital banking and payment services responded by 3,005 consumers between the ages of 18 and 75. This dataset allows us to explore financial digitalization in a developed country with deep internet penetration (84.6% of adults are internet users), a highly banked population (97.2% of adults have a bank account), and a growing use of electronic banking among consumers (62% of the sample individuals are e-banking users to some extent, although the degree and scope of the adoption varies substantially across individuals), according to OECD, World Bank and GlobalWeb data.

By way of preview, we find that the random forest algorithm achieves the best performance in terms of accuracy to predict bank customers’ digitalization. This algorithm -coupled with the classification trees- reveals that bank customers need to become familiar with the information content of digital services before they begin to make financial transactions. Going digital begins with information-based services and is then followed by transactional services. Customers check their bank balances, make inquiries, and explore the possibilities of the digital channels before making payments, transferring money, or engaging in other transactional services. As for the scope of digitalization, the perceived safety of digital bank services by consumers becomes a critical filter for consumers’ diversified use of digital bank services. However, there appear to be notable exceptions. In the case of mobile banking, for example, even if perceived safety influences consumers’ adoption decisions, the speed and ease of use of the device appear to be more decisive. The efficiency of this service contrasts with the adoption process of more traditional and more established bank services such as credit and debit cards, which are used on a regular basis only when they are perceived as safe and relatively costless. Moreover, consumers adopt other non-bank digital financial services (e.g., Amazon or PayPal) only after they have already become frequent and diversified digital bank customers. These results are also confirmed when using the extreme gradient boosting algorithm and plotting a Bayesian network for each of the dimensions considered. Causal forests reveal that checking online balances has the largest effect on adopting online banking, while making money transfers with a smartphone seems to be relatively more important to become a diversified mobile banking customer. Regarding the use of bank payment methods, we find that the perception of safety has the largest impact on using credit cards while the perception of cost and convenience have the largest impact on paying with debit cards.

These results seem to have relevant business implications for the banking industry when designing strategies to bring on board and keep digital users (e.g., offer digital services focused on satisfying customers’ needs), to face the increasing competition in the payment sector by BigTech and FinTech (e.g. link payments experiences with social media) or to succeed with their digitalization programs (e.g. segmenting customers). Moreover, these results are also valuable for policymakers to design efficient measures to promote financial digitalization.

The remainder of the paper is organized as follows: Section 2 reviews the related literature; Section 3 describes the dataset and the methodology employed; Section 4 discusses the main empirical results; Section 5 addresses the causal impact using causal forests; Section 6 shows the consistency of the findings over alternative supply-side explanations and presents the implications, limitations, and scope for future research; and Section 7 concludes.

2. Related works

The main relevant studies related to financial technology adoption in the digital age refer to firm management and information systems. A number of theories aim to explain the evolution of these new technologies and the interaction between the consumer and the firm. Among them, the technology acceptance model (TAM) [ 34 ] and its latter versions (TAM2 and TAM3) have become popular for explaining how people accept and adopt new technology in the context of banking. The TAM model, which is based on the theory of reasonable action (TRA) [ 35 ] and the theory of planned behavior (TPB) (Ajzen [ 36 , 37 ]), suggests that technological adoption depends on customers’ perception of the utility and ease of use of the technology. Other theories such as the diffusion of innovations (DIT) [ 38 ], the task-technology fit (TTF) [ 39 ], the unified theory of acceptance and use of technology (UTAUT) [ 40 ], and the technology resistance theory (TRT) [ 41 ] have complemented the drivers of online adoption. These theories have thereby given prominence to a number of technological components of the service and not just to consumers’ perceptions. However, as it has recently been argued, those factors explored by the existing literature on information systems may not be sufficient to explain banking digitalization [ 20 , 42 ].

From an empirical standpoint, prior studies on customers’ perceptions have identified the main factors that explain the adoption and utilization of online banking. These include security [ 9 , 10 , 13 , 43 ], ease of use [ 12 – 14 , 44 , 45 ], convenience [ 12 , 13 ], and cost [ 11 , 46 ]. Overall, consumers use e-banking services when they perceive them as safe, useful, convenient, and relatively costless. As for the relative importance of these factors, Hoehle et al. [ 10 ] have surveyed the literature and concluded that security is a major determinant of consumers’ use of e-banking services. Additionally, many of these studies highlight that a range of socio-demographic characteristics [ 11 , 16 , 17 ] and cultural characteristics [ 18 ] also influence the adoption of online banking services. Specifically, young people who have a higher income and live in areas of high internet penetration [ 11 , 47 , 48 ] are prone to using online services. However, as Montazemi and Qahri-Saremi [ 15 ] have highlighted, the importance of these socio-demographic factors depends on the stage of the adoption of online banking services within each market segment or jurisdiction. Moreover, Szopiński [ 19 ] has found that having other banking products such as mortgages and credit cards also has a significant influence on consumers’ use of online banking services.

Closely related to online banking, studies on mobile banking adoption have also recently emerged. The empirical and theoretical approaches in these studies are similar to those to online banking [ 49 – 53 ]. The results of these studies suggest that age is the most decisive factor in mobile banking adoption. However, other determinants such as trust in the device, security, and cost have also been reported to strongly influence the adoption of mobile payments [ 54 ].

Our paper aims to offer a twofold contribution to the existing literature on bank customers’ digitalization. First, by employing a machine learning approach, it reveals the patterns driving the digitalization process. Second, unlike prior studies we do not focus on a single dimension of digitalization. We explore the digital journey of bank customers by examining a number of dimensions (adoption, diversity of use and bank and non-bank’s payment choices) to provide a more complete picture of the digitalization process.

3.1 The survey

The primary data for this study were collected from a consumer survey that was specifically conducted for this research by IMOP Insights during November and December 2016. The survey participants—a population of Spanish consumers between the ages of 18 and 75—were asked about their digital preferences and in particular about those related to banking and payment services. The survey followed the structure of the Survey of Consumer Payment Choice (SCPC) originally conducted by the Federal Reserve Bank of Boston and it is currently conducted by the Federal Reserve Bank of Atlanta. However, our survey incorporated comprehensive information about consumers’ digital preferences and not just about payment services. Controlled quotas for a representative sample of the population were established based on age, sex, and location. The survey was conducted via telephone interviews and resulted in a sample size of 3,005 consumers. The human participation in this study is simply the voluntary participation of subjects in a telephone survey conducted with all the legal and sociological guarantees. The consent of all the survey participants was informed before conducting the questionnaire and this consent was documented as part of the recorded telephone survey. Data were analyzed anonymously by the authors. S1 Appendix in S1 File offers detailed information about the survey and the data collection process and all the variables extracted from the survey questionnaire.

Spain seems to be a good laboratory for this study because it has overcome the initial implementation phase of electronic banking and ranks third in the world for annual growth in mobile banking adoption, according to OECD statistics. The penetration of online banking and the general level of financial digitalization in Spanish society are similar to those in other developed economies. Consequently, the main findings—with the necessary caveats—could likely to be extrapolated to other jurisdictions or would at least be useful for informing other research in different countries. Table 1 illustrates the sample demographics. The representativeness of the survey data is assured by comparing the sample breakdown with the Spanish National Statistics Institute (INE).

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3.2 Descriptive statistics

Fig 1 illustrates the degree to which consumers use various financial services. On average, each banking client has two bank accounts and operates with more than one entity. It is worth noting that while 79.6% of respondents have an online bank account, only 13% are exclusively online users. Regarding the type of financial activities conducted online, internet users reported accessing online banking services to check account balances (68.72% of respondents), to receive online communications from their bank (52.18%), and to make payments or transfer money (51.13%). In the case of mobile banking, the activities lean even more toward checking and communication rather than transactional services.

Fig 1 illustrates the degree to which consumers use various financial services: receiving emails, paying bills, making payments and checking the account balances.

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Fig 19 also provides some descriptive statistics on consumers’ perceptions and on the adoption of non-banking services and social networks by gender, age, and employment status. 91.3% of participants are frequent internet users. In terms of digital equipment, 74.8% of them reported having a laptop, 98.5% reported having a mobile phone and 46.8% reported having a tablet. In any event, the figures suggest that Spanish consumers have attained a medium-high degree of digitalization and a medium degree of financial digitalization. 79.5% of participants have an online bank account. In general, it seems that adults under the age of 45 (working or studying) are the most digitalized. It does not seem to be a gender gap in terms of financial digitalization. More significant differences emerge by employment status as working people tend to be more digitalized than the unemployed. In terms of perceptions, while most of the people perceive online and mobile banking as having a low or very low cost and safe or very safe, this percentage is smaller for those above 65 years. Finally, as expected, young people and users of social media are also more frequent adopters. 50.7% of young people (18–24 years old) have a non-bank account to make payments and 91.1% of them are active users of social networks. However, it seems that social networks such as Facebook and Twitter are seldomly used to interact or to express a complaint to the provider of financial services.

3.3 Dimensions of the digitalization process

Going digital is a much broader concept than is commonly understood. Digitalization is not a single dimensional technological expansion but a multifaceted phenomenon. While literature about the global digitalization of societies has examined several dimensions of the digitalization process [ 55 – 57 ], previous studies on the financial digitalization of consumers have mainly focused on the adoption of online channels. As the OECD has suggested, it is convenient to apply a examine a number of dimensions to explore the digital transformation of bank customers. Furthermore, prior findings in the context of online banking—a variety of mechanisms are involved in technology adoption, and the relevance of each one depends on the stage of the adoption [ 15 ]—suggest exploring more than one dimension to address issues related to digitalization. Consequently, our study assumes a broad definition of adoption that considers not only the first use of a certain service but also its scope and frequency. Fig 2 plots the main dimensions that we identified from earlier studies (see among others [ 9 , 10 , 13 , 48 , 58 – 62 ]): adoption of digital banking, diversification of use, and adoption of bank and non-bank payment instruments. For each dimension, the number of classes is equal to the number of categories in which the individuals are classified (see S3 Appendix in S1 File ).

Fig 2 plots the dimensions of bank customers’ digitalization: adoption of digital banking, diversification of use, and adoption of bank and non-bank payment instruments.

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• Adoption of Digital Banking.

Regarding the adoption of digital banking we examine 3 classes: non-users, occasional users and incipient users. Non-users are defined as those who over the course of the year have not adopted any kind of financial digitalization, including those who are not even digitalized consumers (i.e., they do not use the internet). Respondents who have become digital customers and conduct online banking activities, but not on a monthly basis, are classified as occasional users. Finally, frequent users are those who conducted online financial activities every month over the course of the year. Fig 3 shows that 59% of the survey participants are frequent users of online financial services, which is consistent with the growth of online banking in Spain officially reported by the European Digital Agenda monitoring exercises.

Fig 3 reports the number of surveyed participants for each dimension considered. The total number of surveyed participants is 3,005.

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• Diversity of Digital Use.

While the initial phase of the digital transformation of consumers involves regular online access, going digital is also related to consumers’ use of diverse digital services. Going digital therefore means conducting a number of financial activities online and not just a single online activity (e.g., just checking one’s account balance).

The factors that drive consumers’ digital diversification might be different depending on the capabilities of the electronic device used to access the service. Therefore, we differentiate between the diversification of online banking users and mobile banking users. As such, survey respondents are classified according to the variety of tasks they carry out (check account balances, pay bills, make transfers, or receive communications). Based on these factors, respondents are then sorted into four categories: no digital users, non-users of digital financial services, incipient users and diversified users.

Individuals who are outside of the digitalization process (i.e., who have no access to the internet) are classified as no digital users. In case of mobile banking, those who do not own a smartphone are also classified as no digital users. Individuals who are frequent internet users but do not conduct any financial activity online are classified as non-users of digital financial services. Incipient users are those who perform some but not all online financial activities at least once a month. Finally, those users that carry out all financial activities online at least once a month are classified as diversified users of digital financial services. Fig 3 reveals that most of the respondents are incipient users, which reflects the worth of exploring this dimension. Bank customers also appear to be customers of digital financial services, but they are still far from being considered “omni-digital” users.

• Use of Banks’ Payment Instruments.

Although debit and credit cards cannot be considered fully new electronic payment instruments, we also consider them because there has been a technological and safety evolution (i.e. contactless technology). Individuals are divided into 2 classes: non-debit (non-credit) card users and debit (credit) card users. As Fig 3 shows, there is a larger use of debit cards (78%) in comparison to credit cards (51%).

• Use of Non-Bank Payment Instruments.

While banks have traditionally offered non-cash payment instruments, some technology companies, particularly BigTech and FinTech, have begun to offer non-banking alternatives to pay bills or transfer. Since the adoption of these new means of payment provided by non-financial entities has gained ground, it is interesting to analyze how consumers adopt these alternative means of payments. A non-banking payment instrument is the one that is provided by non-banking institutions (Amazon Pay, PayPal, Google Wallet, Apple Pay, etc.). Regarding the use of these non-bank payment options, 3 classes are considered: non-digital users, non-users of non-bank payment instruments and users of non-bank payment instruments.

Consumers who do not use the internet regularly were classified as non-digital users. Consumers of online financial services who do not use non-bank means of payment were classified as non-users of non-bank payment instruments. Finally, users of non-bank payment instruments include consumers that utilize payment methods of non-bank providers. As illustrated in Fig 3 , most respondents are non-users of non-bank payment instruments despite being digitalized.

4. Methodology

The human participation in this study was voluntary. It consisted of a telephone survey conducted with all the legal and sociological guarantees by the specialised firm IMOP, as stated in the manuscript. The consent of all the survey participants was informed before conducting the questionnaire. This consent was documented as part of the recorded telephone survey which is guarded by IMOP Insights (C/ Antracita n° 7 Planta 4, 28045 –Madrid). The consent has been verified to be legal according to the Funcas’s Ethics committee and approved by the same Funcas's Ethics Committee, which also ensures that the survey has been conducted according to the principles expressed in the Declaration of Helsinki. Data were analyzed anonymously by the authors.

Most previous studies have employed discrete choice models to examine consumer preferences regarding payments and other financial services [ 6 , 14 , 63 ]. These models, derived from utility theory, are based on maximizing consumers’ utility. Other studies have used structural equations. These structural equations are useful for imputing relationships between latent variables that affect e-banking adoption [ 12 , 15 , 43 ].

However, recent studies have shown that digitalization is a challenging endeavor since there are several and complex factors driving the digitalization of people [ 20 ]. Then, this complex patterns suggests that a multidisciplinary approach is required to address digitalization [ 5 ]. In doing so, as Delen & Zolbanin [ 22 ] argue, machine learning techniques complement to the traditional research methods to address this sort of research questions. Machine learning methods are powerful tools for data mining and permit to take new insights into consumer behavior [ 21 , 23 , 24 ]. In the context of bank customers’ digitalization, machine learning would allow to reveal the complex patterns driving the digitalization process as these algorithms are able to identify complex and nonobvious patterns or knowledge hidden in a database with millions of data points.

In this sense, Bajari, Nekipelov, Ryan, and Yang [ 64 ] survey a number of methods used in demand studies to conclude that machine learning techniques are both adequate and effective for this type of analyses as they reveal complex patterns. The advantages of following a machine learning approach in complex contexts, such as consumer behavior, would explain why this machine learning is gaining ground. Miguéis, Camanho, and Borges [ 65 ] use a random forest model to find hidden patterns that may be valuable for decision-making in bank marketing. Among others, a machine learning approach is employed to estimate consumer preferences for technology products [ 26 ], to examine travel choices [ 27 ] or, more generally, to model consumer response [ 21 ]. These studies as well as other related research [ 66 – 70 ] indicate that, in a context similar to ours, machine learning algorithms provide greater accuracy (compared to other standard approaches).

Instead of ex-ante selecting a default machine learning technique, we initially consider a number of machine learning techniques that have proved their value in this field (see among others [ 28 – 32 ]. The following machine learning approach is employed to examine bank customer digitalization:

• Compare a number of machine learning methods arising from many different families and areas of knowledge to select the method that achieves the best performance in terms of accuracy.
• Employ the selected algorithm to identify the main features driving the bank customer digitalization process.
• Build a set of classification trees to illustrate the sequence of consumers’ decision-making actions.
• Use a causal machine learning technique (causal forests) to estimate causal relationships in the digitalization process.

Step 1 and 2 allow us to identify the main features driving the bank customer digitalization process based on the machine learning algorithm with better predictive performance. This way we avoid biases from ex-ante self-selecting a machine learning model. Step 3 allows us to go further in the analysis of bank customers’ digitalization. By estimating a conditional inference tree for each dimension, we may explain the decision-making process. Finally, step 4 allows us to use a causal machine learning algorithm to estimate the impact of the features with the larger predictive power on the digitalization process. Unlike prior studies, this approach allows us to examine what characteristics have a predictive power in explaining the digitalization process (step 1 and 2) but also to explore the decision-making process and the potential effect of these features (causality) on going digital (steps 3 and 4).

All the empirical analyses conducted in the paper are carried out using R software. In each and every case, the models are fed with all the variables extracted from the survey (94 variables—S2 Appendix in S1 File ), excluding the outcome. This is the a common procedure in the literature when data comes from a survey specifically designed to examine digital banking (see among others [ 17 , 71 – 73 ]). Moreover, as it has been argued in the literature, if the input variables that feed the algorithms are ex-ante filtered or chosen by the researcher, the results obtained would be biased due self-selection process.

Additionally, for those machine learning techniques that require selecting some hyperparameters (e.g. number of features for each tree in the random forest algorithm or C and gamma values in the SVM), they are not arbitrarily chosen but tuned to obtain the optimal parameter values for higher accuracy. The performance of all the machine learning methods and the logit models is computed after having optimized the hyper-parameters for each and every method. In doing so, the following R packages are employed: tune, caret, tuneRF and xgboost.

4.1 Machine learning techniques

• random forest..

Random forests are an ensemble of tree predictors in which each tree depends on the values of a random vector sampled independently and with the same distribution for all trees within the forest. Because of the law of large numbers they do not tend to overfit [ 74 ]. The algorithm follows these steps:

• A forest of many trees is grown. Each tree is grown from an independent bootstrap sample derived from the data.
• For each node of the tree, m variables are independently selected at random out of all M possible variables. Then, on the selected m variables it finds the best split.
• The algorithm grows each tree to largest extent possible.
• These steps are iterated over all trees in the ensemble, and the average vote of all the trees is reported as the random forest prediction.

• Extreme Gradient Boosting.

Gradient boosting is a machine learning technique for regression and classification problems. It came out of the idea of whether a weak learner can be modified to become better. As Valiant [ 75 ] argues, the weak learning method is used several times to get a succession of hypotheses, each one refocused on the examples that the previous ones found difficult and misclassified. Then, using a training sample (y, x) the goal of the algorithm is to obtain an estimate of the function F(x) that minimizes the expected value of a loss function over the joint distribution of all the observed values.

Among the gradient boosting methods used in practice, the Extreme Gradient Boosting, is widely used as it is an efficient implementation of the gradient boosting framework. The most important factor behind the success of the extreme gradient boosting is its scalability in all scenarios. Compared to other gradient boosting methods, the extreme gradient boosting use a more regularized model formalization to control over-fitting [ 76 ].

• K-Nearest Neighbor.

The k-nearest neighbors (k-NN) algorithm is a supervised machine learning technique employing a non-parametric method [ 77 ]. This algorithm assigns points to the data, compares them using a distance function, and assigns a classification based on the labels of the nearest points. The data point which is located at the minimum distance from the test point is assumed to belong to the same class. One of the advantages of this algorithm is that it does not derive any discriminative function from the training data. That is why the k-nearest neighbor algorithm is called a lazy learner or Instance based learning. Moreover, the k-NN algorithm is robust to data that contains a lot of noise and it is able to handle data with multiple classes.

• Support Vector Machine.

A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms to solve a prediction problem for a discrete outcome using a vector of regressors, initially developed by Vapnik [ 78 ]. The algorithm constructs an optimal hyperplane that correctly classifies data points by separating the points of categories as much as possible [ 79 ]. The closest values to the classification margin are known as support vectors while the goal is to maximize the margin between the hyperplane and the support vectors [ 80 ].

Empirically, the kernel used in training the support vector machine includes the linear, radial, polynomial and sigmoid functions.

• Bayesian Networks.

Bayesian network classifiers [ 81 , 82 ] are competitive performance classifiers [ 83 ]. In this sense, a Bayesian network classifier is simply a Bayesian network applied to classification. Specifically, the prediction of the probability of some discrete (class) variable Y given some features X. Together with the well-known Naive Bayes classifier [ 84 ] more elaborate models exist taking advantage of the Bayesian network [ 85 , 86 ] such as the averaged one-dependence estimators (AODE) [ 87 ], the Chow-Liu’s algorithm for one-dependence estimators (CL-ODE) [ 81 ], the forward sequential selection and joining (FSSJ) [ 88 ], the backward sequential elimination and joining (BSEJ) [ 88 ], the Hill-climbing tree augmented naive Bayes (TAN-HC) [ 89 ] and the Hill-climbing super-parent tree augmented naive Bayes (TAN-HCSP) [ 89 ].

• Artificial Neural Networks: Extreme learning machine

Extreme learning machine (ELM) is a type of artificial neural network, called feedforward neural networks, which randomly chooses hidden nodes and analytically determines the output weights of single-hidden layer feed forward neural networks (SLFNs). The learning speed is thousands of times faster than traditional feedforward network learning algorithms like the back-propagation (BP) algorithm [ 90 ]. Moreover, compared with the conventional neural network learning algorithm it overcomes the over-fitting problem [ 91 ]. Mathematically, given a training set, an activation function and a hidden node, the algorithm follows three main steps:

• It assigns randomly input weight w i and bias b i , (i = 1,… N).
• It calculates the hidden layer output matrix
• It calculates the output weight

The activation function commonly used include the sigmoidal functions as well as the radial basis, sine, hard-limit, symmetric hard-limit, satlins, tan-sigmoid, triangular basis, rectifier linear unit and linear function.

4.2 Logit Model

Since prior literature has mainly employed discrete choice models to examine customers’ behavior, we also employ logit models to examine bank customer digitalization where Y {\ displaystyle y {*} } YY is the level of bank digitalization for each dimension of financial digitalization considered, X = ( x 1 ,…, x n ) is the set of variables and i = (1,… j) are the different categories for each dimension.

We employ an ordered logit regression for the adoption decision and the diversification of digital usage and a simple conditional logit—for the adoption of bank or non-bank payment instruments. To be consistent, the same set of variables used in the machine learning methods are employed.

4.3 Conditional inference trees

We use the characteristics and determinants with the largest discriminant power to build a decision tree for each dimension by estimating a conditional inference tree. This technique estimates a regression relationship by binary recursive partitioning in a conditional inference framework. In order to build the trees for each dimension, we follow the methodology developed by Hothorn, Hornik, & Zeileis [ 92 ] and Hothorn, Hornik, Van DeWiel, et al. [ 93 ]. The algorithm tests the global null hypothesis of independence between each of the input variables and the response and selects the input variable with the strongest association to the response. The algorithm then implements a binary split in the selected input variable and recursively repeated this process for the each of the remaining variables. The classification tree infers the sequencing of customers’ decision-making process, which helps to explain how bank customers go digital. This is particularly relevant since those classification trees do not require any linearity assumptions, which is important because many of the digitalization determinants could be nonlinearly related.

4.4 Causal machine learning

Since machine learning models are not designed to estimate causal effects, a new field of study has emerged very recently, the causal machine learning. Over the last few years, different causal machine learning algorithms have been developed, combining the advances from machine learning with the theory of causal inference [ 94 ]. The aim of these causal machine learning techniques is to complement the machine learning methods by estimating causal effects, rather than to substitute them [ 95 – 97 ]. The main advantage of causal machine learning is that it can be used after the modeling phase in order to confirm some of the relations between variables and the target/outcome. In our context, by employing a causal learning method we aim to examine the causal effect of those features with the larger predictive power on the digitalization process.

Among the recent methods developed in the causal machine learning literature, causal forest have gained relevance [ 95 – 97 ]. Knaus et al. [ 98 ] show that causal forests perform consistently well across different data generation processes and aggregation levels. The causal forest algorithm [ 96 ] is a forest-based method for treatment effect estimation that allows for a tractable asymptotic theory and valid statistical inference extending Breiman’s random forest algorithm.

Methodologically, causal forests maintain the main structure of random forests—including recursive partitioning, subsampling, and random split selection- but instead of averaging over the trees they allow to estimate heterogeneous treatment effects (causality) [ 99 ]. Then, compared to a regular decision tree, the causal tree uses a splitting rule that explicitly balances two objectives: first, finding the splits where treatment effects differ most, and second, estimating the treatment effects most accurately. In order to obtain consistent estimates of the treatment effects (the features that may have an impact on digitalization) it splits the training data into two subsamples: a splitting subsample and an estimating subsample [ 97 , 99 ]. The splitting subsample is used to perform the splits and thus grow the tree and the estimating subsample is then used to make the predictions. All observations in the estimating subsample are dropped down the previously grown tree until it falls into a terminal node. So, the prediction of the treatment effects is then given by the difference in the average outcomes between the treated and the untreated observations of the estimating subsample in the terminal nodes. Athey & Wager [ 99 ] provide a full mathematical explanation on how causal forests are built for causal inference.

Using this novel empirical methodology, we are able to examine the causal effect of those features with the larger predictive power on the digitalization process. Then, the level of digitalization is not our main interest but the impact of those features with the larger predictive power on the digitalization process. All analyses are carried out using the R package grf [ 100 ]. To run this causal algorithm, we take a conservative approach assuming that the level of digitalization of the customers can be arbitrarily correlated within a bank. Sample individuals are customers of 33 different banks. Hence, the errors are clustered at the bank-level, and we have a total of 33 clusters/banks.

5.1 Model selection

In order to select the model with the best performance, being consistent with the standard practice followed in the machine learning literature, we randomly selected 70% of the data as training data (2,104 observations) and designated the remaining data (901 observations) as test data. By doing so, we are able to determine the accuracy of the model ensuring that the algorithm is actually finding real patterns in the data and not just overfitting.

The performance of the models is compared by computing several metrics. Consistent with earlier machine learning studies (see among others [ 101 – 105 ]), we use accuracy as a measure of performance. It is defined as the number of correctly predicted data points out of all the data points. Moreover, we also compute additional standards metrics: precision (the number of correctly identified positive results divided by the number of all positive results, including those not identified correctly), recall (the number of correctly identified positive results divided by the number of all individuals that should have been identified as positive) and F1 score (the harmonic mean of the precision and recall). While recall tells us about the sensitivity of the model and precision provides information about its positive predictive value, the advantage of the F1 score is that it combines both metrics. A high F1 score is a sign of a well-performing model, even in situations where you might have highly imbalanced classes. Finally, for those dimensions with three or more classes (multi-classes) we also compute the Macro F1 score which is the averaged F1.

For the sake of brevity, Table 2 reports just the results for the best model identified per machine learning method after having optimized the hyper-parameters for each and every method. The forecasting accuracy for those cases in which several models (using several kernels and activation functions) are estimated could be found in S4 Appendix in S1 File reports. Moreover, to save space in Table 2 , we just report the precision, recall and F1 score for the class which is more frequent among the survey participants (see Fig 3 ). Overall, Table 2 shows that the random forest algorithm provides the highest level of accuracy for all the dimensions considered. Random forests accurately predict 88.41% of bank customers’ online banking adoption profile, 70.11% of the diversity of digital use of online banking, 70.01% of the diversity of digital use of mobile banking, 85% (74.89%) of debit (credit) card adoption, and 76.14% of non-bank payment instruments adoption. The second best method is the extreme gradient boosting algorithm, which also present a high percentage of accuracy.

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Regarding the F1 and macro F1 scores, the random forest model also seems to provide the highest values. For example, the macro F1 score is 91.41% for the adoption of online banking. The higher F1 and macro F1 scores of the random forest, together with the highest predicted accuracy, suggest that the random forest is the machine learning method that exhibits the best performance. Furthermore, we also observe that the performance obtained using most of the machine learning techniques, but specially the random forest, outperforms the standard logit and ordered logit models.

The higher predicted accuracy of the random forest algorithm is in line with prior studies. Bajari, Nekipelov, Ryan, & Yang [ 64 ] compare several methods and based on the out-of-sample prediction error shows that the random forest is the most accurate. Similarly, Fernández-Delgado et al. [ 102 ] evaluate 179 machine learning algorithms arising from 17 families to conclude that random forests provide the best results in terms of predicted accuracy. Consequently, since in our case the random forest is the most accurate algorithm, this algorithm is employed in order to identify the main features driving the bank customers’ digitalization process.

5.2 Validity

Finally, in order to check the stability of the accuracy of the results, we employ two cross validation methods: the k-fold cross-validation and the repeated K-fold cross-validation. In doing so, the dataset is split into 10 groups (k = 10), since this value has been shown empirically to yield test error rate estimates that suffer neither from excessively high bias nor from very high variance [ 106 , 107 ]. In case of the repeated K-fold cross-validation, the data is split into 10-folds, repeating the process five times. The results reported in Table 3 confirm the validity of the models employed.

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For replicability purposes -and given that the random forest is the selected algorithm- Table 3 reports the optimal hyperparameters of this algorithm for each dimension of financial digitalization. The Out-of-bag (OOB) error remains stable if more than a thousand trees are built. Then, since the improvement is mostly insignificant, the number of trees is set to 1,000. Moreover, since taller trees allow the model to learn very specific relationships between the features splitting the nodes and our data set [ 108 ], it is important to limit the depth of the tree in order to avoid overfitting. In doing so, we allow up to 20 nodes from the root down to the furthest leaf node.

5.3 Features of the digitalization of bank customers

Employing the random forest algorithm [ 74 ] we identify the features with the largest power in predicting bank customers’ digitalization reporting the relative statistical importance of each factor in the classification of individuals by their digital profiles (Figs 4 to 9 ). The determinants and characteristics are plotted on the y -axis ranked by their absolute level of importance while their relative importance is charted on the x -axis. The mean decrease in accuracy reflects the mean loss in accuracy when each specific variable is excluded from the regression algorithm. Therefore, the determinants and characteristics with the greater mean decrease in accuracy are the most relevant for the classification of bank customers. Additionally, the mean decrease in Gini is a measure of how each feature contributes to the homogeneity between the decision trees used in the resulting random forest. Furthermore, besides reporting the mean decrease in accuracy and the mean decrease in Gini for each variable, we employ the variable selection procedure MDAMDG proposed by Han et al. [ 109 ]. It consists of 1) running the random forest algorithm and returns the mean decrease in accuracy and the mean decrease in Gini of each variable 2) ranking every variable using the mean decrease in accuracy and the mean decrease in Gini, respectively, 3) scoring each variable 4) computing the total score of each variable 5) reordering them by the total score. While, as abovementioned, all the variables extracted for the survey (S2 Appendix in S1 File ) are used to feed the algorithm as input features, for the sake of brevity Figs 4 to 9 report only the top 20 features by their relative importance. These Figures provide the rank of the variables based on the mean decrease in accuracy, mean decrease in Gini and the total score [ 109 ].

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Figs 4 to 9 report the plots showing the relative statistical importance of each feature in the classification. The left-hand side graph shows the Top 20 features by Mean Decrease in Accuracy. The centered graph shows the Top 20 features by Mean Decrease in Gini. The righ-hand side graph shows the Top 20 features by Total Score [ 109 ].

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The machine learning algorithm reveals that the first-order factors determining the adoption of digital banking are online check balance (whether account balances are checked online), number of online bank accounts , online transfers (whether the customer has made an online bank transfer in the last three months) and consciousness (degree to whether the customer is conscious that an online access is available).

These results suggest that the relevant factors in going digital are those related to customers becoming accustomed to the online channels by checking their bank account balances or transferring money and being aware that these activities can be conducted online. Bank customers’ perceptions of security, cost, and ease of use of banking services were found to be secondary factors in going digital. As in other industries, consumers tend to go through several stages of adoption: awareness, consideration, and choice. Our results confirm the significance of awareness in the multistage process of going digital.

• Diversity of Digital Use: Online and Mobile Banking.

Figs 5 and 6 show the baseline random forest results in terms of the diversification of online and mobile banking services, respectively. The number of online bank accounts , consciousness (being aware of the possibility of having access to online services), safety of online banking (how customers perceive the level of security of online banking) and online banking communication (whether customers have used online services or e-mail as their communication method with their bank) are the features with the largest influence on diversifying the use of online banking.

Considering both the adoption and diversification of digital use, we argue that the digitalization process originates from the customers’ need to check their bank account balances and transfer money. However, being aware of the possibility of accessing financial services through online banking and the perceived safety of operating online are the main factors to diversify the use of online banking services. Furthermore, the digitalization of the communication channel between customers and banks also fosters the diversification of customers’ online activities.

Regarding the diversification of the use of mobile banking, we find that the factors with the greatest predictive power are the number of online bank accounts , safety mobile banking , consciousness and transferring money via mobile .

Overall, the algorithm reveals that online and mobile diversification are driven by common features: consciousness of the possibilities offered by digital banking, the perceived level of security of the channel used, and the number of digital bank accounts available. However, it is worth noting that transferring money was a distinct factor in determining the diversification of mobile banking. It seems that money transferring via mobile may become the gateway to other digital financial activities. This finding partially explains the importance of the irruption of FinTech companies in the payment sector compared to other financial services.

• Use of Banks’ Payment Instruments: Debit and Credit Cards.

The main factors that influence the use of debit and credit cards (see Figs 7 and 8 ) are the perceived cost , safety , acceptance and convenience of these payment instruments. Unlike the adoption and penetration of online and mobile banking, the use of debit and credit cards seems to be dominated by bank customers’ perceptions of cards’ cost, safety, and acceptance. It is interesting to see that merchants’ acceptance of debit and credit cards as payment instruments is relevant since it determines their utility, which could explain why bank customers are concerned about ensuring their acceptance before adopting them as regular payment instruments. This result suggests that the technological changes linked to cards (CVC code, EMV chips, contactless technology, multi-factor authentication) have been evolving and affect customers’ perceptions of safety and convenience.

Fig 9 illustrates that the adoption of non-bank payment methods is driven by mobile payment app (whether customers’ use of mobile apps to make payments), frequency and degree of online banking , online banking complaint ( whether customers’ use online channels to lodge a complaint with the bank) and being active on social media ( Twitter and/or Facebook user) . These findings reveal that the prior profile as digital bank customer (frequency and scope using online banking) as well as being already using payment apps determine the use of alternative payment methods. Moreover, the relevance of using online channel to complain may reveal that a certain level of dissatisfaction with the bank may lead bank customers to adopt non-bank means of payment.

Overall, while prior theories and studies have given prominence to the technological components of the service and to consumers’ perceptions to explain the digital jump (see among others [ 10 , 12 , 13 , 34 , 110 ]), our approach reveals that customers go digital first for information-based needs and, later, to undertake transactional services. Customers’ perceptions also play a role but only to explain the scope of the digitalization (being a diversified digital customer). However, customers’ perceptions (in particular, safety and cost), are particularly related to the use of bank payment methods (credit and debit cards). Moreover, the adoption of non-bank payments seems to be driven by the prior adoption and usage of online and mobile banking services.

• Robustness and stability over subsamples.

Finally, for robustness purposes we also employ the second best algorithm in terms of accuracy ( Table 2 ), the extreme gradient boosting, to identify the features with the largest predictive power. The figures in S5 Appendix in S1 File plot the most important features that predict bank customer digitalization based on this algorithm. The relative importance of each feature is computed using the contribution of the corresponding feature for each tree in the model (Gain). Overall, they show that the features with the largest predictive power according to the random forest algorithm are also identified as the most important by the extreme gradient boosting algorithm. Since both methods coincide on the main customers’ features predicting the level of digitalization, this adds robustness to the ability of machine learning methods to reveal the characteristics that drive customers’ digitalization.

Furthermore, we also aim to ensure that when feeding different data to the algorithm the predicted accuracy was stable. In doing so, we employ different subsamples based on socio-economics characteristics—gender, age, and habitat—to go through the machine learning process in order to show the robustness in terms of accuracy. Young people are those between 18 and 34, while old people are over 55 years old. The rural areas category includes people living in municipalities with less than 10,000 inhabitants while the urban category includes those living in cities with more than 200,000 inhabitants.

Fig 10 shows the accuracy across the different subsamples- based on gender, age, and habitat—that feed the model. As it could be observed the performance of the algorithm across these three subsamples remains similar to the whole performance when the algorithm is fed with the entire dataset. This result shows that the performance of the algorithm when examining the digitalization of bank customers is stable, which means, that it is not dependent on the sample subset used to feed the model. This is relevant since it reveals that the machine learning algorithm does not overfit bank customers’ digitalization for a particular profile of customers.

Fig 10 plots the predicted accuracy of the random forest algorithm across the different subsamples: based on gender (male vs female), age (young vs old) and habitat (rural vs urban areas).

https://doi.org/10.1371/journal.pone.0240362.g010

5.4 Bank customers’ digitization trees

The characteristics and determinants with the largest discriminant power are employed to estimate a conditional inference tree for each dimension. This technique estimates a regression relationship by binary recursive partitioning in a conditional inference framework. As already mentioned, these trees are built following the methodology developed by Hothorn, Hornik, & Zeileis [ 92 ] and Hothorn, Hornik, Van DeWiel, et al. [ 93 ]. In doing so, those variables with the largest relative importance based on Han et al. [ 109 ]’s total score, which accounts for mean decrease in accuracy and mean decrease in Gini, are selected (those variables are colored in a different color in Figs 4 to 9 ).

• Tree: Adoption of Digital Banking.

Fig 11 shows that although the range of services available online is wide, the adoption of online banking seems to emerge from customers checking their account balances. It is only after customers check their account balances that they move into transferring money online. Bank customers who do not perform either of these activities are classified as occasional or low frequency users (Node 5). Comparing those individuals who only check their account balances (Node 10) with those who only transfer money (Nodes 7 and 8), checking account balances appears to be more decisive. Furthermore, when customers begin to make transactions and are largely aware of the online possibilities, they become frequent users (Nodes 14 and 15).

https://doi.org/10.1371/journal.pone.0240362.g011

An overview of the random models and the classification trees suggests that the main channel by which bank customers become frequent users of online banking services is by their need to check their account balances and, subsequently, transfer money. Consciousness of the availability of online possibilities is also important for the customer to become a frequent digital bank user. Furthermore, the perceived safety of online banking services is not a primary determinant in becoming a frequent user. As we show in the next subsection, safety only becomes influential when customers consider conducting a wide range of transactions online.

• Tree: Diversity of Digital Banking Use.

Fig 12 reveals the relevance of the perceived security of online banking in influencing customers’ use of online financial services (Branch 2). Customers who do not consider online banking safe are not likely to become diversified users of online services (Nodes 14–21). Together with safety, customers’ use of digital channels for information purposes and their awareness of the range of online services are key determinants of the diversification of digital services demanded (Node 11). However, consciousness does not compensate for the perceived lack of safety. At most, being conscious make customers switch from non-users to incipient users (Nodes 17–21). Overall, the results suggest that while being a regular online banking user is driven by customers’ needs (e.g., checking account balances and transferring money) as well as by having a certain level of consciousness about the online possibilities, becoming a diversified digital user depends largely on the perceived level of safety.

https://doi.org/10.1371/journal.pone.0240362.g012

Fig 13 plots the classification tree for the diversity of digital use of mobile banking. The results suggest that the diversity of online and mobile banking use are driven by similar factors. The perceived level of safety of mobile banking is also relevant (Node 7). It is unlikely to find diversified users not transferring money with their phones even if they perceive mobile banking as not safe (Node 5).

https://doi.org/10.1371/journal.pone.0240362.g013

• Tree: Adoption of Bank Payment Instruments

Figs 14 and 15 plot the classification trees for debit and credit card adoption, respectively. Both trees demonstrate that safety and cost are the main drivers of adoption. Debit card users can be classified into users who consider debit cards safe, accepted, but not very convenient regardless of their cost (Node 11), and users who consider the method convenient, costless, and safe (Nodes 24 and 26). It can then be argued that a costless perception could compensate for a lack of perceived convenience.

https://doi.org/10.1371/journal.pone.0240362.g014

https://doi.org/10.1371/journal.pone.0240362.g015

In the case of credit cards, customers who perceive credit cards as unsafe regardless of their cost are less likely to use them (Nodes 14–19). Similar to debit cards, users who perceive credit cards as safe and relatively costless make up the majority of the credit card users (Node 12). The probability of adoption drops to 12% if the credit cards are considered costly.

• Tree: Use of Non-Bank Payment Instruments.

Fig 16 reveals that the adoption of non-bank payment methods occurs when customers are frequent and diversified digital banking users. For occasional and incipient online users, the likelihood of using non-bank payment instruments is quite small. However, as the frequency and diversity of use increases, being active on social media and making mobile payments increases the likelihood that customers would use non-bank payment channels. However, it is worth noting that frequent online users do not use non-bank payment methods if they are just incipient users (Node 23); it is necessary for customers to undertake several digital financial activities to jump into non-bank payments. Similarly, digital banking users who do not have frequent online access are not regular adopters of non-bank payment methods (Nodes 7, 16, 17, and 28).

Figs 11 to 16 plot the decision trees of bank customer digitalization by estimating a conditional inference tree using those features having the largest predictive power according to the random forest algorithm.

https://doi.org/10.1371/journal.pone.0240362.g016

• Robustness: Bayesian network.

We also estimate a Bayesian network based on the hill-climbing algorithm, using the subset of features with the largest discriminant power. Bayesian networks could be defined as graphical models of the relationships among a set of variables. These networks use Bayesian inference for probability computations with the aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph [ 111 ]. All the graphs are shown in S6 Appendix in S1 File .

Regarding the adoption of online banking, the Bayesian network reveals that checking account balances online and making online transfers are parents of adopting digital banking. Interestingly, the network also reveals that checking account balances online is additionally a parent of making online transfers. This finding suggests that while both kind of activities play a role in the adoption of digital banking services, informational activities (checking account balance) may also foster customers to conduct transactional activities (online transfers). In a way, this result complements our finding that the adoption of digital banking services begins with information-based services (e.g., checking account balance), and is then followed by transactional services (e.g., online/mobile money transfer). Moreover, the Bayesian networks also reveal that effect of being conscious of the range of services that could be conducted online is related to the number of online bank accounts that a customer hold.

Regarding the use of cards as payment methods, it could be observed that the perceived cost of debit and credit cards is a parent of their use. In case of credit cards, the perceived safety is a parent of paying regularly with them. However, for debit cards safety is mediated by customers’ perceptions about cost and convenience. This finding would suggest that while the perceived cost has a direct relationship for both type of cards, it does not seem to be the same in case of the perceived safety. As for the adoption of non-bank payment methods, the network shows that being a diversified digital banking user has a direct relationship on paying with non-bank payment instruments. Additionally, being a Facebook user is a common parent of using non-bank payment instruments, together with being a diversified digital banking user, indicates the presence of interactions between social media and the degree of use online banking in paying with non-bank payment methods.

5.5 Causal effects on bank customers’ digitalization: Causal forests

Fig 17 shows the average treatment effect estimations–average differences in the level of digitalization—for those variables identified with the largest predictive power by the random forest. Applying this causal forest algorithm, since the estimated average treatment effects are positive and significantly different from zero, it could be argued that these features drive customers’ levels of digitalization. Then, causal forests reveal that for each of the dimensions examined those features with the largest predictive power also have a large positive effect on the digitalization process. Interestingly, the estimation of the average treatment effects also reveals that checking online balances had the largest effect on adopting online banking while making money transfers with one’s smartphone seems to be relatively more important in order to become a diversified mobile banking customer. Moreover, regarding the use of bank payment methods, we observe that the perception of safety has the largest impact on using credit cards while the perception of cost and convenience have the largest impact on paying with debit cards. This latter result was also highlighted by the Bayesian networks. Finally, regarding non-bank payment methods, the largest effects on adoption come from being a frequent and diversified digital bank customer.

Fig 17 shows the average treatment effect estimations (ATEs) computed using the causal forest algorithm. The ATEs are shown for each dimension of bamk customers’ digitalization and for those variables with the largest predictive power according to the random forest.

https://doi.org/10.1371/journal.pone.0240362.g017

These results confirm that the digitalization of bank customers is largely affected by informational (checking account balances) and transactional activities (online/mobile transfer) while the consciousness about the range of the online services available and the safety of perception have a positive impact on diversifying the use of digital channels.

6. Discussion

6.1 supply side explanations.

While the variable capturing each customer’s bank does not rank among those with the largest importance, we aim to confirm that the digitalization process is primarily driven by consumers’ characteristics and not by their bank’s characteristics. We then re-run the machine learning algorithm for different samples of consumers aggregated by their main bank characteristics to determine whether or not the predictors and decision trees obtained are qualitatively similar to those obtained in the baseline random forests regressions.

Firstly, since bank size (market power) may play a role in digitalizing customers, we re-run separate regressions for customers of large banks with the largest customer bases in Spain: Santander, BBVA, and CaixaBank. Furthermore, we also conduct a within-bank comparison. This type of analysis helps to ensure that digitalization is not mainly driven by supply-side factors since all the consumers from each subsample would have the same supply level of digitalization. In addition, since the closure of bank branches may force some bank customers to go digital, we also check whether or not bank closures drive digitalization. In doing so, separate regressions are estimated for those customers whose main bank closed at least one branch in their province.

Fig 18 reports the relative importance—measured by mean decrease in accuracy—of those variables with the largest predictive power for the adoption of online banking. The full results for the rest of the dimensions are not reported for the sake of simplicity. Checking balances, transferring money, and being conscious of online banking and the number of one’s online accounts are consistently reported as the variables with the largest predictive power across different subsamples. Hence there are not significant differences in the predictive power of the main drivers of adopting of online banking by supply-side factors (banks’ characteristics) nor by the closure of b branches. Similarly, no qualitative differences in the relative importance of the predictors and decision trees obtained are found for other dimensions of digitalization.

Fig 18 reports the relative importance—measured by mean decrease in accuracy—of those variables with the largest predictive power for the adoption of online banking by banks’ characteristics: size (large banks’ customers—Santander, BBVA, and CaixaBank—vs other banks’ customers) and branch closure (customers whose main bank closed at least one branch in their province vs customers whose main bank has not closed any branch in their province). The bottom panel shows the predicted accuracy.

https://doi.org/10.1371/journal.pone.0240362.g018

Fig 19 shows the main characteristics of the survey participants by degree of digitalization, degree of financial digitalization, perceptions on mobile and online banking and the use of non-bank services and social networks. Results are presented by gender, age, and employment situation. Each cell represents the percentage of people over the total number of people belong to this category.

https://doi.org/10.1371/journal.pone.0240362.g019

These results have interesting business implications as they suggest that the digitalization process is mainly driven by consumer characteristics. This would imply an opportunity for banks to segment customers in order to get on board and retain digital customers. Moreover, the limited impact of the closure of bank branches on digitalization suggests that the digitalization process does not emerge because customers are forced to use digital banking when there are no physical branches to reach out. It seems that customers go digital by their own needs and perceptions not because there are fewer physical branches close to where they live.

6.2 Implications, limitations, and scope for future research

Facing digital transformation successfully is among banks’ top priorities. Digital banking is likely to soon become the main channel through which customers interact with their banks. Understanding how customers face the digital jump would help banks to retain their current customers and attract more digital users by, for example, improving those functionalities related to information and transaction-based services. However, since bank customers’ digitalization seems to be explained by the needs and perceptions of consumers, bank marketing strategies should have these dimensions into account. Similarly, the results of the study will help banks understand how their customers could potentially adopt digital payment methods offered by new competitors such as BigTech and FinTech firms.

Just like any other research work, our study has certain limitations. Despite employing is a representative testing ground for research on banking digitalization, it would be ideal to know the digitalization timing of each bank customer in order to provide further insights into the temporal structure of the digitalization process. Our findings are found to be applicable to countries with deep internet penetration, a highly banked population, and a growing use of electronic banking among consumers (e.g. Germany, France, Sweden, United Kingdom, Finland, Italy, United States, Japan, Turkey or Australia). Therefore, it would be interesting to explore whether emerging economies may face the same banking digitalization process documented in this study. It should also be acknowledged that examining bank customers’ digitalization using questionnaire data may involve some biases. In any event, we use a questionnaire that follows the structure of a well-established survey, the Survey of Consumer Payment Choice (SCPC).

Despite these limitations, we believe that the results of this study are valuable for other researchers and practitioners interested in understanding how people go digital. Overall, our study confirms the need to conduct research that covers the entire digitalization process rather than focusing on a single dimension. In addition, our research finds that the application of machine learning techniques on consumer research provides more accurate results that improve the understanding of complex topics.

7. Conclusion

Modern societies are undergoing a rapid digital transformation. A sizeable part of this change is related to the demand for financial services. The use of electronic devices such as smartphones, laptops, and tablets to conduct many financial activities has risen sharply. While the banking industry is aware of this transformation, adjusting the supply side depends on related changes in demand.

Understanding the process of financial digitalization is valuable for the banking industry to design strategies that bring on board and retain digital users. It would help banks to obtain information on how they can face competition from new providers of financial services (BigTech and FinTech). Additionally, policymakers may use this knowledge to implement more efficient policies to promote financial digitalization and enhance financial inclusion and literacy. To reach this end, this paper employs a machine learning approach to reveal the patterns driving the digitalization process and to offer a multi-dimensional comprehensive picture of the process by which bank customers become digitalized. While most previous studies have discussed the determinants of certain adoption decisions, we outline the sequence of steps that customers follow to adopt digital financial services and become diversified users. Several dimensions are considered: adoption of online banking, diversification of the use of online services, and the choice of bank versus non-bank payment instruments. Our approach benefits from the advantages of machine learning techniques, including the capacity to identify complex and nonobvious patterns or knowledge hidden in a database with millions of data points. These techniques are applied to an in-depth consumer survey specifically designed for the purpose of this study. Furthermore, we run causal forest models to examine the causal relationships on the digitalization process.

The empirical results suggest that the digitalization process is originated from customers’ need to gain information about basic aspects of their banking accounts (e.g., checking their account balances), and this facilitates a transition to transactional services (e.g., transferring money). We also find that once the initial adoption has taken place, the diversification of online and mobile services adopted by the customers becomes larger when they are conscious of the range of possibilities provided by the bank and when they perceive those options as safe. Taken together, these results suggest that while customers’ perceptions are important on using digital channels, in banking the adoption is primarily driven by information-based services. Furthermore, we show that the adoption of non-bank payment instruments (e.g., PayPal and Amazon) happens when consumers are already diversified digital bank customers. Users of non-bank payment instruments seem to have previously reached a substantial degree of banking digitalization. This suggests a certain degree of complementarity between bank and non-bank digital services.

The causal machine algorithm reveals that among the information-based activities, checking online balances has the largest effect on adopting online banking. Similarly, making money transfers with a smartphone is the transactional-based activity that is relatively more important to define a diversified mobile banking customer. These results are confirmed by Bayesian networks, which also indicate that the relevance of interactions between social media and the degree of use online banking and non-bank payment methods. Importantly, we find that the digitalization process is not mainly driven by bank characteristics. We report a limited impact of the closure of bank branches on digitalization, which suggests that customers go digital by their own needs and perceptions not because there are fewer physical branches close to where they live (a diminishing role of geographic distance in banking).

These findings are relevant to better understand the digital transformation of consumers. While prior theories and studies have given prominence to the technological components of the service and to consumers’ perceptions to explain the digital jump, our machine learning approach reveals that customers go digital first for information-based needs and, later, to undertake transactional services.

Overall, the findings of the study suggest that financial providers could benefit from the digitalization phenomenon by offering services that better match customers’ needs. In this sense, segmenting customers using similar techniques and data, would make possible to offer them more personalized digital services. Moreover, linking payments experiences to social media interactions could also be used to foster the adoption of digital payments. Finally, our findings could be used by policymakers to improve the communication and social awareness of the range of online services available, as part of the policies and official strategies to promote financial digitalization.

Supporting information

https://doi.org/10.1371/journal.pone.0240362.s001

Acknowledgments

The authors are very grateful for comments from Sumit Agarwal, Charles Kahn, Thorsten Beck, Dalida Kadyrzhanova, Glenn Harrison, Rohan Ganduri, Meryem Duygun, Ilaf Elard, Francisco Rivadeneyra, Michael King, Stefan Güldenberg, John Holland, Robert E. Wright, Kamlesh Kumar, and Paola Bognini as well as conference participants at the 15 th International Conference WEAI-IBEFA in Tokyo 2019; the EURAM 2019 Conference in Lisbon; the IFABS 2018 Chile Conference; the 2018 Workshop on Credit Card Lending and Payments, Developments, and Innovations at the Federal Reserve Bank of Philadelphia; the 2018 Conference on Financial Stability Implications of New Technology at the Federal Reserve Bank of Atlanta; the 3 rd International Workshop on the Internet for Financial Collective Awareness & Intelligence at Glasgow University; the 2018 International Workshop on Financial System Architecture and Stability (IWFSAS) at Cass Business School; and the 1 st Banca March Workshop on Contemporary Issues in Banking.

• 1. OECD (2017), OECD Digital Economy Outlook 2017, OECD Publishing, Paris.
• View Article
• Google Scholar
• PubMed/NCBI
• 24. Witten I. H., Frank E., & Hall M. A. (2019). Data Mining: Practical Machine Learning Tools and Techniques (Third Edition). In Morgan Kaufmann (The Morgan, Vol. 53, Issue 9). Morgan Kaufmann.
• 25. Bishop C.M. (2006). Pattern Recognition and Machine Learning, Information Science and Statistics (Vol.53). Springer-Verlag New York.
• 33. Federal Reserve Bank of Atlanta. (n.d.). https://www.frbatlanta.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice . Survey of Consumer Payment Choice.
• 35. Fishbein M., & Ajzen I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. In Reading, MA: Addison-Wesley.
• 38. Rogers E. M. (1962). Diffusion of innovations. In New York, Free Press of Glencoe.
• 75. Valiant L. (2013). Probably Approximately Correct : Nature’s Algorithms for Learning and Prospering in a Complex World . Basic Books, Inc.
• 76. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. ArXiv:1603.02754v3 , 1–13.
• 78. Vapnik V. N. (1995). The Nature of Statistical Learning Theory . Springer-Verlag.
• 85. Koller D., & Friedman N. (2009). Probabilistic Graphical models . Principles and Techniques . The MIT Press.
• 86. Pearl J. (1998). Probabilistic Reasoning in Intelligent Systems . Morgan Kaufmann.
• 108. Shu X. (2020). Knowledge Discovery in the Social Sciences: A Data Mining Approach. In University of California Press.
• 109. Han, H., Guo, X., & Yu, H. (2016). Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS , 219–224.
• 111. Holmes D. E., & Jain L. C. (2008). Innovation in Bayesian Networks . Theory and Applications . Springer.

Digital banking transformation: Accelerating into 2024

“The acceleration of digital transformation is one of those things that just can’t be stopped,” says Viren Patel, Industry Strategist for Financial Services and Insurance at Workday. 

A process many banks have either continued or started to undertake in 2023, as far as Kevin Pettet, COO of Banking Solutions at Bottomline is concerned, it’s not a question of “how much digital transformation will accelerate in 2024, but what form it will take”.

Below, we ask a host of industry experts – including Patel and Pettet – what trends they expect to see as the banking world continues its transformative charge into 2024, with comments from: 

• Prakash Pattni, Global MD for Financial Services Digital Transformation at IBM
• Kevin Pettet, Chief Operating Officer of Banking Solutions at Bottomline
• Kshitij Jain, SVP, Head of UK/Europe Analytics and Global Chief Strategy Officer for EXL Analytics
• Viren Patel, Industry Strategist for Financial Services and Insurance at Workday
• Wendy Li, SVP of Emerging Technologies at Marqeta

1. What prevalent digital banking transformations are you expecting in 2024?

Pattni: 

The financial and banking sector has undergone a significant digital transformation in 2023, driven by a focus on ESG commitments, macroeconomic uncertainty, a renewed focus on risk management and the rapid spread of automation. This transformation is expected to continue in 2024, with technology playing a leading role.

While we’re at the beginning of the generative AI (GenAI) journey, the banking sector is further along with decentralised finance (DeFi). We’re seeing much more focus on central bank digital currencies (CBDCs) as they gain traction and central banks exploring the possibility of issuing their own regulated digital currencies. There have already been tests of a digital Euro in Spain. 

This is shifting the focus away from cryptocurrencies and towards DeFi becoming mainstream, so we’ll move into the sphere of regulated entities where more people can use it online with established banks. 

This will lead to the creation of more retail solutions but also simplify cross-border payments. Fintech companies will explore ways to make it part of their day-to-day assets and digital currencies will see broader adoption due to legitimacy, further embedding them into society. 

The momentum for regulated digital currencies will continue to grow in 2024 as banks look to incorporate this into their architecture, helping this to become the new normal. However, there are still technical challenges to overcome to ensure blockchain-based solutions can meet the needs of banks and customers such as real-time payment processing.

Pettet: 

In my view, it’s not how much digital transformation will accelerate in 2024, but what form it will take. Digital transformation accelerated during COVID-19 as banks and businesses were forced to define new operating models in order to continue to operate and compete. At this point, digital transformation is the norm and will continue to be so. 

This point is evident as major banks reduce their branch footprints while increasing their investment in digital channels. In doing so, they’ve placed a heavy focus on differentiation by creating unique experiences and new value propositions for their customers.  

This will continue, but digital transformation will also accelerate with investments focused on reducing operating costs in 2024 and beyond, driven largely by the changing interest rate environment.  

The era of cheap deposits to fund lending operations is ending and banks need to find alternate revenue streams while reducing costs, which will drive where they focus their digital investment.  

Areas such as processing lending applications, customer onboarding, migration of cheque payments to electronic payments and customer support represent some of the areas where banks can focus digital investment to reduce operating costs in parallel to their investments in creating unique experiences and driving innovation.

Throughout 2024, digital transformation will continue to change how credit, in particular, is applied for and assigned to consumers. 

Traditionally, there are high barriers of entry to accessing credit and lengthy approval processes and, even once an individual is approved, banks often issue credit cards with one-size-fits-all terms, rather than providing a tailored product based on that individual’s specific needs.

Recent digital innovations in embedded finance mean that shorter-term credit offerings such as buy now, pay later (BNPL) can give consumers immediate access to credit at checkout. 

Additionally, merchants and retailers can now issue their own embedded cards directly to customers as part of a credit card programme. 

These credit cards can be seamlessly integrated into customer shopping experiences, harnessing huge volumes of data to offer consumers contextualised, personalised rewards and an intuitive customer experience. 

2. Last year saw AI come on leaps and bounds. How will this impact financial services further in 2024 and what can employees expect?

AI has been leveraged for different purposes by finance institutions in 2023; some have

mostly relied on AI to support the automation of back-office processes, whereas others have deployed AI to streamline and improve the customer experience. 

For those who have embraced generative AI, the goal may have been to not just streamline customer experience (CX), but enable hyper-personalisation.

For employees within banks and financial institutions, the adoption of AI may be met with some concern or distrust. Some will be asking, “will my job be replaced by AI?”. 

The honest answer is that AI does have great potential to remove or replace manual tasks and processes, yet the role of the ‘human in the loop’ will become more significant as AI adoption accelerates. 

The human-centred soft skills of empathy, and the ability to holistically appraise and provide feedback on AI outcomes to promote ongoing improvements, will be highly sought after in the year ahead.

For those working within customer contact functions, AI has the power to make their lives significantly easier. It has the capability to summarise and contextualise information about a customer, from across a multitude of structured and unstructured data sources, providing a more complete and joined-up view. 

GenAI in particular can be trained to identify which phrases or solutions lead to good customer service outcomes and serve those as prompts to agents. This leads to higher resolution rates and improved customer outcomes.

It also helps upskill new agents more rapidly – giving them access to knowledge in context, rather than through formal training programmes.

Patel:  

AI has upended financial services already and will continue to do so in 2024. For example, in accounting, AI is already being used to detect anomalies and provide intelligent recommendations. This helps drive efficiencies and scale, while freeing team members up to focus on more strategic initiatives.  

AI will also enhance and streamline risk assessments, especially regarding credit assessments and confident lending. AI-driven strategies will also empower and facilitate automatic trading. 

It is already enabling traders to create custom strategies that allow AI systems to trade on their behalf, which helps them respond faster to market trends. 

On the customer side, 24/7 customer service bots, trained accurately on good data, will be available to support customers at all hours of the day. 

Key to all of this, though, is ensuring responsible use of AI. The training of AI must be transparent and use of it must comply with regulatory requirements. 

However, our research shows that many businesses are facing a major AI skills gap, with 71% of finance functions hoping to increase their data scientist headcount to meet their objectives by 2030. As organisations continue to place a greater focus on AI, it’s critical that business leaders can trust their AI.  

GenAI is set to play an even more prominent role in the future of financial services, and 2024 will likely see accelerated innovation and integration of the technology. 

The emergence of so-called ‘predictive credit cards’ will use AI to anticipate consumer spending needs based on criteria such as past behaviour and how spending changes throughout the year, dynamically adjusting credit limits and offering tailored rewards accordingly.  

The technology can also be used for financial fraud detection, where it essentially acts as a co-pilot for security professionals. GenAI can almost instantaneously identify unusual patterns and then investigate the context by analysing sequential data and determining if fraudulent activity has occurred. 

This limits the need for lengthy manual checks, minimises the false positive alerts that waste time and, critically, makes digital banking platforms safer. As a result, security professionals can expect to be using AI tools daily, making their role easier and enabling time that used to be spent on data checks and analysis to be spent elsewhere. 

Additionally, engineers can expect AI to assist in generating tests, reviewing code and solving errors, which will make their roles more efficient and allow them to focus on higher-level tasks. This integration of GenAI is expected to delight employees and unlock their creative potential, leading to increased job satisfaction and innovation.

Consumers are also likely to become comfortable using AI, for example, to access a better overview of their personal finances by using tools to review transactions and even benchmark themselves against other cardholders in similar demographics. 

For example, consumers could ask a GenAI bot, ‘how much debt do I have compared to your available credit limits?’, or ‘what’s the best way to use my rewards points based on my recent purchases?’, and expect a personalised response instantly.

3. How much more will open banking become entrenched in finance?

Open banking has primarily been industry-driven in the US vs. regulatory-driven as seen in other countries. It will continue to progress in the US but on a macro level at a cautious pace. 

However, the Consumer Financial Protection Bureau recently announced its Personal Financial Data Rights rule as the basis for establishing compliance standards and the framework for a more open ecosystem. They expect to finalise this rule in 2024 and quickly follow with implementation in an effort to accelerate progress.  

In the meantime, the larger banks will lead as the basis for their digital transformation efforts and, in doing so, create new business models and value propositions, while smaller banks will take more targeted or even wait-and-see approaches.  

Regardless, most banks will focus digital investment in preparing for open banking through developing open application programming interfaces (APIs) to enable secure data exchange between institutions, beginning to identify value propositions and customer segments to target and progressing thinking around privacy and security processes to protect customer data.  

Ultimately, banks are viewed favourably by their customers who believe they will safeguard their money and data. As a result, they are well positioned to expand their offerings beyond traditional banking services as open banking progresses in the US.

I think 2024 may well be the year that open banking becomes a mainstay in the global financial landscape. As a technology, open banking enables a bank to safely share financial information with third-party financial institutions, through so-called open banking APIs.

Third parties like expense tracking companies, money lending firms and others use this data to develop wider product offerings. Ultimately, what this data exchange allows modern financial services providers to do is offer personalised and client-focused services. 

And like last year, 2024 will be another year where client-centric product and service development are paramount. My view is open banking will come to underpin much of the financial services sector’s future development, especially in terms of ecosystems and connected finance. 

And it is imperative, for any financial institution that wants to excel – let alone stay competitive – that they incorporate it from the ground up. 

4. What other digital transformation trends are you expecting to see in 2024?

Jain:  

One of the big barriers to digital transformation is legacy technology which also has a downstream impact, creating bad or incomplete data that further leads to sub-optimal user experience.

Generative AI has the potential to impact the economic case for legacy migration. GenAI (with humans in the loop) has the ability to translate legacy code base, create code documentation, testing automation, variable lineage etc. which contributes immensely to platform modernisation and, therefore, more effective and more pervasive digital transformation.

Pattni:  

Wearables and biometrics have been another recurring trend we have seen in recent years, and the rise of the Internet of Things (IoT) will continue to lead to new and innovative ways to make payments.

Payment friction as a means to reduce fraud remains a key challenge for banks and fintechs, and wearable technology will help to reduce this.

Privacy and security concerns are also associated with linking payment technologies to identity. Banks currently use standard encryption methods to protect this sensitive data. However, the rise of quantum computing could pose a new challenge as quantum computers could break these encryption methods. 

This applies not only to payments but to all sensitive data protected by traditional encryption methods. Banks, with the help of partners, are already working to develop quantum-safe environments to address this risk.

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Zero-day and zero-click attacks on digital banking: a comprehensive review of double trouble

• Original Article
• Published: 28 September 2023
• Volume 25 , article number  25 , ( 2023 )

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• Kausar Yasmeen 1 &
• Muhammad Adnan   ORCID: orcid.org/0009-0000-3752-0841 2  

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The media has consistently covered the far-reaching consequences of Zero-Click and Zero-Day attacks on digital banking, which have resulted in widespread disruption. Despite this, there is a noticeable lack of scientific research conducted on this subject. This review aims to provide a modest yet significant contribution to understanding Zero-Click and Zero-Day attacks on digital banking. To achieve this objective, this study employs a comprehensive methodology that incorporates a multitude of scholarly sources. These include articles, review articles, books, and whitepapers published up until 2023. The aim is to develop a theoretical framework for preventing zero-click attacks with zero-day vulnerabilities. The research findings suggest that the combination of a zero-click attack, and zero-day vulnerabilities poses a significant challenge for banks in detecting such attacks. This, in turn, increases the hacker’s chances of success. Based on the literature review, this study has formulated a framework with the potential to minimize the likelihood of zero-click and zero-day attacks on digital transactions. The uniqueness of this review paper lies in its in-depth analysis of scholarly sources and the development of a theoretical framework to prevent Zero-Click and Zero-Day attacks on digital banking. The potential implementation of this framework could significantly improve the security of digital transactions by reducing the probability of these types of attacks.

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Adil, M., R. Khan, and M.A.N.U. Ghani. 2020. Preventive techniques of phishing attacks in networks. In 2020 3rd international conference on advancements in computational sciences (ICACS), 1–8. IEEE.

Alabdan, R. 2020. Phishing attacks survey: Types, vectors, and technical approaches. Future Internet 12 (10): 168.

Article   Google Scholar  

Alshaer, M., and M. Alsaleh. 2019. Exploring the factors that influence consumer trust in online shopping: A systematic review. Journal of Retailing and Consumer Services 50: 221–238. https://doi.org/10.1016/j.jretconser.2019.05.008 .

Anantharaman, P. 2022. Protecting systems from exploits using language-theoretic security (Doctoral dissertation, Dartmouth College).

Andersen, J., and J.L. Lawall. 2010. Generic patch inference. Automated Software Engineering 17 (2): 119–148.

Anon. 2021a. https://gamecurse.com/en/shedun-a-malware-that-can-install-itself-on-your-android/ . Accessed 2 Apr 2022.

Anon. 2021b. www.csa.gov.sg/singcert/Publications/zeroing-on-zero-click-attacks-against-mobile-attacks . Accessed 20 Mar 2022.

Aung, M.M., and S. Venkatesan. 2020. An empirical study of factors affecting mobile payment adoption in developing countries. International Journal of Information Management 50: 92–105. https://doi.org/10.1016/j.ijinfomgt.2019.06.005 .

Ayat, M., and M. Eltoweissy. 2017. A survey of blockchain security issues and challenges. Journal of Internet Services and Applications 8 (1): 28. https://doi.org/10.1186/s13174-017-0066-y .

Azizi, N., and O. Haass. 2023. Cybersecurity issues and challenges. In Handbook of research on cybersecurity issues and challenges for business and FinTech applications , 21–48. IGI Global.

Bella, G., P. Biondi, and G. Tudisco. 2023. A double assessment of privacy risks aboard top-selling cars. Automotive Innovation 6 (2): 146–163.

Belli, L., O. Manzar, and N. Pahwa. 2020. The value of internet openness in times of crisis. India: Digital Empowerment Foundation.

Bera, A. 2019. Terrifying cybercrime statistics. https://safeatlast.co/blog/cybercrime-statistics/5 .

Bressolles, G., F. Durrieu, and S. Senecal. 2014. A consumer typology based on e-service quality and e-satisfaction. Journal of Retailing and Consumer Services 21 (6): 889–896.

Bronk, C., and E. Tikk-Ringas. 2013. The cyber attack on Saudi Aramco. Survival 55 (2): 81–96.

Cloth, C. 2015. Assessment of the challenges facing tour guides and porters: a case of Kilimanjaro National Park (Doctoral dissertation, The University of Dodoma).

Corner, M.D., and B.D. Noble. 2002. Zero-interaction authentication. In Proceedings of the 8th annual international conference on mobile computing and networking , 1–11.

Cui, Y., and X. Zhang. 2017. A survey on fog computing. In 2017 IEEE 3rd international conference on big data security on cloud (BigDataSecurity), IEEE international conference on high performance and smart computing (HPSC) and IEEE international conference on intelligent data and security (IDS) , 138–143. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2017.52 .

Davison, T. 2021. Cyberthreats are going mobile and it’s time to take action. Computer Fraud & Security 2021 (3): 18–19.

Demme, J., M. Wachs, and M. Schunter. 2017. Securing IoT devices with certificate less public key cryptography. In 2017 IEEE 5th international conference on future internet of things and cloud (FiCloud), 28–35. https://doi.org/10.1109/FiCloud.2017.30 .

Estrela, P.M.A.B., R.D.O. Albuquerque, D.M. Amaral, W.F. Giozza, and R.T.D.S. Júnior. 2021a. A framework for continuous authentication based on touch dynamics biometrics for mobile banking applications. Sensors 21 (12): 4212.

Estrela, V.V., L.A. Paz, T.H.M. Ramos, and M.A.M. Vieira. 2021b. The effects of social media influences on consumer behavior: A literature review. International Journal of Information Management 57: 102261. https://doi.org/10.1016/j.ijinfomgt.2020.102261 .

Estrela, V.V., T.H.M. Ramos, L.A. Paz, and M.A.M. Vieira. 2021c. Examining the influence of social media influences on consumer trust and purchase intention. International Journal of Information Management 57: 102295. https://doi.org/10.1016/j.ijinfomgt.2020.102295 .

Fairchok, S. 2022. Spike in destructive attacks, ransomware boosts banks’ cybersecurity spending in 2022 . Insider Intelligence.

Google Scholar  

Fiscutean, A. 2022. Zero-click attacks explained, and why they are so dangerous . CSO. https://www.csoonline.com/article/3660055/z .

Ghelani, D., T.K. Hua, and S.K.R. Koduru. 2022. Cyber security threats, vulnerabilities, and security solutions models in banking. Authorea Preprints . https://doi.org/10.22541/au.166385206.63311335/v1 .

Goel, S., and R. Agarwal. 2018. Customer loyalty programs and multi-channel shopping behavior: The role of brand trust. Journal of Retailing and Consumer Services 44: 115–123. https://doi.org/10.1016/j.jretconser.2018.06.005 .

Grewal, R., and R. Dharwadkar. 2002. The role of the institutional environment in marketing channels. Journal of Marketing 66 (3): 82–97.

Guri, M. 2022. AIR-FI: Leaking data from air-gapped computers using Wi-Fi frequencies. IEEE Transactions on Dependable and Secure Computing . https://doi.org/10.22541/au.166385206.63311335/v1 .

Hamandi, K., A. Salman, I.H. Elhajj, A. Chehab, and A. Kayssi. 2015. Messaging attacks on Android: Vulnerabilities and intrusion detection. Mobile Information Systems . https://doi.org/10.1155/2015/746930 .

Hasan, M.F., and N.S. Al-Ramadan. 2021. Cyber-attacks and cyber security readiness: Iraqi private banks case. Social Science and Humanities Journal 5 (8): 2312–2323.

Hashemi, S.H., A. Mousavi, D. Kouchakinejad, and H.H. Gharakheili. 2017. Towards efficient traffic engineering in SDN: A survey. Computer Networks 129: 341–364. https://doi.org/10.1016/j.comnet.2017.10.002 .

Hopcraft, R., K. Tam, J.D.P. Misas, K. Moara-Nkwe, and K. Jones. 2023. Developing a maritime cyber safety culture: Improving safety of operations. Maritime Technology and Research . https://doi.org/10.33175/mtr.2023.258750 .

Huawei Technologies Co., Ltd. 2022. HUAWEI AI open platform. In Artificial intelligence technology , 221–238. Singapore: Springer Nature Singapore.

Jain, A.K., and B.B. Gupta. 2022. A survey of phishing attack techniques, defense mechanisms and open research challenges. Enterprise Information Systems 16 (4): 527–565.

Johansen, J.D., K. Aalto-Korte, T. Agner, K.E. Andersen, A. Bircher, M. Bruze, et al. 2015. European Society of Contact Dermatitis guideline for diagnostic patch testing—Recommendations on best practice. Contact Dermatitis 73 (4): 195–221.

Kaafar, M.A., and A. Panchenko. 2013. A privacy-preserving technique for network measurement. Computer Networks 57 (3): 727–742. https://doi.org/10.1016/j.comnet.2012.10.007 .

Kamarushi, M.V., S.L. Watson, G.W. Tigwell, and R.L. Peiris. 2022. OneButtonPIN: A single button authentication method for blind or low vision users to improve accessibility and prevent eavesdropping. In Proceedings of the ACM on human–computer interaction , 6(MHCI), 1–22.

Kanekar, S. 2022, www.financialexpress.com/money/top-digital-payment-trends-of-2022/2459345/ . Accessed 11 Apr 2022.

Kanimozhi, G., and K.S. Kamatchi. 2017. Security aspects of mobile-based E-wallet. International Journal on Recent and Innovation Trends in Computing and Communication 5 (6): 1223–1228.

Krombholz, K., H. Hobel, M. Huber, and E. Weippl. 2013. Social engineering attacks the knowledge worker. In Proceedings of the 6th international conference on security of information and networks , 28–35.

Krombholz, K., H. Hobel, M. Huber, and E. Weippl. 2015. Advanced social engineering attacks. Journal of Information Security and Applications 22: 113–122.

Kruegel, C., and G. Vigna. 2003. Anomaly detection of web-based attacks. In Proceedings of the 10th ACM conference on computer and communications security , 251–261.

Li, K.S., R.Z. Wang, L. Xu, C.C. Zhang, X.X. Xia, M.J. Tang, et al. 2022. Life prediction and damage analysis of creep-fatigue combined with high-low cycle loading by using a crystal plasticity-based approach. International Journal of Fatigue 164: 107154.

Marczak, B., and J. Scott-Railton. 2016. The million dollar dissident: NSO group’s iPhone zero-days used against a UAE human rights defender . Citizen Lab, 24.

Marczak, B., J. Scott-Railton, B. Abdul Razzak, N. Al-Jizawi, S. Anstis, K. Berdan, and R. Deibert. 2021. Forced Entry. https://citizenlab.ca/2021/09/forcedentry-nso-group-iMessage-zero-click-exploit-captured-in-the-wild . Accessed 22 Apr 2022.

Marczak, B., J. Scott-Railton, N. Al Jizawi, S. Anstis, and R. Deibert. 2020. The great iPwn: Journalists hacked with suspected NSO group iMessage ‘zero-click’ exploit . Toronto: University of Toronto, Citizen Lab.

Mohammad, R., F. Thabtah, and L. McCluskey. 2015. Tutorial and critical analysis of phishing websites methods. Computer Science Review 17: 1–24.

Monisha, C., K. Pavan Kumar, P. Ajay, P. Kumar Chandra, and S.K. Negi. 2022. Implicit methods of multi-factor authentication. In International conference on metaheuristics in software engineering and its application , 20–31. Cham: Springer.

Mugarza, I., J.L. Flores, and J.L. Montero. 2020. Security issues and software update management in the industrial Internet of Things (IoT) era. Sensors 20 (24): 7160.

Najaf, K., M.I. Mostafiz, and R. Najaf. 2021. Fintech firms and banks sustainability: Why cybersecurity risk matters? International Journal of Financial Engineering 8 (02): 2150019.

Nichols, S. 2020. One malicious MMS is all it takes to pwn a Samsung smartphone: Bug squashed amid android patch batch. www.theregister.com/2020/05/08/samsung_android_patches/ . Accessed 23 Mar 2022.

Nisha, T.N., and M.S. Kulkarni. 2022. Zero click attacks—A new cyber threat for the e-banking sector. Journal of Financial Crime .

Nour-Eldin, H.A. 2017. The impact of social media on the Arab spring uprisings. Journal of Arab & Muslim Media Research 10 (2): 189–209.

Nour-Eldin, H.A., and Y. Al-Saggaf. 2018. Social media use in the Arab world: A review of the literature. Journal of Middle East Media 14 (1): 1–22.

Noyes, M., and H. Alamleh. 2022. Input fuzzing for network-based attack vector on smartphones. In 2022 IEEE international IOT, electronics and mechatronics conference (IEMTRONICS), 1–4. IEEE.

Perdisci, R., K. Wu, and S. Jha. 2013. Using probabilistic topic models for criminal behavior analysis. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining , 785–793.

Purkait, S. 2012. Phishing countermeasures and their effectiveness—Literature review. Information Management and Computer Security . https://doi.org/10.1108/09685221211286548 .

Qamar, A., A. Karim, and V. Chang. 2019. Mobile malware attacks: Review, taxonomy & future directions. Future Generation Computer Systems 97: 887–909.

Qasaimeh, M., R.A. Hammour, M.B. Yassein, R.S. Al-Qassas, J.A.L. Torralbo, and D. Lizcano. 2022. Advanced security testing using a cyber-attack forecasting model: A case study of financial institutions. Journal of Software: Evolution and Process 34 (11): e2489.

Reurink, A. 2018. Financial fraud: A literature review. Journal of Economic Surveys 32 (5): 1292–1325.

Rudie, J.D., Z. Katz, S. Kuhbander, and S. Bhunia. 2021. Technical analysis of the no group’s Pegasus spyware. In 2021 international conference on computational science and computational intelligence (CSCI), 747–752. IEEE.

Saini, H., Y.S. Rao, and T.C. Panda. 2012. Cyber-crimes and their impacts: A review. International Journal of Engineering Research and Applications 2 (2): 202–209.

Sausalito, C. 2020. Cyberwarfare in the C-suite. Cybercrime Magazine 24.

Schmitt, M. 2022. Mobile security for the modern CEO: Attacks, mitigations, and future trends. arXiv Preprint . https://arxiv.org/abs/2207.08105 .

Scott-Railton, J., E. Campo, B. Marczak, B.A. Razzak, S. Anstis, G. Böcü, S. Solimano, and R. Deibert. 2023. Catalangate: Extensive mercenary spyware operation against Catalans using Pegasus and Candiru. The Citizen Lab. Retrieved Feb 17, 2023.

Sengupta, S., and A.R. Chowdhury. 2017. A comparative study on supervised and unsupervised learning algorithms for intrusion detection. International Journal of Computer Applications 171 (7): 35–40.

Shafqat, M.N., S. Ahmed, S. Ahmad, and N. Alrajeh. 2022a. A comprehensive survey of machine learning and deep learning-based intrusion detection systems. Journal of Network and Computer Applications 194: 103207.

Shafqat, N., C. Topcuoglu, E. Kirda, and A. Ranganathan. 2022b. Experience report on the challenges and opportunities in securing smartphones against zero-click attacks. arXiv Preprint . https://arxiv.org/abs/2211.03015 .

Shaikh, F.A., and M. Siponen. 2023. Information security risk assessments following cybersecurity breaches: The mediating role of top management attention to cybersecurity. Computers & Security 124: 102974.

Shaker, A.M.N.F., and A.M. Mohamed. 2021a. Zero click attack. In The international undergraduate research conference , vol. 5, no. 5, 46–49. The Military Technical College.

Shaker, S., and A. Mohamed. 2021b. A survey on intrusion detection systems. Computers & Electrical Engineering 87: 106960.

Silvanovich, N. 2019. The fully remote attack surface of the iPhone. https://google.projectzero.blogspot.com/2019/08/the-fully-remote-attack-surface-of.html . Accessed 13 Mar 2022.

Srinivasan, S., and K. Raja. 2016. Preventing cloud attacks using bio-metric authentication in cloud computing. Indian Journal of Science and Technology 9 (23): 1–9.

Sun, Y., J. Liu, K. Yu, M. Alazab, and K. Lin. 2021. PMRSS: Privacy-preserving medical record searching scheme for intelligent diagnosis in IoT healthcare. IEEE Transactions on Industrial Informatics 18 (3): 1981–1990.

Tarhini, A., C. Mgbemena, M.S.A. Trab, and R. Masa’Deh. 2015. User adoption of online banking in Nigeria: A qualitative study. The Journal of Internet Banking and Commerce 20 (3): 1–8.

Uddin, M.A.M., and M.K. Hassan. 2020. Cybersecurity hazards and financial system vulnerability: A synthesis of literature. Risk Management 22 (4): 239–309.

Vagadia, B. 2020. Data integrity, control, and tokenization. In Digital disruption , 107–176. Cham: Springer.

Van Bossuyt, D.L., B. Hale, R. Arlitt, and N. Papakonstantinou. 2023. Zero-trust for the system design lifecycle. Journal of Computing and Information Science in Engineering . https://doi.org/10.1115/1.4062597 .

Vartanian, T.P. 2023. The unhackable internet: How rebuilding cyberspace can create real security and prevent financial collapse . Lanham: Rowman & Littlefield.

Vigna, G., F. Valeur, D. Balzarotti, W. Robertson, C. Kruegel, and E. Kirda. 2009. Reducing errors in the anomaly-based detection of web-based attacks through the combined analysis of web requests and SQL queries. Journal of Computer Security 17 (3): 305–329.

Welander, P. 2012. What is a zero-day cyber attack? Control Engineering 59 (8): 56–57.

Xiao, Y., Y. Jia, C. Liu, X. Cheng, J. Yu, and W. Lv. 2019. Edge computing security: State of the art and challenges. Proceedings of the IEEE 107 (8): 1608–1631.

Yan, G. 2022. Zero-day attack . Hawie. https://support.huawei.com/enterprise/it/doc/EDOC1100196788 .

Younis, A.A., Z. Daher, B. Martin, and C. Morgan. 2022. Mapping zero-click attack behavior into MITRE ATT&CK mobile: A systematic process. In 2022 international conference on computational science and computational intelligence (CSCI), 890–896. IEEE.

Zdziarski, J. 2014. Identifying back doors, attack points, and surveillance mechanisms in iOS devices. Digital Investigation 11 (1): 3–19.

Zhang, J., and J. Hu. 2017. Anomaly-based network intrusion detection using clustering and supervised learning. In 2017 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), 796–802.

Zou, D., and X. Wang. 2020. Deep learning-based network intrusion detection: A review. Neurocomputing 399: 46–61.

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Yasmeen, K., Adnan, M. Zero-day and zero-click attacks on digital banking: a comprehensive review of double trouble. Risk Manag 25 , 25 (2023). https://doi.org/10.1057/s41283-023-00130-4

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David Birch confirmed as EBAday 2024 challenge speaker

Exploring digital money, digital assets, digital identity and the future of banking, David G. W. Birch has been confirmed as the keynote speaker for one of the most compelling sessions at EBAday every year.

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In 2023, for example, alumni of the Young Global Leader by the World Economic Forum programme and founder and CEO of Stellar Capacity, Claudia Olsson was EBAday’s challenge speaker.

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Ranked one of 2022’s global top 30 fintech influencers, Birch is one of the top three most influential people in London’s fintech community and rated Europe’s most influential commentator on emerging payments.

He is also a contributor for Forbes, a columnist for Financial World magazine and has written for publications ranging from the Parliamentary IT Review to The Financial Times. He also wrote a column in The Guardian for many years. A media commentator on electronic business issues, he has appeared on BBC television and radio, Sky and other channels around the world.

In 2022, Finextra  sat down with Birch to discuss some of the evolving trends in digital identity; when asked why it is important that we strive toward a world where the consumer has full autonomy and control over their data, he said: “This comes down to the difference between implementing things using biometrics. For example, I walk into Waitrose, Waitrose takes my picture and they run it through some facial software, they realise it’s Dave, I buy some stuff. That all works. But identification has danger. You don’t have to be paranoid to be worried about surveillance and hacking. In world where you have something like a ring with a secure chip, that’s different. I think it’s giving customers that control.

“Giving customers control I see very positively. I understand there is some convenience benefits in not having anything and just walking in and having a face scanner, but there’s a lot of negatives. This includes one of my favourite stories from the South China Morning Post , about a woman who got a nose job and could no longer get into her bank account!” Birch also explored how he thought digital identity would evolve, to which he answered: “Without a shadow of a doubt digital identity is a keystone. The crucial dynamic I think everyone understands is, if you have identity, if you know who everybody is, payments is just a bit of mucking about on a spreadsheet. All of the complexity of payments is to do with authentication, fraud, risk management, all of these. It’s because we don’t know who everyone is. If the identity side is sorted out, payments are easy.

“As for what’s going on in the space, I think people don’t see what’s bubbling under the surface. For example, I read that Apple, Google, and Microsoft were going to support W3C FIDO authentication. I think people just really didn't understand the implications of that, the idea that you'll have a key ring or whatever and you'll be able to log into anything. So Apple will accept Microsoft authentication etc. This sort of stuff is bubbling along, and I don't think people see quite how important all is. It's really going to change a lot.”

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Bank of England Sets Out Conditions for 'Digital Sandbox'

Bank of England Sets Out Conditions for 'Digital Sandbox'

A general view shows the Bank of England building, in London, Britain November 3, 2022. REUTERS/Toby Melville/File Photo

LONDON (Reuters) - Britain's financial regulators on Wednesday launched a public consultation on their new "sandbox" for trading digital securities in "real world situations" to keep up with rapid advances in technology.

A "sandbox" allows the testing of new services in the market with real customers, but within a controlled regulatory environment.

The Bank of England and Financial Conduct Authority said in joint proposals that within the sandbox, existing financial rules would be modified to enable companies to try out new technology, such as distributed ledger technology or blockchain that underpins cryptoassets, for trading and settling digitised bonds and stocks.

The regulators are consulting on draft guidance on how firms would apply to operate within the sandbox, and scale up activities .

"For the first time, they will be able to provide these services from a single legal entity," the BoE and FCA said.

"The adoption of new technology in this area, if done safely, could lead to a technological transformation that will promote greater efficiency as well as greater resilience in the financial system in the long run."

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The new sandbox will last five years and could lead to a new permanent regulatory regime for securities settlement, whereby ownership of a stock or bond is swapped for cash.

The consultation is open until May 29, with final guidance published in the summer.

(Reporting by Huw Jones; Editing by Emelia Sithole-Matarise)

Copyright 2024 Thomson Reuters .

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Adoption of digital banking channels in an emerging economy: exploring the role of in-branch efforts

Simran jit kaur.

1 School of Management Studies, Punjabi University, Patiala, Punjab India

M. Kabir Hassan

2 Department of Economics and Finance, University of New Orleans, New Orleans, LA 70148 USA

Md Al-Emran

3 McNeese State University, Lake Charles, LA 70609 USA

The aim of this qualitative study is to analyse the role of in-branch efforts of banks on migrating customers from branch banking to digital banking in India. In-depth semi-structured interviews were conducted with bank executives representing senior management from public and private sector banks in India. Qualitative content analysis technique was used to analyse the data. Varieties of responses received during interviews were clubbed into four main themes based on data reduction, display, and conclusion-drawing processes. In-branch communication with customers, digital transformation of the branch, customer-centric initiatives, and redefined role of branch staff hold the potential to bridge the customers’ migration to digital banking. The paper suggests that the key identified factor in improving digital banking acceptance in India is the requirement of integrated cultural and organisational changes at the bank’s level to gain the customers’ confidence and trust in digital banking.

Introduction

In the last few decades, huge investments have been made by banks in technology to reduce their cost and improve customer’s experience. Banks are offering digital banking channels such as ATM, Internet banking, mobile banking, digital banking kiosks to deliver best quality services to customers with the expectation of increasing profitability and reducing operating cost (Sarel and Marmorstein 2003 ). It is observed that the bank’s costs reduce with the shift of a major chunk of customers to modern banking channels (Howcroft et al. 2002 ). However, the expected reduction in operating expenses has not been achieved yet by the banks as they are still struggling to move customers towards digital banking channels (Sarel and Marmorstein 2002 ; DeYoung et al. 2007 ; He et al. 2019 ). The situation is much critical for emerging countries such as India where only 16% of the rural population use the Internet for making digital payments (Pandey 2018 ).

According to the report released by Gartner, IT expenditure by securities and banking firms in India has reached $9.1 billion with a growth of 11.7% (Shetty 2017 ). Further, the total IT expenditure is expected to reach $11 billion in 2020 (Gartner 2019 ). However, the return on investment of Indian banks in technology is just 12% of US banks due to the low rate of digital banking acceptance (Sinha and Mukherjee 2016 ). At the same time, it is worth mentioning that the cash transactions cost is 1.7% of Indian GDP which puts a huge burden on the economy (Bakshi 2016 ). In this regard, the Government of India initiated the ‘Digital India’ campaign in 2015 to empower people digitally. The success of the ‘Digital India’ campaign is apparent from the fact that more than a billion Indian citizens have a digital identity with 560 million Internet connections (Kumar 2019 ). The purpose of digitisation is to bring disconnected rural remote regions into the formal financial sector through electronic banking channels which in turn will contribute to economic development. Digital banking mediums help to connect the underserved masses with mainstream banking system by offering various innovative banking services. The modern mobile banking apps also enable customers to use non-financial services. However, due to the lack of awareness and knowledge, these services have not been fully utilised by customers (Shaikh et al. 2020 ). Certainly, there is a dire need to positively influence customers about the usability of modern banking channels and persuade them to migrate to digital channels (Figs.  1 and ​ and2 2 ).

Research framework (adapted from Davis 1989 ; Lee et al. 2007 ; Yap et al. 2010 )

Key in-branch efforts and associated challenges

Banks in India need to understand that making huge investments in technology is not enough unless most bank customers adopt it for banking transactions. As rightly argued by Shaikh and Karjaluoto ( 2016 ) that digital banking is much more than an innovative banking channel and a convincing marketing strategy. The term digitisation has brought a significant change in how banks understand and satisfy its customers’ needs. In India, efforts have been made by the banks to persuade customers to adopt digital banking channels such as intensive digital marketing campaigns to educate customers about modern channels, but still, the adoption rate is not as expected (Patel and Patel 2018 ). Since bank branches provide an opportunity to the bank to communicate and persuade customers personally by demonstrating the proposed benefits of adopting modern banking channels, the present study exhibits the need for Indian banks to focus upon implementing serious in-branch effort. It is the high time for banks to identify cost-effective in-branch strategies to connect the masses with digital banking channels, though this area is under-researched. In this regard, the present study attempts to evaluate the bank executives’ perceptions regarding the effectiveness of in-branch efforts of banks to persuade the customers to adopt and use digital banking channels.

The earlier studies conducted in the area of digital banking have mainly explored the attitude of customers and antecedent variables that matter to customers while using digital banking channels or the factors impacting the intention of customers’ to use the modern banking channels (Montazemi and Qahri-Saremi 2015 ; Szopiński 2016 ; Alalwan et al. 2017 ; He et al. 2019 ). But surprisingly, rarely a study in India has given attention to study the impact of the bank’s initiatives within the branch to encourage customers to adopt modern banking channels.

The present study tries to close this research gap by answering the following research question:

RQ: How In-branch experience and technological initiatives can contribute to the adoption of digital banking channels by customers.

To answer this research questions, we draw on TAM (technology acceptance model) to analyse how in-branch efforts of banks can contribute to enhancing customers’ perceived usefulness (PU) and ease of use (PEOU) which positively influences customers’ attitude towards technology acceptance. Also, the analysis is guided by additional antecedents’ variables identified by extended TAM proposed by various researchers and scholars in the context of adopting self-service banking technology.

The empirical data for the qualitative study are based on in-depth interviews conducted with 22 bank executives from public and private sector banks in northern urban India. The recent report by Vater et al. ( 2019 ) strongly highlights the struggle for banks at present to migrate customers to digital channels. Due to huge investments involved in the digital banking platform, ensuring the adoption and usage of these channels is an important goal of the banks. The study elucidates the need to transform future bank branches by identifying cost-effective strategies and segmenting the branch customers on the basis of their banking needs and preferences. The present study contributes to the literature by studying how personalisation and in-branch initiatives should be strengthened by the bank managers to build customers’ initial trust in digital banking channels. The study also contributes to identifying various integrated cultural and organisational obstacles particularly in public sector bank branches which hinder the customers’ adoption of digital banking. The study begins with a review of the literature on customer technology acceptance for banking transactions. In the later section, we describe the conceptual framework for analysing interview data based on TAM (Davis 1989 ) and its extensions propounded in context of adopting self-service banking technology, followed by research methodology section. The following section describes the findings in terms of identified themes and concludes with the discussion of findings.

Status of digital banking adoption in India

Technology has transformed the banking industry all over the world. However, the adoption rate of technology-enabled banking services varies across different countries (Takieddine and Sun 2015 ). In India, almost all banks offer digital banking services to their customers as a strategic tool to survive in the market (Safeena et al. 2014 ). With the growth of investment in technology by financial service providers in India, it becomes highly important to understand the perceptions of customers and designing the strategies accordingly (Roy et al. 2017 ).

Post demonetisation (invalidation of large currency notes) Government of India has launched various efforts to migrate customers to digital payment channels from cash (e.g. e-Wallets, Unified Payment Interface, Aadhaar-enabled payment system, etc.). As per the report of Reserve Bank of India (RBI) on digital transactions, the total volume of non-cash transaction in India has reached 1.9 billion in 2016–2017 from 228.9 million in 2004–2005. Despite this rapid growth, the largest public sector bank (SBI) in India has reported only 5.86% mobile banking users and 9.69% of Internet banking users in its recent annual report 2016–17. Thus, only informing or spreading awareness would not shift customers towards digital banking but requires changes in the implementation process and successful implementation depends upon how well these technological advancements are communicated to customers (Sarel and Marmorstein 2002 ).

The point of utmost importance is that in India the challenge is not just migrating customers from traditional banking channels to digital channels but also to connect the unbanked masses with the mainstream banking system using digital finance. Undoubtedly, digital mediums have increased the level of financial inclusion globally from 51% in 2011 to 69% in 2017 (Global Findex Database 2017 ). But the fact which differentiates the developing and emerging economies from developed economies is the adoption and use of digital banking channels from the consumer end. Apparently, in high-income economies, 91% of adults use digital payment method, while in developing economies just 44% of adults make digital payment through their account (Global Findex Database 2017 ).

India is an emerging economy wherewith the launch of digitisation campaign (2015) and implementation of demonetisation (2016) the significant chunk of the population is shifted quickly from traditional banking channels to digital channels. However, education, lack of infrastructure and strong Internet connectivity are the issues which hinder the adoption of digital banking channel in India (Tiwari 2019 ). As per statistics, 80% of Indians have a bank account (The Economic Times 2018a , b ). However, World Bank reported ( 2017 ) that 48% of the total 310 million accounts opened in India from 2014 to 2017 are inactive. The lower than anticipated regular usage of digital mediums for banking transactions calls for more in-depth and critical research on formulating the strategies and practices to migrate customers to technology-enabled banking channels based on the factors that matter to customers the most.

Conceptualisation of in-branch efforts and technology acceptance

The literature on digital banking is replete with examining the customers’ attitude (Pikkarainen et al. 2004 ; Eriksson et al. 2005 ; Ibrahim et al. 2006 ; Walker and Johnson, 2006 ; Poon 2007 ; Alalwan et al. 2016 ; Sánchez-Torres 2018 ) and factors affecting the acceptance of digital banking services (Montazemi and Qahri-Saremi 2015 ; Szopiński 2016 ; Alalwan et al. 2017 ). Few studies have also explored the impact of technology on the bank-customer relationship (Harden 2002 ; Durkin and Howcroft 2003 ). Further, Karjaluoto et al. ( 2018 ) highlighted the impact of investment in mobile financial services apps (MFSAs) on improved bank-customer relationships. Even in India vast number of studies have been conducted on customers’ behavioural factors (Malhotra and Singh 2010 ; Singh and Kaur 2011 ; Sinha and Mukherjee 2016 ) with regard to technology acceptance in the banking industry. However, very scanty literature is available on understanding the significance of in-branch efforts of banks to migrate customers to adopt digital banking channels. Impact of in-branch communication with the customers about technology-driven efforts has received hardly any attention of scholars in India.

In reference to the present study, the in-branch efforts are “the services provided at branch level to make customers comfortable with digital channels by imparting first-hand knowledge of technology-enabled banking services”. Sarel and Marmorstein ( 2002 ) were among the pioneering researchers who highlighted the role of communication at branch level to migrate customers to digital banking channels. They opined that effective communication with customers at the branch for persuading them to use digital banking channels can make a difference in their perception and attitude. It is worth mentioning that Former RBI governor Raghuram Rajan ( 2015 ) had also emphasised the significance of communicating the banking products and services to customers in regional language to connect the bank with its customers:

It is the responsibility of the government and the banking sector to provide banking facilities to those who have money but have no access to formal banking channels in a language that they would understand. We should also arrange for financial literacy in the language that they understand.

Previous studies have already reported high bank branch footfall in Indian banks. A survey report by Schofield and Chew ( 2013 ) on future of bank branch in Asian banks established that an average bank customer in India makes 28 branch visits in a year which is very high as compared to developed countries like Australia, UK and the USA. Similarly, Avaya ( 2017 ) surveyed 5000 banking customers from UK, Australia, UAE and India. The survey reports that 51% of Indian bank customers still prefer to visit their branch regularly which is highest among the four countries surveyed.

Further, Marous ( 2013 ) identified that encouraging customers to change the current banking channel is a difficult venture. Similarly, Karjaluoto et al. ( 2019 ) asserted that customers’ intention to use contactless payment systems is highly influenced by habit and it is hard to change such behaviours. The best alternative at this stage that the bank could have is to set up an optimal channel mix to meet customers banking needs. Banks can educate their customers about online banking channels in the branch or online using interactive kiosks and tablets. Incentives for using online banking channel can persuade the customers to use these channels in the future (Accola 1996 ). Further, Brunier et al. ( 2015 ) opined that banks can reduce their cost and improve technology adoption rate by educating customers using in-branch interactive screens and branch employees can make customers familiar with technology-enabled banking services during their visit in the branch. They further reported that in the era of technology the significance of bank branch cannot be overlooked since customers still prefer branch network for taking highly specialised advisory services to buy high-value and complex products such as investment and mortgage.

In a recent study, Yu and Hughes ( 2016 ) highlighted the significance of in-branch ATMs and kiosks to successfully migrate customers to digital channels. The authors revealed that banks need to identify the customer segments as per their banking needs and channel preference to create a unique channel mix. Similarly, the cognizant survey (2016) reported that bank branches can serve as the best platform to interact with the customers and influence them positively. The best customer experience at branch travels across other banking channels. Dallerup et al. ( 2018 ) defined various formats of smart branches in a digital era based on location. Four categories of bank branches such as Box branch, Standard branch, Segment branch and Flagship branch are suitable for areas with specific attributes. At the same time, branch employees need to be trained to perform multiple analytical tasks to improve bank performance.

Schofield and Chew ( 2013 ) revealed that most of the branch visits in Asian countries involve routine banking transaction which increases the cost and can easily be performed online. Thus, the biggest challenge for the banks is to divert the customers from branch banking to digital banking in an interactive way. The fact that in the era of the technology bank branches will continue to serve customers’ high-value and complex banking needs is well established in the literature (Luchetti 2017 ; Joyce 2017 ). Since the strong human relationship is the base of business in most of the Asian cultures (Rotchanakitumnuai and Speece 2003 ), the study attempts to concentrate on efforts to improve the online banking acceptance by maintaining the human touch. The study was initiated to highlight the need for banks to recognise the potential of in-branch initiatives to persuade customers to use technology-enabled banking channels and how banks can improve its online banking customer base by convincing its existing branch visiting customers about the usefulness of digital banking channels. There is no doubt that digital channels hold the potential to improve customer experience and banks have already invested hugely in technology.

The acceptance rate of digital banking channels in India is much lesser than anticipated (Patel and Patel 2018 ). The question is why banks are still struggling to move customers to digital banking channels even after making a huge investment in technology. Even in the USA, 38% of customers reported preferring bank branches or ATMs to other digital banking channels in a recent survey conducted by McKinsey Consumer Insight ( 2016 ). The matter of fact is that the company cannot always allow its customers to follow their preferences as this leads to an increase in the cost (Myers et al. 2004 ). This scenario brings light to focus on directing the customers to adopt the right channel mix for products and services. The first thing that banks can start with is transforming or modernising the bank branches to satisfy the needs of customers (Cognizant 20–20 Insights 2016 ). More specifically in India where the acceptance rate is already so low and digital banking is in its nascent stage.

TAM and digital banking adoption

In order to analyse the impact of in-branch efforts on the adoption of digital banking in India, the study draws upon the technology acceptance model (TAM) (Davis 1989 ). This is the highly accepted model to study the users’ attitude to technology by analysing perceived usefulness (PU) and perceived ease of use (PEOU), the two main determinants of users’ behaviour towards technology. Here, perceived usefulness (PU) is the degree to which the potential customer believes the new technology to improve his/her performance and perceived ease of use (PEOU) is concerned with prospective customers’ perception that new technology will reduce the efforts required (Davis 1989 ). TAM has been very well accepted and validated in a number of studies worldwide in the context of technology adoption (Taylor and Todd 1995 ; Wang et al. 2003 ; Pikkarainen et al. 2004 ; King and He 2006 ). Therefore, TAM provides a valid approach for our study to understand the adoption of digital banking in the Indian context. Also, the analysis is guided by additional antecedents’ variables identified by extended TAM proposed by various researchers and scholars in the context of adopting self-service banking technology.

Our review of related studies from developing countries mainly demonstrates the extension of technology acceptance model (TAM) (Davis 1989 ) based on cultural attributes and individual characteristics of consumers, as it has been believed by various researchers that basic TAM ignores the impact of external influences such as cultural and infrastructure availability on adoption pattern. For instance, evidence from the Middle East as provided by Sukkar and Hassan ( 2005 ) highlights the need to include cultural factors from the consumer side and technical quality from bank side to the existing TAM in order to make it more relevant in the context of developing countries. In the same line, Tobbin ( 2012 ) identified two additional variables such as economic factor and trust apart from TAM variables that matter to unbanked customers in Ghana while adopting mobile banking services.

Evidence from India brings light on the impact of computer self-efficacy, i.e. the ability to use computers, quality of Internet connection, Internet banking awareness and social influence apart from basic TAM variables on customers’ adoption of Internet banking (Sharma and Govindaluri 2014 ). In another study conducted by Nath et al. ( 2013 ) from the perspective of bank employees, three additional factors, namely computer self-efficacy, social influence, technological facility in terms of infrastructure, were reported as an extension to TAM variables in the Indian context. Banu et al. ( 2019 ) identified ease of use and self-efficacy as major drivers of technology adoption in India. Researchers reported a direct link between trust, offline service quality and adoption of technology (Patricio et al. 2003 ; Lee et al. 2007 ; Bashir and Madhavaiah 2015 ). Lack of required infrastructure and connectivity and trust has been reported as the major inhibitor for technology adoption in India (Nath et al. 2013 ; Sinha and Mukherjee 2016 ). In previous studies, traditional service quality in the bank has been viewed as an enabler to build customers’ trust in e-banking services which in turn influences customers’ adoption of e-banking. For example, Yap et al. ( 2010 ) were the pioneer researchers who explored the impact of offline service quality provided in the branch on the adoption of digital banking channels. Later on, Chiou and Shen ( 2012 ) highlighted the significance of offline environment on Internet banking acceptance.

Following the same thought, we proposed research framework for our study to analyse the bank executive’s perceptions of how in-branch efforts of banks in India can influence the adoption of digital banking based on some key identified factors from the literature to make it more relevant in the Indian context. Table  1 provides an overview of the predictors of technology adoption. In order to shift customers towards digital banking channels, we identify some most significant factors for the adoption of technology by customers in India. These factors were further used for coding the interview responses and identifying relevant themes for analysis purpose.

Table 1

Predictors of technology adoption

Data methodology

The current study attempts to qualitatively analyse the perceptions of the bank executives from public and private sector banks in India, working at branch level and well-versed with the bank’s operational and marketing strategies. Face-to-face interviews were conducted to collect the responses from the respondents between October 2017 and February 2018. The purpose is to get deeper insights into what bank executives perceive, experience and believe regarding the effectiveness of in-branch efforts on consumers’ attitude to adopt technology-enabled banking channels.

There are two reasons for selecting the qualitative approach: the dearth of empirical research (Hirschman 1986 ) in India on exploring the potential of in-branch efforts to promote digital banking channels and the flexibility which qualitative approach extends to deeply investigate complex relationships (Healy and Perry 2000 ).

Sample selection

Bank executives were selected from the public sector (State Bank of India, Punjab National Bank) as well as the private sector (Axis and HDFC). Bank executives have been selected as respondents as they possess a better understanding of ground reality due to extensive experience of the banking industry. Geographically, the study concentrates on two states from the Northern part of India where information technology (IT) hubs of north India are situated. As in most of the qualitative studies, purposive sampling technique was used to select the respondents from various banks. In total, 35 bank executives representing senior management from public and private banks were approached, but 13 bankers refused to participate in the study. Sample organisations (Banks) selected for the study represent the top banks in India in terms of market share and IT investment.

Data collection and analysis

In total 22 face-to-face interviews were conducted, out of which 12 were with public sector managers and 10 with private sector bank managers. The high response rate (62%) indicates the willingness of bankers to share their experiences and perceptions on the topic. One interviewee from public sector bank had previous work experience with a private sector bank. This helped to attain the unique set of perspectives regarding differences among in-branch practices of public and private sector banks in India. Most of the interviews lasted for an average of 25–30 min. Interviews were conducted in the English language. Most of the bank managers were not comfortable with the tape recording of the interview, so detailed notes were prepared to record the responses of the managers. During the fieldwork, it was observed that most of the bank branches were quite busy and the staff was fully occupied specifically in case of the public sector banks. Due to heavy rush and long queues in most of the bank branches, it was hard to take the time of bank managers for interview. However, with repeated visits and strong potential of the subject matter for improving banking experience, we managed to get insights of bank managers on the topic.

During interview sessions with bank executives basically, three areas were covered

• Their perception regarding technological interventions in the banking industry and how technology has changed working experience in the bank;
• In-branch efforts undertaken by the bank for persuading customers to use innovative digital banking channels if any;
• Can these efforts bring a positive change in customers’ attitude and perception towards technology-enabled banking channels and how?

The interviews were semi-structured in nature, and various other related questions were asked loosely to allow flexibility and get maximum insights. Appendix Table  3 gives an outline of the semi-structured interview.

Table 3

Interview protocol/questions framework

Qualitative content analysis technique (Mayring 2000 ) was used for the analysis of interview data. Extensive notes were prepared during interview sessions. Varieties of responses received during interviews were clubbed into four main themes using NVIVO 9 software, based on data reduction, display and conclusion-drawing processes (Miles and Huberman 1984 ).

For the analysis purpose, both inductive and deductive technique was used for identifying themes. Firstly, using the deductive technique the TAM aspects were explored based on the research question and theoretical concepts (Davis 1989 ; Lee et al. 2007 ; Yap et al. 2010 ) and then the codes were identified. Following the coding process, the second, third and fourth author refined the codes based on their suggestions but did not identify new codes. An inductive approach was used to connect the codes and identify themes relating to in-branch communication, branch staff roles, customer-centric initiatives and digitally driven branches with the human touch. Table  2 provides an overview of the coding criteria and theme identification process of the study.

Table 2

Coding criteria and theme identification

In-branch communication with customers

Communication at the branch to educate customers about online banking channels with either self-service technology like Internet kiosks or specialised bank staff was the dominant factor reported by interviewees. Majority of interviewees believe that effective communication at branch level to guide customers for using technology to fulfil their banking needs can bring a positive change in customers’ perception, specifically in rural and semi-urban areas where customers rarely have hands-on experience in computers and Internet.

It is worth mentioning that no direct question regarding communication was asked to respondents during the interview. However, 13 out of 22 bankers agreed that communication with customers at the branch is a key effort to bring positive change in current trends. A typical comment from Banker 1 was:

There is no better approach than interacting with customers about modern technology-based banking channels; it is as much important as handling their (customers) queries atthe branch. As banks have already invested hugely in technology…. its acceptance is pertinent to banks.

Most of the bankers in rural and semi-urban areas were highlighting the need for live demonstration at bank branches and communicating the benefits of online banking channels to customers. But surprisingly, when asked about live demonstration practices at their institutions, there were only a few bankers who agreed to have interactive screens in the bank to educate customers about using digital banking. A colleague from the same bank argued that:

There is need to make strategical changes, with shrinking staff level and heavy branch footfall it is next to impossible for us (Bankers) to initiate personalise interactive sessions with customers regarding how to use technology-enabled banking channels. Dedicated digital tech experts in every branch especially in rural branches can help customers to make maximum use of online banking channels (Banker 6).

Customer satisfaction was observed by respondents as an important factor while demonstrating the use of online banking channels at the bank branch. Enduring relationship of the bank with its customers is the result of regular communication (Howcroft et al. 2002 ; Waite and Harrison 2002 ). Previous studies have already established the significance of possessing communication skills along with technical knowledge about the product by the sales force to provide maximum satisfaction to the customer (Goff et al. 1997 ). Our research findings also highlight that the introduction of new banking channels requires bankers to gain expertise not only in banking technology but also in communication skills to educate customers to use innovative banking channels, as customers’ trust in online banking is the positively related to effective communication among bank and its customers (Mukherjee and Nath 2003 ). One of the interviewees commented:

That first experience of customers with online channels defines their future chances of using it and effective interactive sessions at bank branch have great chances to make this first experience positive…. But again, it requires dedicated expert staff which most of our bank branches lack (Banker 19).

Another concerning issue which bank executives brought up during the study is using different communication approaches for different segments of customers. Various segments of bank customers exist based on their attitude and expected benefits (Machauer and Morgner 2001 ). The interview data suggest that with limited trained experts it is not feasible for banks to target all branch visiting customers for migrating to online banking channels. One bank executive from a private sector bank suggests that branch managers can segment customers based on customers’ frequency of branch visit. High-cost customers who visit branch more frequently can be targeted on the priority basis with high-touch and demonstrative communication approach to reduce the workload significantly. Thus, the challenge for banks is to identify the most appropriate mode of communication to interact with different segments of customers to convince them to adopt digital banking channels.

Redefining the role of branch staff

Many of the branch managers (16/22) reported that technology has redefined the role of branch staff in the digital era. Bankers perceive that by equipping branch staff with right soft skills and competencies in technology to solve the issues of customers and improve their banking experience, banks can expedite the online banking adoption in a more effective manner. Bank executives shared their perception about pressing need for changing the role of bank staff due to technological interventions in the banking sector. Banker 15 presented his views regarding internal challenges:

I believe ourworkforce performs multiple jobs at a time like advice customers on high-value products, handles daily transactions but still I am not sure our efforts are enough to equip our staff with right skills to optimally utilize technology and branch space to develop a close relationship with customers and improve branch productivity.

However, during interviews, it was observed that the scenarios are quite different between public and private sector banks in India. When asked “If there is any special front desk for a relationship manager to provide advisory services in the branch”, 85% respondents from public sector banks responded negatively. Branch staff role in public sector banks was still found to be confined to handling queries of customers with no specific front counter for the relationship manager to extend personal advisory services to customers at the branch. On the flip side, private sector bank branches demonstrate financial advisory services provided by trained personal advisor designated as the Relationship manager.

One interviewee who had previous experience with a private sector bank explained:

Technology has changed the way banking is done within the branches, but when it comes to encouraging people to adopt technology in an engaging way…public sector banks have a long way to go (Banker 5).

Interestingly, public banks in India are very available in remote and rural areas, unlike private banks. They handle much of total bank accounts in the country. We find that front desk staff in public banks lack adequate customer bonding. Since branch service quality influences the customers’ adoption of online banking services (Yap et al. 2010 ), there is need for these banks to focus on training their staff with interpersonal skills to become more productive and effective. This finding is also supported by Kaur et al. ( 2012 ) in a previous study in which they highlighted the significance of training the bank employees as per bank’s future strategies and plans to improve their job commitment. In this regard one interviewee from major public sector bank confesses:

In the era of technology, we need to establish a closer link between employees and customers if we want to see positive results. The workforce at the branch needs to be trained digitally to educate customers about new banking products and channels (Banker 6).

A colleague from the same bank argues that most of the branch visiting customers of public sector banks belong to a low-income group and lack even basic knowledge about banking activities and these customers generally engage staff with regular activities which can easily be done online. It was observed that lack of resources (time and staff) and long queues at branches make the situation more critical for public sector banks. However, proper training for branch staff to develop a positive attitude towards technology (Lymperopoulos and Chaniotakis 2004 ) and educating customers to use digital banking channels can help to serve the purpose for both parties.

Customer-centric initiatives to strengthen the relationship

Another important observation during interviews with bank executives was concerned with understanding individual customers’ preferences and needs at the branch. Bank executives described it as permanent pressure for improving customer services to survive in the present volatile and competitive market. Prior studies have also observed that offline fulfilment for customer satisfaction is as significant as online service quality (Semeijin et al. 2005 ). Twelve bank managers asserted that understanding the needs of customers and delivering tailored products to build customers’ trust in the bank, strengthens the bank-customer relationship. When asked about how would customers’ trust in the bank helps to sell digital banking products to customers, majority of bank executives responded that trust on digital banking is a function of customers’ perceived trustworthiness of bank. If customers trust the bank and its services, then they would intend to use its other digital mediums as well.

At the same time, bank executives from public sector banks expressed concern over the attitude of branch employees and organisational culture issues because they perceived that due to lack of motivation and heavy workload branch employees find it hard to provide individual attention to customers which in turn negatively affects the bank-customer relationship. Such a comment came from Banker 10:

We don’t have an active action plan to ensure the positive frontline employee involvement with customers at the branch which is imperative to improve the overall performance of organisation and bank-customer relationship in digital era.

Interestingly, Banker 17 presented a completely different perspective on this issue and recognised that much more is needed to be done in India to make digital banking an indispensable part of people’s life. When asked about the challenges in the way of establishing trust and the strong bank-customer relationship he responded:

With limited resources at branches, it’s not feasible to handle a large pool of branch customers at once to convince them to adopt digital banking channel especially when majority of your customers lacks trust and competency to use technology for banking transactions.

The interviewee findings reveal that on the part of banks, efforts are required in the direction of segmenting the customers at the branch on the basis of their investments with the bank, banking needs, demographic profile, frequency of branch visits and then targeting the high-cost customers on a priority basis by offering them right channel mix. For instance, even previous studies conducted in the USA have documented that 58% of transactions at bank branch are generated by 18% of customers (Toit and Burns 2016 ). Bankers perceive that customer relationship management (CRM) at branch provides an opportunity to deepen the relationship with customers which in turn helps to convince the customers to adopt digital banking channels. Strong customer relationship with the customer-centric approach is suggested to create better chances for banks to reduce operational costs and improve their market share. Most of the bank executives emphasised that better customer relationship can make it possible for banks to create customer segments and target high-touch clients in bank branch to increase return on investment.

Digitally driven branches with human touch

In the digital era, banks are not only embracing technology-based banking channels to conduct transactions online but also focusing on modernising the bank branches. Various previous studies have already established that bank branches cannot be replaced in the present digital world (Baxter and Rigby 2014 ; Charniauski and Freeborn 2015 ; Brunier et al. 2015 ). During the current study, 15 bank executives reported that the banks can think of modernising branches with in-branch digital capabilities so that they can educate customers about modern technologies using the same platform which in turn helps to reduce the workload of branch staff. Previous studies have also demonstrated the importance of in-branch self-service technology to persuade non-adopter to adopt online banking channels (Berger 2009 ). It is evident that RBI’s guidelines ( 2017 ) (May 2017) regarding digital banking outlets (e.g. SBI InTouch) have shifted the banking landscape in India. Banks in India are more inclined towards transforming existing branches than opening new ones.

When asked about the target customer segment, the majority of respondents were of the view that as a digital push initiative bank branch transformation intends to target predominantly the branch visitors who still prefer branch banking than digital channels. Few interviewees believed that even in the case of tech-savvy customers, the human touch is significant for customer satisfaction and behavioural intentions (Makarem et al. 2009 ). A participant from private sector bank gave the example of security and performance-based risk issues and how educating and informing customers about the security measures and benefits of digital banking channels through live demonstrations at the branch can build the trust of customers in digital banking (Martins et al. 2014 ).

Interestingly, while most of the bank managers recognised the significance of transforming the bank branches they also raised the concern over the cost of modernising and equipping all or most of the branches with self-service technology. One senior bank executive from a major public sector bank commented:

No doubt, all our branches need to be modernised and equipped with self-service kiosks and touch screens, but timing is not right due to high capital expenses involved. The best approach at this time can be to link digital banking channels to bank branches” (Banker 21).

Discussion of findings

Davis ( 1989 ) argues that perceived usefulness (PU) and perceived ease of use (PEOU) are the most significant determinants of technology adoption. TAM has established a strong and positive relationship between PU and technology adoption. Customers’ behaviour intention has found to be influenced by perceived usefulness in a study conducted by Alalwan et al. ( 2017 ). Various other researchers have also highlighted the significant impact of perceived usefulness on customers’ attitude and intention to use technology-enabled banking services (Wang et al. 2003 ; Juwaheer et al. 2012 ; Wentzel et al. 2013 ; Loureiro et al. 2014 ). Our research findings indicate that effective in-branch communication between branch staff and customers may significantly influence customers’ attitude and intentions to use digital banking channels. The study reveals that once customers are communicated and informed personally about the usefulness, convenience and ease of using digital banking channels, they may perform all their future banking transactions online (Sathye 1999 ; Pikkarainen et al. 2004 ).

The proposed technological interventions and methods need to be simpler to understand; otherwise, customers would resist the change and continue with the traditional banking practices. Perceived ease of use has been identified as an important factor positively influencing the attitude of customers regarding technology-enabled banking services (Marakarkandy et al. 2017 ). More specifically individual’s ability to use computer and technology positively influences the adoption of technology (Nath et al. 2013 ). The research findings depict that live demonstration in the digitally enabled branches with self-service kiosks can educate customers about how to use digital banking mediums which in turn will result in customers’ acceptance. This finding is in line with the previous study conducted by Roy et al. ( 2017 ) where lack of self-efficacy (competence to use Internet and banking applications) was seen to negatively affect customers’ perceived ease of use which consecutively was found to have the negative impact on customers’ adoption of Internet banking in India.

The extended TAM supports the notion that branch service quality influences the customers’ adoption of online banking services (Yap et al. 2010 ). There is a need for banks to focus on training their staff with interpersonal skills to become more productive and effective. The findings of the study show that effective customer financial advisory service at branch helps in attaining customers’ trust. The quality of services delivered by branch staff influences the customers’ adoption of online banking services. Previous studies have (Patricio et al. 2003 ; Yap et al. 2010 ) similarly highlighted that traditional service quality at branch leads to customers’ satisfaction and trust in Internet banking services. Further, offline fulfilment for customer satisfaction is as significant as online service quality (Semeijin et al. 2005 ). Our research findings reveal that customer-centric efforts at bank branch can improve the customers’ overall satisfaction and trust by developing and strengthening the personal relationship with customers. In other words, understanding the needs of customers and delivering tailored products to customers builds customers’ trust in the bank and strengthens the bank-customer relationship. Trust on digital banking was reported as a function of customers’ perceived trustworthiness of a bank (Fig. ​ (Fig.2 2 ).

Theoretical implications of the study

The most prominent outcome of the present study pertains to the significance given to the in-branch customers’ experience and changing role of bank branches to encourage customers to adopt digital channel for conducting future banking transactions. This study proposed a model that highlights the impact of in-branch customer engagement on their intention to adopt digital banking channels in India. The present study contributes to the literature by studying how personalisation and in-branch initiatives can facilitate to build customers’ initial trust towards digital banking channels. In prior literature, some studies have been conducted on customers’ behavioural factors (Malhotra and Singh 2010 ; Singh and Kaur 2011 ; Sinha and Mukherjee 2016 ) with regard to technology acceptance in the Indian banking industry. However, our study is first to explore and understand the significance of in-branch efforts of banks to migrate customers to adopt digital banking channels. Impact of in-branch communication with the customers about technology-driven efforts has hardly received any scholarly attention.

Various studies have reported a strong, positive relationship between perceived usefulness, ease of use and customers’ attitude to adopt the technology (Wang et al. 2003 ; Juwaheer et al. 2012 ; Wentzel et al. 2013 ; Loureiro et al. 2014 ). Our research indicates that effective in-branch communication between branch staff and customers (Sathye 1999 ; Pikkarainen et al. 2004 ) and computer self-efficacy (Roy et al. 2017 ) developed through live demonstration in the digitally enabled branches may significantly influence the customers’ attitude and intention to use digital banking channels.

The study reveals that branch service quality influences the customers’ adoption of online banking services (Yap et al. 2010 ) by developing the trust of customers in e-banking services. Indian banks mainly public sector banks need to focus on training their staff with interpersonal skills to become more productive and effective. This finding is also supported by Kaur et al. ( 2012 ) in a previous study in which they highlighted the significance of training the bank employees as per bank’s future strategies and plans to improve their job commitment.

Our study supports the view that offline fulfilment for customer satisfaction is as significant as online service quality (Semeijin et al. 2005 ). Further, we add to the literature by establishing a direct relationship between customers’ trust in the bank and its e-banking services. The study emphasised the significance of customer relationship management (CRM) to identify the customer segments and target high-touch clients in a bank branch to increase return on investment.

Managerial implications

In emerging countries, the challenge for the banking industry is to meet the needs of highly distinctive segments of customers in urban, semi-urban and rural areas. Against all the sunny reports released by banking industry regarding digital banking adoption in India, our research findings suggest that banks need to take serious in-branch initiatives to educate and make a majority of customers comfortable with digital channels for banking payments and transactions. The present study elucidates the need to transform future bank branches by identifying cost-effective strategies. Trust on digital banking has been identified as a function of customers’ perceived trustworthiness of the bank. If customers trust the bank and its services, then they would intend to use the other digital mediums as well. Understanding and segmenting the branch visiting customers on the basis of how tech-savvy they are, their investments with the bank, banking needs, frequency of branch visits and then targeting the high-cost customers on a priority basis by offering them right channel mix (customer-centric approach) can make it possible for banks to increase return on investment and develop strong bank-customer relationship. Hence, banking regulators and industry should focus their attention to develop strong customer relationship management (CRM) practices in the branches to deepen the relationship with customers which in turn helps to build their trust in digital banking channels. One of the major hurdle in adopting the innovative digital banking channel is the lack of customers’ trust. Hence banks need to build the trust of customers through providing them personalised banking services by identifying the different segments of customers and offering them the right channel mix.

The study also identifies various integrated cultural and organisational obstacles particularly in public sector banks which hinder the customers’ adoption of digital banking. The banks need to focus the marketing strategy on enabling branches with digital capabilities, deploying more digitally trained employees in the branches to develop technological self-efficacy among customers to use innovative digital banking channels which in turn can help to reduce the digital divide in India.

This study draws the attention of the bank managers towards the need to design appropriate in-branch communication strategy by identifying various segments of branch visiting customers and make special efforts for the training of frontline branch staff (Cooper et al. 1994 ) to instil expertise not only in banking technology but also in communication skills. Further, our interview data suggest that banks in India are concerned about the high cost involved in branch digitisation. Banks need to understand that return on investment in technology would occur in the long run only if the large chunk of its customers migrates to digital channels and that is possible with the transformation of existing branch service model (Tang 2016 ). Migration of customers to digital banking channels in India calls for developing branch transformation strategy with the focus on a customer-centric approach. However, the branch transformation is seen to face various challenges, especially in the context of public sector banks in India. These issues raise the urgent need for focusing on improving in-branch practices of banks to convince customers to migrate to digital banking channels.

Due to COVID-19 maintaining physical distancing and providing in-branch services to customers is another challenge for banks in India with high number of branch visiting customers. In this strange time, banks can put a restriction on number of customers entering the branch at a time with flexible working hours to spread customer footfall and rotating staff. Additionally, visit by appointment only could be another alternative for banks to ensure the safety of customers and staff. The banks can also develop integrated services for customers in rural areas where business correspondents and India Post channel can provide banking services using digital tools at the door step of the customers with the purpose of reducing the number of customers visiting the branch.

Limitations of the study and future research

The study significantly contributes to the literature but with few limitations which can be addressed in future research. The present study concentrates on bank managers perceptions about in-branch efforts. For future research, it would be fruitful to trace out the perceptions of customers regarding the impact of in-branch efforts on their adoption of digital banking channels. Further, insights from this study could be used to frame a model for the impact of in-branch initiatives on customers’ adoption of digital banking that can be empirically tested. Future research can examine the impact of COVID-19 on digital banking and mobile payments acceptance in India as the current pandemic situation has encouraged customers to access remote banking services. Another avenue for future research is exploring the potential of modern agent network-based payment models in reaching out financially excluded section and examining whether these new models are opportunity or threat to the established digital channels.

See Table  3 .

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Md Al-Emran, Email: ude.eseencm@narmelam .

• Accola, J. 1996. Bank pays customers to use ATM, Denver Rocky Mountain News, 25 April, pp. 3b.
• Alalwan AA, Dwivedi YK, Rana NP. Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management. 2017; 37 (3):99–110. doi: 10.1016/j.ijinfomgt.2017.01.002. [ CrossRef ] [ Google Scholar ]
• Alalwan AA, Dwivedi YK, Rana NP, Williams MD. Consumer adoption of mobile banking in Jordan: examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management. 2016; 29 (1):118–139. doi: 10.1108/JEIM-04-2015-0035. [ CrossRef ] [ Google Scholar ]
• AVAYA, 2017. “The Avaya-commissioned Customer Experience in Banking 2017 Report”, available at: https://www.avaya.com/en/about-avaya/newsroom/news-releases/2017/pr-aus-170810/(accessed 10 January 2018).
• Bakshi, I. 2016. Cash deals cost 1.7% of GDP: Visa, available at: https://www.business-standard.com/article/economy-policy/cash-deals-cost-1-7-of-gdp-visa-116100500617_1.html(accessed 20 November 2017).
• Banu AM, Mohamed NS, Parayitam S. Online banking and customer satisfaction: Evidence from India. Asia-Pacific Journal of Management Research and Innovation. 2019; 15 (1–2):68–80. doi: 10.1177/2319510X19849730. [ CrossRef ] [ Google Scholar ]
• Bashir I, Madhavaiah C. Trust, Social Influence, Self-Efficacy, Perceived Risk and Internet Banking Acceptance: An Extension of Technology Acceptance Model in Indian Context. Metamorphosis. 2015; 14 (1):25–38. doi: 10.1177/0972622520150105. [ CrossRef ] [ Google Scholar ]
• Baxter, M. and D. Rigby. 2014. Rethinking the Bank Branch in a Digital World, available at: https://hbr.org/2014/09/rethinking-the-bank-branch-in-a-digital-world(accessed 12 December 2017).
• Berger SC. Self-service technology for sales purposes in branch banking: The impact of personality and relationship on customer adoption. International Journal of Bank Marketing. 2009; 27 (7):488–505. doi: 10.1108/02652320911002322. [ CrossRef ] [ Google Scholar ]
• Brunier, F., D. Patsch, and F. Stradtmann. 2015. Transforming the Banking Branch”, available at: https://www.accenture.com/t20160321T020410Z__w __/in- en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Strategy_7/Accenture-Strategy-Transforming-Banking-Branch.pdf (accessed 15 January 2018).
• Charniauski, A. and L. Freeborn. 2015. Branch Transformation in a Digital World, available at: https://www.idc.com/getdoc.jsp?containerId=FIBA01X(accessed 15 January 2018).
• Chiou JS, Shen CC. The antecedents of online financial service adoption: the impact of physical banking services on Internet banking acceptance. Behaviour & Information Technology. 2012; 31 (9):859–871. doi: 10.1080/0144929X.2010.549509. [ CrossRef ] [ Google Scholar ]
• Cognizant 20-20 Insights. 2016. Transforming the Branch: What Banks Need to Do, available at: https://www.cognizant.com/whitepapers/transforming-the-branch-what-banks-need-to-do-codex2217.pdf (accessed 10 February 2018).
• Cooper, W.W. 1994. Data envelopment analysis: Survey and interpretation, Operations Research:Communication of the Operations Research Society of Japan .
• Dallerup, K., S. Jayantilal, G. Konov, H. Stockmeier, and A. Legradi. 2018. The bank branch for the digital age, available at: https://www.mckinsey.com/industries/financial-services/our-insights/a-bank-branch-for-the-digital-age (accessed 13 September 2018).
• Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly , 319–340.
• Devi Juwaheer T, Pudaruth S, Ramdin P. Factors influencing the adoption of internet banking: a case study of commercial banks in Mauritius. World Journal of Science, Technology and Sustainable Development. 2012; 9 (3):204–234. doi: 10.1108/20425941211250552. [ CrossRef ] [ Google Scholar ]
• DeYoung R, Lang WW, Nolle DL. How the Internet affects output and performance at community banks. Journal of Banking & Finance. 2007; 31 (4):1033–1060. doi: 10.1016/j.jbankfin.2006.10.003. [ CrossRef ] [ Google Scholar ]
• Durkin MG, Howcroft B. Relationship marketing in the banking sector: the impact of new technologies. Marketing Intelligence & Planning. 2003; 21 (1):61–71. doi: 10.1108/02634500310458162. [ CrossRef ] [ Google Scholar ]
• Eriksson K, Kerem K, Nilsson D. Customer acceptance of internet banking in Estonia. International Journal of bank marketing. 2005; 23 (2):200–216. doi: 10.1108/02652320510584412. [ CrossRef ] [ Google Scholar ]
• Featherman MS, Pavlou PA. Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies. 2003; 59 (4):451–474. doi: 10.1016/S1071-5819(03)00111-3. [ CrossRef ] [ Google Scholar ]
• Gartner 2019. Gartner Predicts Indian Banking and Securities IT Spending to Grow 9% in 2020, available at: https://www.gartner.com/en/newsroom/press-releases/2019-06-18-gartner-predicts-indian-banking-and-securities-it-spe (accessed 21 March 2020).
• Goff BG, Boles JS, Bellenger DN, Stojack C. The influence of salesperson selling behaviors on customer satisfaction with products. Journal of Retailing. 1997; 73 (2):171–183. doi: 10.1016/S0022-4359(97)90002-6. [ CrossRef ] [ Google Scholar ]
• Harden G. E-banking comes to town: Exploring how traditional UK high street banks are meeting the challenge of technology and virtual relationships. Journal of Financial Services Marketing. 2002; 6 (4):323–332. doi: 10.1057/palgrave.fsm.4770062. [ CrossRef ] [ Google Scholar ]
• He, D., K. You, W. Li, and J. Wu. 2019. Determinants of Technology Adoption: Evidence from the Chinese Banking Industry, Emerging Markets Finance and Trade , 1–23.
• Healy M, Perry C. Comprehensive criteria to judge validity and reliability of qualitative research within the realism paradigm. Qualitative Market Research: An International Journal. 2000; 3 (3):118–126. doi: 10.1108/13522750010333861. [ CrossRef ] [ Google Scholar ]
• Hirschman, E. C. 1986. Humanistic inquiry in marketing research: philosophy, method, and criteria, Journal of Marketing Research , 237–249.
• Howcroft B, Hamilton R, Hewer P. Consumer attitude and the usage and adoption of home-based banking in the United Kingdom. International Journal of Bank Marketing. 2002; 20 (3):111–121. doi: 10.1108/02652320210424205. [ CrossRef ] [ Google Scholar ]
• Ibrahim, E.E., M. Joseph, and K.I. Ibeh. 2006. Customers’ perception of electronic service delivery in the UK retail banking sector”, International Journal of Bank Marketing, 24(7), 475–493. In Qualitative data analysis: a sourcebook of new methods . Sage publications.
• Joyce, L. 2017. Digital Bank Transformation: The Evolution of Branch Banking, available at: https://thefinancialbrand.com/67893/banking-branch-network-strategy-design/(accessed 23 January 2018).
• Karjaluoto H, Shaikh AA, Saarijärvi H, Saraniemi S. How perceptual value drives the use of mobile financial services apps? International Journal of Information Management. 2018; 47 :252–261. doi: 10.1016/j.ijinfomgt.2018.08.014. [ CrossRef ] [ Google Scholar ]
• Karjaluoto H, Shaikh AA, Leppäniemi M, Luomala R. Examining consumers’ usage intention of contactless payment systems. International Journal of Bank Marketing. 2019; 38 (2):332–351. doi: 10.1108/IJBM-04-2019-0155. [ CrossRef ] [ Google Scholar ]
• Kaur G, Sharma RD, Mahajan N. Exploring customer switching intentions through relationship marketing paradigm. International Journal of Bank Marketing. 2012; 30 (4):280–302. doi: 10.1108/02652321211236914. [ CrossRef ] [ Google Scholar ]
• King WR, He J. A meta-analysis of the technology acceptance model. Information & Management. 2006; 43 (6):740–755. doi: 10.1016/j.im.2006.05.003. [ CrossRef ] [ Google Scholar ]
• Kumar Sharma S, MadhumohanGovindaluri S. Internet banking adoption in India: structural equation modeling approach. Journal of Indian Business Research. 2014; 6 (2):155–169. doi: 10.1108/JIBR-02-2013-0013. [ CrossRef ] [ Google Scholar ]
• Kumar, A. 2019. A Refreshed ‘Digital India’ Programme will Play Critical Role in the Pursuit of $5 Trillion Economy, available at: https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/a-refreshed-digital-india-programme-will-play-critical-role-in-the-pursuit-of-5-trillion-economy/articleshow/70067053.cms?from=mdr (accessed 20 March 2020).
• Lee KC, Kang I, McKnight DH. Transfer from offline trust to key online perceptions: an empirical study. IEEE Transactions on Engineering Management. 2007; 54 (4):729–741. doi: 10.1109/TEM.2007.906851. [ CrossRef ] [ Google Scholar ]
• Luchetti, G. 2017. The future of banking will include brick-and-mortar branches, available at: https://www.scmp.com/business/banking-finance/article/2101363/future-banking-will-include-brick-and-mortar-branches(accessed 21 January 2018).
• Luo X, Li H, Zhang J, Shim JP. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decisionsupport systems. 2010; 49 (2):222–234. [ Google Scholar ]
• Lymperopoulos C, Chaniotakis IE. Branch employees’ perceptions towards implications of e-banking in Greece. International Journal of Retail & Distribution Management. 2004; 32 (6):302–311. doi: 10.1108/09590550410538006. [ CrossRef ] [ Google Scholar ]
• Machauer A, Morgner S. Segmentation of bank customers by expected benefits and attitudes. International Journal of Bank Marketing. 2001; 19 (1):6–18. doi: 10.1108/02652320110366472. [ CrossRef ] [ Google Scholar ]
• Makarem SC, Mudambi SM, Podoshen JS. Satisfaction in technology-enabled service encounters. Journal of Services Marketing. 2009; 23 (3):134–144. doi: 10.1108/08876040910955143. [ CrossRef ] [ Google Scholar ]
• Malhotra P, Singh B. An analysis of Internet banking offerings and its determinants in India. Internet Research. 2010; 20 (1):87–106. doi: 10.1108/10662241011020851. [ CrossRef ] [ Google Scholar ]
• Marakarkandy B, Yajnik N, Dasgupta C. Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM) Journal of Enterprise Information Management. 2017; 30 (2):263–294. doi: 10.1108/JEIM-10-2015-0094. [ CrossRef ] [ Google Scholar ]
• Loureiro Maria Correia, Rudiger Kaufmann H, Rabino S. Intentions to use and recommend to others: an empirical study of online banking practices in Portugal and Austria. Online Information Review. 2014; 38 (2):186–208. doi: 10.1108/OIR-01-2012-0100. [ CrossRef ] [ Google Scholar ]
• Marous, J. 2013. Migrating Banking Customers to Digital Channels, available at: https://thefinancialbrand.com/37287/migrating-banking-customers-to-digital/(accessed 15 January 2018).
• Martins C, Oliveira T, Popovič A. Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management. 2014; 34 (1):1–13. doi: 10.1016/j.ijinfomgt.2013.06.002. [ CrossRef ] [ Google Scholar ]
• Mayring, P. 2000. Qualitative content analysis. Qualitative Social Research Forum 1 (2):20. https://www.qualitative-research.net/index.php/fqs/article/view/1089/2385 .
• McKinsey Consumer Insight 2016. Reimagining the bank branch for the digital era, available at: https://www.mckinsey.com/industries/financial-services/our-insights/reimagining-the-bank-branch-for-the-digital-era(accessed 10 December 2017).
• Miles, M.B., and A.M. Huberman. 1984. Qualitative data analysis: A sourcebook of new methods . London: Sage.
• Montazemi AR, Qahri-Saremi H. Factors affecting adoption of online banking: A meta-analytic structural equation modeling study. Information & Management. 2015; 52 (2):210–226. doi: 10.1016/j.im.2014.11.002. [ CrossRef ] [ Google Scholar ]
• Mukherjee A, Nath P. A model of trust in online relationship banking. International Journal of Bank Marketing. 2003; 21 (1):5–15. doi: 10.1108/02652320310457767. [ CrossRef ] [ Google Scholar ]
• Myers JB, Pickersgill AD, Van Metre ES. Steering customers to the right channels. McKinsey Quarterly. 2004; 4 (3):36–47. [ Google Scholar ]
• Nath R, Bhal KT, Kapoor GT. Factors influencing IT adoption by bank employees: An extended TAM approach. Vikalpa. 2013; 38 (4):83–96. doi: 10.1177/0256090920130406. [ CrossRef ] [ Google Scholar ]
• Normalini MK, Ramayah T. Trust in internet banking in Malaysia and the moderating influence of perceived effectiveness of biometrics technology on perceived privacy and security. Journal of Management Sciences. 2017; 4 (1):3–26. [ Google Scholar ]
• Osho GS. How technology is breaking traditional barriers in the banking industry: Evidence from financial management perspective. European Journal of Economics, Finance, and Administrative Sciences. 2008; 11 (3):15–21. [ Google Scholar ]
• Pandey, N. 2018. Only 16% of rural users access Internet for digital payments: Report, available at: https://www.livemint.com/Politics/PhY0kTxoJqq6U9GISIpSaK/Only-16-of-rural-users-access-Internet-for-digital-payments.html(accessed 5 January 2018).
• Patel KJ, Patel HJ. Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing. 2018; 36 (1):147–169. doi: 10.1108/IJBM-08-2016-0104. [ CrossRef ] [ Google Scholar ]
• Patricio L, Fisk RP, Falcao e Cunha J. Improving satisfaction with bank service offerings: measuring the contribution of each delivery channel. Managing Service Quality: An International Journal. 2003; 13 (6):471–482. doi: 10.1108/09604520310506531. [ CrossRef ] [ Google Scholar ]
• Pikkarainen T, Pikkarainen K, Karjaluoto H, Pahnila S. Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research. 2004; 14 (3):224–235. doi: 10.1108/10662240410542652. [ CrossRef ] [ Google Scholar ]
• Poon WC. Users’ adoption of e-banking services: the Malaysian perspective. Journal of Business & Industrial Marketing. 2007; 23 (1):59–69. doi: 10.1108/08858620810841498. [ CrossRef ] [ Google Scholar ]
• Rajan, R. 2015. Hindi and Other Indian Languages can act as a bridge between Banker and Customer: Dr Raghuram G. Rajan, Governor, Reserve Bank of India, June 19, 2015, available at: https://rbi.org.in/scripts/BS_PressReleaseDisplay.aspx?prid=34227 .
• Rawashdeh A. Factors affecting adoption of internet banking in Jordan: Chartered accountant’s perspective. International Journal of Bank Marketing. 2015; 33 (4):510–529. doi: 10.1108/IJBM-03-2014-0043. [ CrossRef ] [ Google Scholar ]
• Reserve Bank of India 2017. Reserve Bank of India Annual Report 2016–17, available at: https://www.thehindubusinessline.com/multimedia/archive/03196/RBI_Annual_Report__3196461a.PDF (accessed 6 January 2018).
• Rotchanakitumnuai S, Speece M. Barriers to Internet banking adoption: A qualitative study among corporate customers in Thailand. International Journal of Bank Marketing. 2003; 21 (6/7):312–323. doi: 10.1108/02652320310498465. [ CrossRef ] [ Google Scholar ]
• Roy SK, Balaji MS, Kesharwani A, Sekhon H. Predicting Internet banking adoption in India: A perceived risk perspective. Journal of Strategic Marketing. 2017; 25 (5–6):418–438. doi: 10.1080/0965254X.2016.1148771. [ CrossRef ] [ Google Scholar ]
• Safeena, R., A. Kammani, and H. Date. 2014. Assessment of internet banking adoption: An empirical analysis. Arabian Journal for Science and Engineering 39 (2): 837–849.
• Sánchez-Torres JA, Canada FJA, Sandoval AV, Alzate JAS. E-banking in Colombia: factors favouring its acceptance, online trust and government support. International Journal of Bank Marketing. 2018; 36 (1):170–183. doi: 10.1108/IJBM-10-2016-0145. [ CrossRef ] [ Google Scholar ]
• Sarel D, Marmorstein H. Migrating customers to new distribution channels: The role of communication. Journal of Financial Services Marketing. 2002; 6 (3):254–266. doi: 10.1057/palgrave.fsm.4770056. [ CrossRef ] [ Google Scholar ]
• Sarel D, Marmorstein H. Marketing online banking services: The voice of the customer. Journal of Financial Services Marketing. 2003; 8 (2):106–118. doi: 10.1057/palgrave.fsm.4770111. [ CrossRef ] [ Google Scholar ]
• Sathye M. Adoption of Internet banking by Australian consumers: an empirical investigation. International Journal of Bank Marketing. 1999; 17 (7):324–334. doi: 10.1108/02652329910305689. [ CrossRef ] [ Google Scholar ]
• Schofield, M. and S. Chew. 2013. Future of the bank branch in Asia: Redesigning footprint and format, available at: http://www.bain.com/publications/articles/future-of-the-bank-branch-in-asia-redesigning-footprint-and-format.aspx(accessed 6 January 2018).
• Semeijn J, Van Riel AC, Van Birgelen MJ, Streukens S. E-services and offline fulfilment: how e-loyalty is created. Managing Service Quality: An International Journal. 2005; 15 (2):182–194. doi: 10.1108/09604520510585361. [ CrossRef ] [ Google Scholar ]
• Shaikh AA, Alharthi MD, Alamoudi HO. Examining key drivers of consumer experience with (non-financial) digital services-An exploratory study. Journal of Retailing and Consumer Services. 2020; 55 :102073. doi: 10.1016/j.jretconser.2020.102073. [ CrossRef ] [ Google Scholar ]
• Shaikh AA, Karjaluoto H. Some misconceptions concerning digital banking and alternative delivery channels. International Journal of E-Business Research. 2016; 12 (3):1–16. doi: 10.4018/IJEBR.2016070101. [ CrossRef ] [ Google Scholar ]
• Shetty, S. 2017. Gartner Says The Transition of The Indian Banking Sector To Cashless Society is Driving Increased Technology Investments, available at: https://www.gartner.com/newsroom/id/3827164 (accessed 10 January 2018).
• Singh J, Kaur G. Customer satisfaction and universal banks: an empirical study. International Journal of Commerce and Management. 2011; 21 (4):327–348. doi: 10.1108/10569211111189356. [ CrossRef ] [ Google Scholar ]
• Sinha I, Mukherjee S. Acceptance of technology, related factors in use of off branch e-banking: an Indian case study. The Journal of High Technology Management Research. 2016; 27 (1):88–100. doi: 10.1016/j.hitech.2016.04.008. [ CrossRef ] [ Google Scholar ]
• Sukkar AA, Hasan H. Toward a model for the acceptance of internet banking in developing countries. Information Technology for Development. 2005; 11 (4):381–398. doi: 10.1002/itdj.20026. [ CrossRef ] [ Google Scholar ]
• Susanto A, Lee H, Zo H, Ciganek AP. User acceptance of Internet banking in Indonesia: initial trust formation. Information Development. 2013; 29 (4):309–322. doi: 10.1177/0266666912467449. [ CrossRef ] [ Google Scholar ]
• Szopiński TS. Factors affecting the adoption of online banking in Poland. Journal of BusinessResearch. 2016; 69 (11):4763–4768. [ Google Scholar ]
• Takieddine S, Sun J. Internet banking diffusion: A country-level analysis. Electronic Commerce Research and Applications. 2015; 14 (5):361–371. doi: 10.1016/j.elerap.2015.06.001. [ CrossRef ] [ Google Scholar ]
• Tang, D. 2016. Branch transformation in the digital era, available at: https://www.ibm.com/industries/banking-financial-markets/resources/branch-transformation-digital-era/ (accessed 13 March 2018).
• Taylor S, Todd PA. Understanding information technology usage: A test of competing models. Information Systems Research. 1995; 6 (2):144–176. doi: 10.1287/isre.6.2.144. [ CrossRef ] [ Google Scholar ]
• The Economic Times 2018. Acute urban-rural divide in Internet penetration in India: Report, available at: https://economictimes.indiatimes.com/tech/internet/acute-urban-rural-divide-in-internet-penetration-in-india-report/articleshow/62997468.cms(accessed 24 December 2017).
• The Economic Times. 2018. Number of Adult Indians with Bank Accounts Rises to 80%, available at: https://economictimes.indiatimes.com/industry/banking/finance/banking/number-of-adult-indians-with-bank-accounts-rises-to-80/articleshow/63838930.cms(accessed 21 March 2020).
• Tiwari R. Contribution of Cyber Banking towards Digital India: A Way Forward. Khoj. An International Peer Reviewed Journal of Geography. 2019; 6 (1):46–52. doi: 10.5958/2455-6963.2019.00005.5. [ CrossRef ] [ Google Scholar ]
• Tobbin P. Towards a model of adoption in mobile banking by the unbanked: A qualitative study. Info. 2012; 14 (5):74–88. doi: 10.1108/14636691211256313. [ CrossRef ] [ Google Scholar ]
• Toit, G. and M. Burns. 2016. Customer Loyalty in Retail Banking: Global Edition 2016, available at: http://www.bain.com/publications/articles/customer-loyalty-in-retail-banking-2016.aspx(accessed 10 January 2018).
• Vater, D., J. Engelhardt, J. Fielding, and R. Hatherall. 2019. As Banks Pursue Digital Transformation, Many Struggle to Profit from It, available at: https://www.bain.com/insights/as-banks-pursue-digital-transformation-many-struggle-to-profit-from-it/ (accessed 24 March 2020).
• Waite K, Harrison T. Consumer expectations of online information provided by bank websites. Journal of Financial Services Marketing. 2002; 6 (4):309–322. doi: 10.1057/palgrave.fsm.4770061. [ CrossRef ] [ Google Scholar ]
• Walker RH, Johnson LW. Why consumers use and do not use technology-enabled services. Journal of Services Marketing. 2006; 20 (2):125–135. doi: 10.1108/08876040610657057. [ CrossRef ] [ Google Scholar ]
• Wang YS, Wang YM, Lin HH, Tang TI. Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management. 2003; 14 (5):501–519. doi: 10.1108/09564230310500192. [ CrossRef ] [ Google Scholar ]
• Wentzel JP, Diatha KS, Yadavalli VSS. An application of the extended Technology Acceptance Model in understanding technology-enabled financial service adoption in South Africa. Development Southern Africa. 2013; 30 (4–5):659–673. doi: 10.1080/0376835X.2013.830963. [ CrossRef ] [ Google Scholar ]
• World Bank. 2017. The Global Findex Database- Measuring Financial Inclusion and the Fintech Revolution”, available at: https://globalfindex.worldbank.org/sites/globalfindex/files/2018-04/2017%20Findex%20full%20report_0.pdf(accessed 21 March 2020).
• Yap KB, Wong DH, Loh C, Bak R. Offline and online banking–where to draw the line when building trust in e-banking? International Journal of Bank Marketing. 2010; 28 (1):27–46. doi: 10.1108/02652321011013571. [ CrossRef ] [ Google Scholar ]
• Yiga C, Cha KJ. Toward understanding the importance of trust in influencing Internet banking adoption in Uganda. Information Development. 2014; 32 (3):622–636. doi: 10.1177/0266666914563359. [ CrossRef ] [ Google Scholar ]
• Yu, D. and J. Hughes. 2016., Struggle for Banks: Migrate Customers to Digital, available at: http://news.gallup.com/businessjournal/196778/struggle-banks-migrating-customers—digital.aspx (accessed 10 November 2017).