research paper related to banking

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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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Utilization of artificial intelligence in the banking sector: a systematic literature review

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• Published: 11 August 2022
• Volume 28 , pages 835–852, ( 2023 )

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• Omar H. Fares   ORCID: orcid.org/0000-0003-0950-0661 1 ,
• Irfan Butt 1 &
• Seung Hwan Mark Lee 1  

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This study provides a holistic and systematic review of the literature on the utilization of artificial intelligence (AI) in the banking sector since 2005. In this study, the authors examined 44 articles through a systematic literature review approach and conducted a thematic and content analysis on them. This review identifies research themes demonstrating the utilization of AI in banking, develops and classifies sub-themes of past research, and uses thematic findings coupled with prior research to propose an AI banking service framework that bridges the gap between academic research and industry knowledge. The findings demonstrate how the literature on AI and banking extends to three key areas of research: Strategy, Process, and Customer. These findings may benefit marketers and decision-makers in the banking sector to formulate strategic decisions regarding the utilization and optimization of value from AI technologies in the banking sector. This study also provides opportunities for future research.

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Introduction

Digital innovations in the modern banking landscape are no longer discretionary for financial institutions; instead, they are becoming necessary for financial institutions to cope with an increasingly competitive market and changing customer expectations (De Oliveira Santini, 2018 ; Eren, 2021 ; Hua et al., 2019 ; Rajaobelina and Ricard, 2021 ; Valsamidis et al., 2020 ; Yang, 2009 ). In the era of modern banking, many new digital technologies have been driven by artificial intelligence (AI) as the key engine (Dobrescu and Dobrescu, 2018 ), leading to innovative disruptions of banking channels (e.g., automated teller machines, online banking, mobile banking), services (e.g., imaging of checks, voice recognition, chatbots), and solutions (e.g., AI investment advisors and AI credit selectors).

The application of AI in banking is across the board, with uses in the front office (voice assistants and biometrics), middle office (anti-fraud risk monitoring and complex legal and compliance workflows), and back office (credit underwriting with smart contracts infrastructure). Banks are expected to save $447 billion by 2023, by employing AI applications. Almost 80% of the banks in the USA are cognizant of the potential benefits offered by AI (Digalaki, 2022 ). Indeed, the emergence of AI has generated a wealth of opportunities and challenges (Malali and Gopalakrishnan, 2020 ). In the banking context, the use of AI has led to more seamless sales and has guided the development of effective customer relationship management systems (Tarafdar et al., 2019 ). While the focus in the past was on the automation of credit scoring, analyses, and the grants process (Mehrotra, 2019 ), capabilities evolved to support internal systems and processes as well (Caron, 2019 ).

The term AI was first used in 1956 by John McCarthy (McCarthy et al., 1956 ); it refers to systems that act and think like humans in a rational way (Kok et al., 2009 ). In the aftermath of the dot com bubble in 2000, the field of AI shifted toward Web 2.0. era in 2005, and the growth of data and availability of information encouraged more research in AI and its potential (Larson, 2021 ). More recently, technological advancements have opened the doors for AI to facilitate enterprise cognitive computing, which involves embedding algorithms into applications to support organizational processes (Tarafdar et al., 2019 ). This includes improving the speed of information analysis, obtaining more accurate and reliable data outputs, and allowing employees to perform high-level tasks. In recent years, AI-based technologies have been shown to be effective and practical. However, many corporate executives still lack knowledge regarding the strategic utilization of AI in their organizations. For instance, Ransbotham et al. ( 2017 ) found that 85% of business executives viewed AI as a key tool for providing businesses with a sustainable competitive advantage; however, only 39% had a strategic plan for the use of AI, due to the lack of knowledge regarding implementation of AI for their organizations.

Here, we systematically analyze the past and current state of AI and banking literature to understand how it has been utilized within the banking sector historically, propose a service framework, and provide clear future research opportunities. In the past, a limited number of systematic literature reviews have studied AI within the management discipline (e.g., Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ). However, the current literature lacks either research scope and depth, and/or industry focus. In response, we seek to differentiate our study from prior reviews by providing a specific focus on the banking sector and a more comprehensive analysis involving multiple modes of analysis.

In light of this, we aim to address the following research questions:

What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

How does AI impact the customer's journey process in the banking sector, from customer acquisition to service delivery?

What are the current research deficits and future directions of research in this field?

Methodology

Selection of articles.

Adhering to the best practices for conducting a Systematic Literature Review (SLR) (see Khan et al., 2003 ; Tranfield et al, 2003 ; Xiao and Watson, 2019 ), we began by selecting the appropriate database and identifying keywords, based on an in-depth review of the literature. Research papers were extracted from Web of Science (WoS) and Scopus. These databases were selected to complement one another and provide access to scholarly articles (Mongeon and Paul-Hus, 2016 ); this was also the first step in ensuring the inclusion of high-quality articles (Harzing and Alakangas, 2016 ). The following query was used to search the title, abstract, and keywords: “Artificial intelligence OR machine learning OR deep learning OR neural networks OR Intelligent systems AND Bank AND consumer OR customer OR user.” The keywords were selected, based on prior literature review, with the goal of covering various business functions, especially focusing on the banking sector (Loureiro et al., 2020 ; Verma et al., 2021 ; Borges et al., 2020 ; Bavaresco et al., 2020 ). The initial search criteria yielded 11,684 papers. These papers were then filtered by “English,” “article only” publications, and using the subject area filter of “Management, Business Finance, accounting and Business,” which resulted in 626 papers.

In this study, we used the preferred reporting method for systematic reviews and meta-analyses (PRISMA) to ensure that we follow the systematic approach and track the flow of data across different stages of the SLR (Moher et al., 2009 ). After extracting the articles, each of the 626 papers was given a distinctive ID number to help differentiate the papers; the ID number was maintained throughout the analysis process. The data were then organized using the following columns: “ID number,” “database source,” “Author,” “title,” “Abstract,” “keywords,” “Year,” Australian Business Deans Council (ABDC) Journals, “and keyword validation columns.”

The exclusion of papers was done systematically in the following manner: a) All duplicate papers in the database were eliminated (105 duplicates); b) as a second quality check, papers not published in ABDC journals (163 papers) were omitted to ensure a quality standard for inclusion in the review,Query a practice consistent with other recent SLRs (Goyal and Kumar, 2021 ; Nusair et al., 2019 ; Pahlevan-Sharif et al., 2019 ); c) in order to ensure the relevance of articles included, and following our research objectives, we excluded non-consumer-related papers, searching for consumers (consumer, customer, user) in the title, abstract, and keywords; this resulted in the removal of 314 papers; d) for the remaining 48 papers, a relevance check was manually conducted to determine whether the papers were indeed related to AI and banking. Papers that specifically focused on the technical computational process of AI were removed (4 papers). This process resulted in the selection of 44 articles for subsequent analyses.

Thematic analysis

A thematic analysis classifies the topics and subtopics being researched. It is a method for identifying, analyzing, and reporting patterns within data (Boyatzis, 1998 ). We followed Chatha and Butt ( 2015 ) to classify the articles into themes and sub-themes using manual coding. Second, we employed the Leximancer software to supplement the manual classification process. The use of these two approaches provides additional validity and quality to the research findings.

Leximancer is a text-mining software that provides conceptual and relational information by identifying concept occurrences and co-occurrences (Leximancer, 2019 ). After uploading all the 44 papers onto Leximancer, we added “English” to the stoplist, which removed words such as “or/and/like” that are not relevant to developing themes. We manually removed irrelevant filler words, such as “pp.,” “Figure,” and “re.” Finally, our results consisted of two maps: a) a conceptual map wherein central themes and concepts are identified, and b) a relational cloud map where a network of connections and relationships are drawn among concepts.

Thematic map

RQ 1: What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

We began with a deductive approach to categorize articles into predetermined themes for the theme identification process. We then employed an inductive approach to identify the sub-themes and provide context for the primary themes (See Fig. 1 ). The procedure for determining the primary themes included, a) reviewing previous related systematic literature reviews (Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ), b) identifying keywords and developing codes (themes) from selected papers; and c) reviewing titles, abstracts, and full papers, if needed, to identify appropriate allocation within these themes. Three primary themes were curated from the process: Strategy, Processes, and Customers (see Fig.  2 ).

Themes by timeline

In the Strategy theme (21 papers), early research shows the potential uses and adoption of AI from an organizational perspective (e.g., Akkoç, 2012 ; Olson et al., 2012 ; Smeureanu et al., 2013 ). Data mining (an essential part of AI) has been used to predict bankruptcy (Olson et al., 2012 ) and to optimize risk models (Akkoç, 2012 ). The increasing use of AI-driven tools to drive organizational effectiveness creates greater business efficiency opportunities for financial institutions, as compared to traditional modes of strategizing and risk model development. The sub-theme Organizational use of AI (14 papers) covers a range of current activities wherein banks use AI to drive organizational value. These organizational uses include the use of AI to drive business strategies and internal business activities. Medhi and Mondal ( 2016 ) highlighted the use of an AI-driven model to predict outsourcing success. Our findings indicate the effectiveness of AI tools in driving efficient organizational strategies; however, there remain several challenges in implementing AI technologies, including the human resources aspect and the organizational culture to allow for such efficiencies (Fountain et al., 2019 ). More recently, there has been a noticeable focus on discussing some of the challenges associated with AI implementation in banking institutions (e.g., Jakšič and Marinč, 2019 ; Mohapatra, 2020 ). The sub-theme Challenges with AI (three papers) covers a range of challenges that organizations face, including the integration of AI in their organizations. Mohapatra ( 2020 ) characterizes some of the key challenges related to human–machine interactions to allow for the sustainable implementation of AI in banking. While much of the current research has focused on technology, our findings indicate that one of the main areas of opportunity in the future is related to adoption and integration. The sub-theme AI and adoption in financial institutions (six papers) covered a range of topics regarding motivation, and barriers to the adoption of AI technology from an organizational standpoint. Fountain et al. ( 2019 ) conceptually highlighted some barriers to organizational adoption, including workers’ fear, company culture, and budget constraints. Overall, in the Strategy theme, organizational uses of AI seemed to be the most prominent, which highlights the consistent focus on technology development compared with technology implementation. However, the literature remains limited in terms of discussions related to the organizational challenges associated with AI implementation.

In the Processes theme (34 papers), after the dot com bubble and with the emergence of Web 2.0, research on AI in the banking sector started to emerge. This could have been triggered by the suggested use of AI to predict stock market movements and stock selection (Kim and Lee, 2004 ; Tseng, 2003 ). At this stage, the literature on AI in the banking sector was related to its use in credit and loan analysis (Baesens et al., 2005 ; Ince and Aktan, 2009 ; Kao et al., 2012 ; Khandani et al., 2010 ). In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021 ). Baesens et al. ( 2005 ) utilized a neural network approach to better predict loan defaults and early repayments. Ince and Aktan ( 2009 ) used a data mining technique to analyze credit scores and found that the AI-driven data mining approach was more effective than traditional methods. Similarly, Khandani et al. ( 2010 ) found machine-learning-driven models to be effective in analyzing consumer credit risk. The sub-theme, AI and credit (15 papers), covers the use of AI technology, such as machine learning and data mining, to improve credit scoring, analysis, and granting processes. For instance, Alborzi and Khanbabaei ( 2016 ) examined the use of data mining neural network techniques to develop a customer credit scoring model. Post-2013, there has been a noticeable increase in investigating how AI improves processes that go beyond credit analysis. The sub-theme AI and services (20 papers) covers the uses of AI for process improvement and enhancement. These process-related uses of technology include institutional uses of technology to improve internal service processes. For example, Soltani et al. ( 2019 ) examined the use of machine learning to optimize appointment scheduling time, and reduce service time. Overall, regarding the process theme, our findings highlight the usefulness of AI in improving banking processes; however, there remains a gap in practical research regarding the applied integration of technology in the banking system. In addition, while there is an abundance of research on credit risk, the exploration of other financial products remains limited.

In the Customer theme (26 papers), we uncovered the increasing use of AI as a methodological tool to better understand customer adoption of digital banking services. The sub-theme AI and Customer adoption (11 papers) covers the use of AI as a methodological tool to investigate customers’ adoption of digital banking technologies, including both barriers and motivational factors. For example, Arif et al. ( 2020 ) used a neural network approach to investigate barriers to internet-banking adoption by customers. Belanche et al. ( 2019 ) investigate factors related to AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examine the drivers of the usage of AI-enabled mobile banking services. In addition, bank marketers have found an opportunity to use AI to better segment, target, and position their banking products and services. The sub-theme, AI and marketing (nine papers), covers the use of AI for different marketing activities, including customer segmentation, development of marketing models, and delivery of more effective marketing campaigns. For example, Smeureanu et al. ( 2013 ) proposed a machine learning technique to segment banking customers. Schwartz et al. ( 2017 ) utilized an AI-based method to examine the resource allocation in targeted advertisements. In recent years, there has been a noticeable trend in investigating how AI shapes customer experience (Soltani et al., 2019 ; Trivedi, 2019 ). The sub-theme of AI and customer experience (Papers 11) covers the use of AI to enhance banking experience and services for customers. For example, Trivedi ( 2019 ) investigated the use of chatbots in banking and their impact on customer experience.

Table 1 highlights the number of papers included in the themes and sub-themes. Overall, the papers related to Processes (77%) were the most frequently occurring, followed by Customer (59%) and Strategy-based (48%) papers. From 2013 onward, there was an increase in the inter-relation between all three areas of Strategy, Processes, and Customers. Since 2016, there has been a surge in research linking the themes of Processes and Customers. More recently, since 2017, papers combining Customers with Strategy have become more frequent.

Leximancer analysis

A Leximancer analysis was conducted on all the papers included in the study. This resulted in two major classifications and 56 distinct concepts. Here, a “concept” refers to a combination of closely related words. When referring to “concept co-occurrence,” we refer to the total number of times two concepts appear together. In comparison, the word association percentage refers to the conditional probability that two concepts will appear side-by-side.

Conceptual and relational analyses

Conceptual analysis refers to the analysis of data based on word frequency and word occurrence, whereas relational analysis refers to the analysis that draws connections between concepts and captures the co-occurrences between words (Leximancer, 2019 ). As Fig.  3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme. The concept “customer” appeared 2,231 times across all papers. For the concept “customer,” some of the key concept associations include satisfaction (324 co-occurrences and 64% word association), service (185 co-occurrences and 43% word association), and marketing (86 co-occurrences and 42% word association). This may imply the importance of utilizing AI in improving customer service and satisfaction, and in marketing to retain and grow the customer base. For instance, Trivedi ( 2019 ) examined the factors affecting chatbot satisfaction and found that information, system, and service quality, all have a significant positive association with it. Ekinci et al. ( 2014 ) proposed a customer lifetime value model, supported by a deep learning approach, to highlight key indicators in the banking sector. Xu et al. ( 2020 ) examined the effects of AI versus human customer service, and found that customers are more likely to use AI for low-complexity tasks, whereas a human agent is preferred for high-complexity tasks. It is worth noting that most of the research related to the customer theme has utilized a quantitative approach, with limited qualitative papers (i.e., four papers) in recent years.

Concept map of content of all papers included in the study

Not surprisingly, the second most prominent concept is “banking,” which is expected as it is the sector that we are examining. The concept “banking” appeared 1,033 times across all the papers. In the “banking” concept, some of the key concept associations include mobile (248 co-occurrences and 88% word association), internet (152 co-occurrences and 82% word association), adoption (220 co-occurrences and 50% word association), and acceptance (71 co-occurrences and 42% word association). This implies the importance of utilizing AI in mobile- and internet-banking research, along with inquiries related to the adoption and acceptance of AI for such uses. Belanche et al. ( 2019 ) proposed a research framework to provide a deeper understanding of the factors driving AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities and found that the need for services, attitude toward AI, relative advantage, and trust had a significant positive association with the usage of AI-enabled mobile banking services.

Figure  4 highlights the concept associations and draws connections between concepts. The identification and classification of themes and sub-themes using the deductive method in thematic analysis, and the automated approach using Leximancer, provide a reliable and detailed overview of the prior literature.

Cloud map of content of all papers included in the study

Customer credit solution application-service blueprint

RQ 2: How does AI impact the banking customer’s journey?

A service blueprint is a method that conceptualizes the customer journey while providing a framework for the front/back-end and support processes (Shostack, 1982 ). For a service blueprint to be effective, the core focus should be on the customer, and steps should be developed based on data and expertise (Bitner et al., 2008 ). As previously discussed, one of the key research areas, AI and banking, relates to credit applications and granting decisions; these are processes that directly impact customer accessibility and acquisition. Here, we develop and propose a Customer Credit Solution Application-Service Blueprint (CCSA) based on our earlier analyses.

Not only was the proposed design developed but the future research direction was also extracted from the articles included in this study. We also validated the framework through direct consultation with banking industry professionals. The CCSA model allows marketers, researchers, and banking professionals to gain a deeper understanding of the customer journey, understand the role of AI, provide an overview of future research directions, and highlight the potential for future growth in this field. As seen in Fig.  5 , we divided the service blueprint into four distinct segments: customer journey, front-stage, back-stage, and support processes. The customer journey is the first step in building a customer-centric blueprint, wherein we highlight the steps taken by customers to apply for a credit solution. The front-stage refers to how the customer interacts with a banking touchpoint (e.g., chatbots). Back-stage actions provide support to customer-facing front-stage actions. Support processes aid in internal organizational interactions and back-stage actions. This section lays out the steps for applying for credit solutions online and showcases the integration and use of AI in the process, with examples from the literature.

Customer credit solution application journey

Acquire customer

We begin from the initial step of customer acquisition, and proceed to credit decision, and post-decision (Broby, 2021 ). In the acquisition step, customers are targeted with the goal of landing them on the website and converting them to active customers. The front-stage includes targeted ads , where customers are exposed to ads that are tailored for them. For instance, Schwartz et al. ( 2017 ) utilized a multi-armed bandit approach for a large retail bank to improve customer acquisition, and proposed a method that allows bank marketers to maintain the balance between learning from advertisement data and optimizing advertisement investment. At this stage, the support processes focus on integrating AI as a methodological tool to better understand customers' banking adoption behaviors, in combination with utilizing machine learning to evaluate and update segmentation activities. The building block at this stage, is understanding the factors of online adoption. Sharma et al. ( 2017 ) used the neural network approach to investigate the factors influencing mobile banking adoption. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities. Markinos and Daskalaki ( 2017 ) used machine learning to classify bank customers based on their behavior toward advertisements.

Visit bank’s website & apply for a credit solution

At this stage, banking institutions aim to convert website traffic to credit solution applicants. The integration of robo-advisors will help customers select a credit solution that they can best qualify for, and which meets their banking needs. The availability of a robo-advisor can enhance the service offering, as it can help customers with the appropriate solution after gathering basic personal financial data and validating it instantly with credit reporting agencies. Trivedi ( 2019 ) found that information, system, and service quality are key to ensuring a seamless customer experience with the chatbot, with personalization moderating the constructs. Robo-advisors have task-oriented features (e.g., checking bank accounts) coupled with problem-solving features (e.g., processing credit applications). Following this, the data collected will be consistently examined through the use of machine learning to improve the offering and enhance customer experience. Jagtiani and Lemieux ( 2019 ) used machine learning to optimize data collected through different channels, which helps arrive at appropriate and inclusive credit recommendations. It is important to note that while the proposed process provides immense value to customers and banking institutions, many customers are hesitant to share their information; thus, trust in the banking institution is key to enhancing customer experience.

Receive a decision

After the data have been collected through the online channel, data mining and machine learning will aid in the analysis and provide optimal credit decisions. At this stage, the customer receives a credit decision through the robo-advisor. The traditional approaches for credit decisions usually take up to two weeks, as the application goes to the advisory network, then to the underwriting stage, and finally back to the customer. However, with the integration of AI, the customer can save time and be better informed by receiving an instant credit decision, allowing an increased sense of empowerment and control. The process of arriving at such decisions should provide a balance between managing organizational risk, maximizing profit, and increasing financial inclusion. For instance, Khandani et al. ( 2010 ) utilized machine learning techniques to build a model predicting customers' credit risk. Koutanaei et al. ( 2015 ) proposed a data mining model to provide more confidence in credit scoring systems. From an organizational risk standpoint, Mall ( 2018 ) used a neural network approach to examine the behavior of defaulting customers, so as to minimize credit risk, and increase profitability for credit-providing institutions.

Customer contact call center

At this stage, we outline the relationship between humans and AI. As Xu et al. ( 2020 ) found that customers prefer humans for high-complexity tasks, the integration of human employees for cases that require manual review is vital, as AI can make errors or misevaluate one of the C's of credit (Baiden, 2011 ). While AI provides a wealth of benefits for customers and organizations, we refer to Jakšič and Marinč's ( 2019 ) discussion that relationship banking still plays a key role in providing a competitive advantage for financial institutions. The integration of AI at this stage can be achieved by optimizing banking channels. For instance, banking institutions can optimize appointment scheduling time and reduce service time through the use of machine learning, as proposed by Soltani et al. ( 2019 ).

General discussion

Researchers have recognized the viable use of AI to provide enhanced customer service. As discussed in the CCSA service advice, facilities, such as robo-advisors, can aid in product selection, application for banking solutions, and time-saving in low-complexity tasks. As AI has been shown to be an effective tool for automating banking processes, improving customer satisfaction, and increasing profitability, the field has further evolved to examine issues pertaining to strategic insights. Recent research has been focused on investigating the use of AI to drive business strategies. For instance, researchers have examined the use of AI to simplify internal audit reports and evaluate strategic initiatives (Jindal, 2020 ; Muñoz-Izquierdo et al., 2019 ). The latest research also highlights the challenges associated with AI, whether from the perspective of implementation, culture, or organizational resistance (Fountain et al., 2019 ). Moreover, one of the key challenges uncovered in the CCSA is privacy and security concerns of customers in sharing their information. As AI technologies continue to grow in the banking sector, the privacy-personalization paradox has become a key research area that needs to be examined.

In addition, the COVID-19 pandemic has brought on a plethora of challenges in the implementation of AI in the banking sector. Although banks' interest in AI technologies remains high, the reduction in revenue has resulted in a decrease in short-term investment in AI technologies (Anderson et al., 2021 ). Wu and Olson ( 2020 ) highlight the need for banking institutions to continue investing in AI technologies to reduce future risks and enhance the integration between online and offline channels. From a customer perspective, COVID-19 has led to an uptick in the adoption of AI-driven services such as chatbots, E-KYC (Know your client), and robo-advisors (Agarwal et al., 2022 ).

Future research directions

RQ 3: What are the current research deficits and the future directions of research in this field?

Tables 2 , 3 , and 4 provide a complete list of recommendations for future research. These recommendations were developed by reviewing all the future research directions included in the 44 papers. We followed Watkins' ( 2017 ) rigorous and accelerated data reduction (RADaR) technique, which allows for an effective and systematic way to analyze and synthesize calls for future research (Watkins, 2017 ).

Regarding strategy, as AI continues to grow in the banking industry, financial institutions need to examine how internal stakeholders perceive the value of embracing AI, the role of leadership, and multiple other variables that impact the organizational adoption of AI. Therefore, we recommend that future research investigate the different factors (e.g., leadership role) that impact the organizational adoption of AI technologies. In addition, as more organizations use and accept AI, internal challenges emerge (Jöhnk et al., 2021 ). Thus, we recommend examining the different organizational challenges (e.g., organizational culture) associated with AI adoption.

Regarding processes, AI and credit is one of the areas that has been extensively explored since 2005 (Bhatore et al., 2020 ). We recommend expanding beyond the currently proposed models and challenging the underlying assumptions by exploring new aspects of risks presented with the introduction of AI technologies. In addition, we recommend the use of more practical case studies to validate new and existing models. Additionally, the growth of AI has evoked further exploration of how internal processes can be improved (Akerkar, 2019 ). For instance, we suggest investigating AI-driven models with other financial products/solutions (e.g., investments, deposit accounts, etc.).

Regarding customers, the key theories mentioned in the research papers included in the study are the Technology Acceptance Model (TAM) and diffusion of innovation theories (Anouze and Alamro, 2019 ; Azad, 2016 ; Belanche et al., 2019 ; Payne et al., 2018 ; Sharma et al., 2015 , 2017 ). However, as customers continue to become accustomed to AI, it may be imperative to develop theories that go beyond its acceptance and adoption. Thus, we recommend investigating different variables (e.g., social influence and user trends) and methods (e.g., cross-cultural studies) that impact customers' relationship with AI. The gradual shift toward its customer-centric utilization has prompted the exploration of new dimensions of AI that influence customer experience. Going forward, it is important to understand the impact of AI on customers and how it can be used to improve customer experience.

Limitations and implications

This study had several limitations. During our inclusion/exclusion criteria, it is plausible that some AI/banking papers may have been missed because of the specific keywords used to curate our dataset. In addition, articles may have been missed due to the time when the data were collected, such as Manrai and Gupta ( 2022 ), who examined investors' perceptions of robo-advisors. Second, regarding theme identification, there may be a potential bias toward selecting themes, which may lead to misclassification. In addition, we acknowledge that the papers were extracted only from the WoS and Scopus databases, which may limit our access to certain peer-reviewed outlets.

This research provides insights for practitioners and marketers in the North American banking sector. To assist in the implementation of AI-based decision-making, we encourage banking professionals to consider further refining their use of AI in the credit scoring, analysis, and granting processes to minimize risk, reduce costs, and improve customer experience. However, in doing so, we recommend using AI not only to improve internal processes but also as a tool (e.g., chatbots) to improve customer service for low-complexity tasks, thereby directing employees' efforts to other business-impacting activities. Moreover, we recommend using AI as a marketing segmentation tool to target customers for optimal solutions.

This study systematically reviewed the literature (44 papers) on AI and banking from 2005 to 2020. We believe that our findings may benefit industry professionals and decision-makers in formulating strategic decisions regarding the different uses of AI in the banking sector, and optimizing the value derived from AI technologies. We advance the field by providing a more comprehensive outlook specific to the area of AI and banking, reflecting the history and future opportunities for AI in shaping business strategies, improving logistics processes, and enhancing customer value.

Agarwal, P., Swami, S., & Malhotra, S. K. 2022. Artificial intelligence adoption in the post COVID-19 new-normal and role of smart technologies in transforming business: a review. Journal of Science and Technology Policy Management .

Akerkar, R. 2019. Employing AI in business. In artificial intelligence for business , 63–74. Cham: Springer.

Google Scholar  

Akkoç, S. 2012. An empirical comparison of conventional techniques, neural networks and the three-stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: the case of Turkish credit card data. European Journal of Operational Research 222 (1): 168–178.

Article   Google Scholar  

Ala’raj, M., and M.F. Abbod. 2016. Classifiers consensus system approach for credit scoring. Knowledge-Based Systems 104: 89–105.

Alborzi, M., and M. Khanbabaei. 2016. Using data mining and neural networks techniques to propose a new hybrid customer behavior analysis and credit scoring model in banking services based on a developed RFM analysis method. International Journal of Business Information Systems 23 (1): 1–22.

Anagnostopoulos, I., and A. Rizeq. 2019. Confining value from neural networks: a sectoral study prediction of takeover targets in the US technology sector. Managerial Finance 45 (10–11): 1433–1457. https://doi.org/10.1108/MF-12-2017-0523 .

Anderson, J., Bholat, D., Gharbawi, M., & Thew, O. 2021. The impact of COVID-19 on artificial intelligence in banking. Bruegel-Blogs , NA-NA.

Anouze, A.L.M., and A.S. Alamro. 2019. Factors affecting intention to use e-banking in Jordan. International Journal of Bank Marketing 38: 86–112.

Arif, I., W. Aslam, and Y. Hwang. 2020. Barriers in adoption of internet banking: a structural equation modeling-neural network approach. Technology in Society 61: 101231.

Azad, M.A.K. 2016. Predicting mobile banking adoption in Bangladesh: a neural network approach. Transnational Corporations Review 8 (3): 207–214.

Baesens, B., T. Van Gestel, M. Stepanova, D. Van den Poel, and J. Vanthienen. 2005. Neural network survival analysis for personal loan data. Journal of the Operational Research Society 56 (9): 1089–1098.

Baiden, J.E. 2011. The 5 C's of Credit in the Lending Industry. Available at SSRN 1872804.

Bavaresco, R., D. Silveira, E. Reis, J. Barbosa, R. Righi, C. Costa, and C. Moreira. 2020. Conversational agents in business: a systematic literature review and future research directions. Computer Science Review 36: 100239.

Belanche, D., L.V. Casaló, and C. Flavián. 2019. Artificial intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management Data Systems 119: 1411–1430.

Bhatore, S., L. Mohan, and Y.R. Reddy. 2020. Machine learning techniques for credit risk evaluation: A systematic literature review. Journal of Banking and Financial Technology 4 (1): 111–138.

Bitner, M.J., A.L. Ostrom, and F.N. Morgan. 2008. Service blueprinting: A practical technique for service innovation. California Management Review 50 (3): 66–94.

Borges, A.F., F.J. Laurindo, M.M. Spínola, R.F. Gonçalves, and C.A. Mattos. 2020. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management 57: 102225.

Boyatzis, R.E. 1998. Transforming qualitative information: Thematic analysis and code development . Thousand Oaks, CA: Sage.

Broby, D. 2021. Financial technology and the future of banking. Financial Innovation 7 (1): 1–19.

Caron, M.S. 2019. The transformative effect of AI on the banking industry. Banking & Finance Law Review 34 (2): 169–214.

Chatha, K.A., and I. Butt. 2015. Themes of study in manufacturing strategy literature. International Journal of Operations & Production Management 35 (4): 604–698.

Chopra, A., and P. Bhilare. 2018. Application of ensemble models in credit scoring models. Business Perspectives and Research 6 (2): 129–141.

Daqar, M.A.A., and S. Arqawi. 2020. Fintech in the eyes of millennials and generation Z (the financial behavior and fintech perception). Banks and Bank Systems 15 (3): 20.

De Oliveira Santini, F., W.J. Ladeira, C.H. Sampaio, and M.G. Perin. 2018. Online banking services: A meta-analytic review and assessment of the impact of antecedents and consequents on satisfaction. Journal of Financial Services Marketing 23 (3): 168–178.

Digalaki, E. 2022. The impact of artificial intelligence in the banking sector & how AI is being used in 2022. https://www.businessinsider.com/ai-in-banking-report?r=US&IR=T

Dobrescu, E.M., and E.M. Dobrescu. 2018. Artificial intelligence (Ai)-the technology that shapes the world. Global Economic Observer 6 (2): 71–81.

Dushimimana, B., Y. Wambui, T. Lubega, and P.E. McSharry. 2020. Use of machine learning techniques to create a credit score model for airtime loans. Journal of Risk and Financial Management 13 (8): 180.

Ekinci, Y., N. Uray, and F. Ülengin. 2014. A customer lifetime value model for the banking industry: A guide to marketing actions. European Journal of Marketing 48 (3–4): 761–784.

Eren, B.A. 2021. Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing 39 (2): 294–331.

Fountain, T., B. McCarthy, and T. Saleh. 2019. Building the AI-powered organization technology isn’t the biggest challenge, culture is. Harvard Business Review 97 (4): 62.

Frączek, B. 2020. A system to support the transparency of consumer credit offers. Journal of Risk and Financial Management 13 (12): 317.

Gallego-Gomez, C., and C. De-Pablos-Heredero. 2020. Artificial intelligence as an enabling tool for the development of dynamic capabilities in the banking industry. International Journal of Enterprise Information Systems (IJEIS) 16 (3): 20–33.

Goyal, K., and S. Kumar. 2021. Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies 45 (1): 80–105.

Guotai, C., M.Z. Abedin, and F.E. Moula. 2017. Modeling credit approval data with neural networks: An experimental investigation and optimization. Journal of Business Economics and Management 18 (2): 224–240.

Harzing, A.W., and S. Alakangas. 2016. Google scholar, scopus and the web of science: A longitudinal and cross-disciplinary comparison. Scientometrics 106 (2): 787–804.

Hua, X., Y. Huang, and Y. Zheng. 2019. Current practices, new insights, and emerging trends of financial technologies. Industrial Management & Data Systems 119 (7): 1401–1410.

Ince, H., and B. Aktan. 2009. A comparison of data mining techniques for credit scoring in banking: A managerial perspective. Journal of Business Economics and Management 10 (3): 233–240.

Jagtiani, J., and C. Lemieux. 2019. The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform. Financial Management 48 (4): 1009–1029.

Jakšič, M., and M. Marinč. 2019. Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management 21 (1): 1–18.

Jindal, N. 2020. The impact of advertising and R&D on bankruptcy survival: A double-edged sword. Journal of Marketing 84 (5): 22–40.

Jöhnk, J., M. Weißert, and K. Wyrtki. 2021. Ready or not, AI comes—an interview study of organizational AI readiness factors. Business & Information Systems Engineering 63 (1): 5–20.

Kao, L.J., C.C. Chiu, and F.Y. Chiu. 2012. A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring. Knowledge-Based Systems 36: 245–252.

Khan, K.S., R. Kunz, J. Kleijnen, and G. Antes. 2003. Five steps to conducting a systematic review. Journal of the Royal Society of Medicine 96 (3): 118–121.

Khandani, A.E., A.J. Kim, and A.W. Lo. 2010. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance 34 (11): 2767–2787.

Kim, K.J., and W.B. Lee. 2004. Stock market prediction using artificial neural networks with optimal feature transformation. Neural Computing & Applications 13 (3): 255–260.

Kok, J.N., E.J. Boers, W.A. Kosters, P. Van der Putten, and M. Poel. 2009. Artificial intelligence: Definition, trends, techniques, and cases. Artificial Intelligence 1: 270–299.

Königstorfer, F., and S. Thalmann. 2020. Applications of artificial intelligence in commercial banks–a research agenda for behavioral finance. Journal of Behavioral and Experimental Finance 27: 100352.

Koutanaei, F.N., H. Sajedi, and M. Khanbabaei. 2015. A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services 27: 11–23.

Larson, E.J. 2021. The myth of artificial intelligence . In The Myth of Artificial Intelligence: Harvard University Press.

Book   Google Scholar  

Leximancer. (2019, November 25). Leximancer User Guide Release 5.0. Leximancer. https://static1.squarespace.com/static/539bebd7e4b045b6dc97e4f7/t/5e58d901137e3077d4409092/1582881372656/LeximancerUserGuide5.pdf .

Loureiro, S.M.C., J. Guerreiro, and I. Tussyadiah. 2020. Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research . https://doi.org/10.1016/j.jbusres.2020.11.001 .

Malali, A.B., and S. Gopalakrishnan. 2020. Application of artificial intelligence and its powered technologies in the indian banking and financial industry: An overview. IOSR Journal of Humanities and Social Science 25 (4): 55–60.

Mall, S. 2018. An empirical study on credit risk management: The case of nonbanking financial companies. Journal of Credit Risk 14 (3): 49–66.

Manrai, R., and K.P. Gupta. 2022. Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing . https://doi.org/10.1057/s41264-021-00134-9 .

Marinakos, G., and S. Daskalaki. 2017. Imbalanced customer classification for bank direct marketing. Journal of Marketing Analytics 5 (1): 14–30.

McCarthy, J., Minsky, M.L., & Rochester, N. 1956. The Dartmouth summer research project on artificial intelligence. Artificial intelligence: past, present, and future. Available at: http://www.dartmouth.edu/*vox/0607/ 0724/ai50.html

Medhi, P.K., and S. Mondal. 2016. A neural feature extraction model for classification of firms and prediction of outsourcing success: Advantage of using relational sources of information for new suppliers. International Journal of Production Research 54 (20): 6071–6081.

Mehrotra, A. (2019, April). Artificial Intelligence in Financial Services–Need to Blend Automation with Human Touch. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 342–347). IEEE.

Mohapatra, S. 2020. Human and computer interaction in information system design for managing business. Information Systems and e-Business Management . https://doi.org/10.1007/s10257-020-00475-3 .

Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., & Prisma Group. 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.  PLoS medicine , 6(7), e1000097

Mongeon, P., and A. Paul-Hus. 2016. The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics 106 (1): 213–228.

Muñoz-Izquierdo, N., M.D.M. Camacho-Miñano, M.J. Segovia-Vargas, and D. Pascual-Ezama. 2019. Is the external audit report useful for bankruptcy prediction? Evidence using artificial intelligence. International Journal of Financial Studies 7 (2): 20.

Nusair, K., I. Butt, and S.R. Nikhashemi. 2019. A bibliometric analysis of social media in hospitality and tourism research. International Journal of Contemporary Hospitality Management 31: 2691–2719.

Olson, D.L., D. Delen, and Y. Meng. 2012. Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems 52 (2): 464–473.

Pahlevan-Sharif, S., P. Mura, and S.N. Wijesinghe. 2019. A systematic review of systematic reviews in tourism. Journal of Hospitality and Tourism Management 39: 158–165.

Payne, E.M., J.W. Peltier, and V.A. Barger. 2018. Mobile banking and AI-enabled mobile banking: The differential effects of technological and non-technological factors on digital natives’ perceptions and behavior. Journal of Research in Interactive Marketing 12 (3): 328–346.

Payne, E.H., J. Peltier, and V.A. Barger. 2021. Enhancing the value co-creation process: Artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing 15: 68–85.

Rajaobelina, L., and L. Ricard. 2021. Classifying potential users of live chat services and chatbots. Journal of Financial Services Marketing 26 (2): 81–94.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. 2017. Reshaping business with artificial intelligence closing the gap between ambition and action.  MIT Sloan Management Review , 59(1).

Rantanen, A., J. Salminen, F. Ginter, and B.J. Jansen. 2019. Classifying online corporate reputation with machine learning: A study in the banking domain. Internet Research 30: 45–66.

Schwartz, E.M., E.T. Bradlow, and P.S. Fader. 2017. Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science 36 (4): 500–522.

Sharma, S.K., S.M. Govindaluri, and S.M. Al Balushi. 2015. Predicting determinants of Internet banking adoption. Management Research Review 38 (7): 750–766.

Sharma, S.K., Govindaluri, S.M., Al-Muharrami, S., & Tarhini, A. 2017. A multi-analytical model for mobile banking adoption: a developing country perspective.  Review of International Business and Strategy .

Shostack, G.L. 1982. How to design a service. European Journal of Marketing 16 (1): 49–63.

Smeureanu, I., G. Ruxanda, and L.M. Badea. 2013. Customer segmentation in private banking sector using machine learning techniques. Journal of Business Economics and Management 14 (5): 923–939.

Soltani, M., M. Samorani, and B. Kolfal. 2019. Appointment scheduling with multiple providers and stochastic service times. European Journal of Operational Research 277 (2): 667–683.

Tarafdar, M., C.M. Beath, and J.W. Ross. 2019. Using AI to enhance business operations. MIT Sloan Management Review 60 (4): 37–44.

Tian, Z., R.Y. Zhong, A. Vatankhah Barenji, Y.T. Wang, Z. Li, and Y. Rong. 2020. A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics. International Journal of Production Research 59: 1–21.

Tranfield, D., D. Denyer, and P. Smart. 2003. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management 14 (3): 207–222.

Trivedi, J. 2019. Examining the customer experience of using banking Chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce 18 (1): 91–111.

Tseng, C.C. (2003, July). Comparing artificial intelligence systems for stock portfolio selection. In The 9th international conference of computing in economics and finance (pp. 1–7).

Vahid, P.R., and A. Ahmadi. 2016. Modeling corporate customers’ credit risk considering the ensemble approaches in multiclass classification: Evidence from Iranian corporate credits. Journal of Credit Risk 12 (3): 71–95.

Valsamidis, S., Tsourgiannis, L., Pappas, D., & Mosxou, E. 2020. Digital banking in the new Era: Exploring customers' attitudes. In Business performance and financial institutions in Europe (pp. 91–104). Springer, Cham.

Verma, S., R. Sharma, S. Deb, and D. Maitra. 2021. Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights 1: 100002.

Watkins, D.C. 2017. Rapid and rigorous qualitative data analysis: The “RADaR” technique for applied research. International Journal of Qualitative Methods 16 (1): 1609406917712131.

Wu, D.D., & Olson, D.L. 2020. The effect of COVID-19 on the banking sector. In Pandemic risk management in operations and finance (pp. 89–99). Springer, Cham.

Xiao, Y., and M. Watson. 2019. Guidance on conducting a systematic literature review. Journal of Planning Education and Research 39 (1): 93–112.

Xu, Y., C.H. Shieh, P. van Esch, and I.L. Ling. 2020. AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal 28 (4): 189–199.

Yang, A.S. 2009. Exploring adoption difficulties in mobile banking services. Canadian Journal of Administrative Sciences/revue Canadienne Des Sciences De L’administration 26 (2): 136–149.

Zeinalizadeh, N., A.A. Shojaie, and M. Shariatmadari. 2015. Modeling and analysis of bank customer satisfaction using neural networks approach. International Journal of Bank Marketing 33 (6): 717–732.

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Fares, O.H., Butt, I. & Lee, S.H.M. Utilization of artificial intelligence in the banking sector: a systematic literature review. J Financ Serv Mark 28 , 835–852 (2023). https://doi.org/10.1057/s41264-022-00176-7

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Received : 12 January 2022

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Issue Date : December 2023

DOI : https://doi.org/10.1057/s41264-022-00176-7

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Banking and Investment in the Future: Unveiling Opportunities and Research Necessities for Long-Term Growth

• Zahra Ahmadirad
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Research evolution in banking performance: a bibliometric analysis

S. m. shamsul alam.

Department of Finance, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Malaysia

Mohammad Abdul Matin Chowdhury

Dzuljastri bin abdul razak, associated data.

The data collected from the Web of Science online database were saved on Microsoft excel and remained with authors. The data are available upon request.

Banking performance has been regarded as a crucial factor of economic growth. Banks collect deposits from surplus and provide loans to the investors that contribute to the total economic growth. Recent development in the banking industry is channelling the funds and participating in economic activities directly. Hence, academic researchers are gradually showing their concern on banking performance and its effect on economic growth. Therefore, this study aims to explore the academic researchers on this particular academic research article. By extracting data from the web of Science online database, this study employed the bibliometrix package (biblioshiny) in the ‘R’ and VOSviewer tool to conduct performance and science mapping analyses. A total of 1308 research documents were analysed, and 36 documents were critically reviewed. The findings exhibited a recent growth in academic publications. Three major themes are mainly identified, efficiency measurement, corporate governance effect and impact on economic growth. Besides, the content analysis represents the most common analysis techniques used in the past studies, namely DEA and GMM. The findings of this study will be beneficial to both bank managers and owners to gauge a better understanding of banking performance. Meanwhile, academic researchers and students may find the findings and suggestions to study in the banking area.

Introduction

The financial services formed a significant contributory trademark in the overall economic growth by stimulating employment, offering vast avenues for investment and services to the consumers and the society [ 1 ]. Thus, economic development is led by economic growth whereby required capital is provided by the financial services [ 2 ]. Suggestively, capital creation by the financial services industry through accumulation and mobilisation of resources is considered the most crucial economic growth strategy component [ 3 ]. The banking system associates with creating funds by accumulating funds from surplus and channelling them to the investors as credit; those exhibit excellent ideas to generate a surplus in the economy but lack the capital to implement such ideas [ 4 , 5 ]. Accordingly, the banking system plays a vital role to pledge the leading role of finance in economic development and promoting stable and healthy financial and economic development [ 6 ].

Banking performance has been regarded as a crucial factor of economic growth [ 7 ]. Efficiency and productivity change measures are rapidly used to evaluate banking performance. Academic researchers have been focusing on the efficiency and productivity of banking institutions for a long period, while economic growth is carried out in the discussions. Discovering research activities on banking efficiency and productivity in economic growth enables researchers to identify the local and international input to this particular discipline. More so, it will enable researchers to identify the ‘hot spots’ discussed by academic researchers and find the research gaps [ 8 ]. Indeed, banking performance in standings is a broad scientific topic, and estimating research activities might not be useful. For instance, research activities in this area extended to several constituents such as methodological approaches, banking approaches. In the current study, banking efficiency and productivity are considered as banking performance that contributes to the economic growth of an economy. Therefore, the main objective of this study is to explore the research activities of banking performance to economic growth. The investigation of banking performance research activities will enable the researchers to find the present directions of the research area and thus speculates the future research suggestions. Besides, it will also enable to expound the depth of past research activities and themes on banking performance relating to the economic growth measurements.

The use of the bibliometric method is appropriate to demonstrate the research shape and activity, volume and growth in a specific discipline [ 9 ]. A bibliometric method is a quantitative application of bibliometric data [ 10 ]. It analyses a wide-ranging quantity of published research articles employing the statistical tool to identify trends and citations or/and co-citations of a certain theme by year, author, country, journal, theory, method, and research constituent [ 11 ]. Significantly, this technique further distinguishes key research themes and active researchers, countries and institutions for future research planning and funding [ 12 ]. Scholars apply this method for several reasons: to reveal emerging trends in published research articles and journal performance, cooperation patterns, and research elements, and to reconnoitre the intellectual edifice of an exact domain in the existing literature [ 9 , 13 ].

Minimal studies have used bibliometric analysis related to banks. For instance, Violeta and Gordana have employed bibliometric analysis to spot the trends of DEA application in banking [ 14 ]. Another study conducted by Ikra et al. applied the bibliometric method to Islamic banking efficiency [ 15 ]. By an extensive search on the Scopus, Web of Science and Google Scholar, no such study was found related to bibliometric analysis on banking performance to the economic growth. Nevertheless, this study will be the first attempt to conduct bibliometric methods on the banking performance to the economic growth that could be the basis for future studies.

The findings of this study unfolded several contributions to both policymakers, bank managers and academic researchers. Firstly, the findings would benefit the policymakers regarding the contribution and trends of banking performance. It would allow them to take necessary initiatives to promote and improve banking performance, thus economic development. Meanwhile, bank managers may utilise the findings to strengthen their banking operations by acknowledging key factors that contributed to the performance. Finally, academic researchers are enabled to detect the current trend and topics related to the banking area that leads to further studies.

Bibliometric analysis has achieved enormous popularity in social sciences research in the current years [ 9 , 16 – 18 ]. The popularity of bibliometric analysis is observed from the development, accessibility and availability of software, for instance, Leximancer, Gephi, VOSviewer, Biblioshiny and publication databases (Web of Science and Scopus). Further, the rapid growth of bibliometric analysis in scientific production has emerged from business research to information science [ 9 ]. The popularity of bibliometric methodology in social science research is not a trend but moderately an image of its usefulness for constructing high research impact by handling excessive scientific data [ 9 ].

The bibliometric analysis is beneficial for briefing the trends in the research documents classifying ‘blind spots’ and ‘hot spots’, and finding a more inclusive understanding of the published research documents [ 19 ]. In detail, this analysis empowers the recognition of the most advanced (hot spots) and the less established topics (blind spots) within the documents that, shared with other bibliometric procedures, recommend future research avenues. The bibliometric analysis uncovers several ascriptions, such as unveiling emerging trends in documents and the performance of journals, research constituents and collaboration patterns and discovering the intellectual edifice of an exact domain in the existing literature [ 13 , 18 ]. The data that apply in this analysis incline to be immense (hundreds, thousands) and unbiased in nature (publications and citations number, keywords occurrences and topics). However, its explanations often depend on both subjective (thematic analysis) and objective (performance analysis) assessments formed through well-versed techniques and procedures [ 9 ]. Therefore, this study applied bibliometric analysis to examine the general perspective on banking performance and economic growth.

Two categories are mainly manifest in the bibliometric techniques, namely, performance and science mapping. Precisely, research elements’ contributions are accounted for in the performance analysis, while the connections between research elements are focused on science mapping [ 9 ]. This study follows performance analysis, science mapping and network analysis suggested by Donthu et al. [ 9 ].

Data extraction process

Two primary databases, the Web of Science and the Scopus, are commonly used in the bibliometric analysis [ 20 ]. Both databases are prominent for the peer-reviewed published research articles. The data for this analysis were a collection of bibliographic data from the Web of Science. The Web of Science (WoS) is a multidisciplinary online database providing access to several citation databases, namely Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Emerging Sources Citation Index (ESCI), Arts and Humanities Citation Index (AHCI), Conference Proceedings Citation Index, Index Chemicus and Current Chemical Reactions [ 18 , 21 ].

This study has applied a two-stage data extraction process, following Bretas and Alon, Alon et al. and Apriliyanti and Alon [ 16 , 22 , 23 ] as shown in Fig.  1 . The choice of the keywords is crucial to ensure that it covers the total body of published documents on banking performance and economic growth [ 21 ]. Accordingly, the selection of keywords was carried out by reviewing several abstracts and authors’ keywords in most related literature on the Web of Science. The selected keywords were executed in the WoS online database on 9 August 2021. A combination of keyword search terms was considered; (1) ‘banking performance*’ to nail all discrepancies of the term such as the role of the bank, bank efficiency, bank productivity, banking efficiency, banking productivity, banking performance, bank performance, upon refining the search by including only research articles from the categories; economics, business finance, business, management, operations research management, social sciences mathematical measures and documents written in English.

The second stage extracted raw data from the online database combined, checked for duplicate documents and merged using ‘R’. Further, the documents were filtered in the ‘biblioshiny’ tool to omit book chapters and conference proceedings. After the extraction process for the bibliometric analysis, several impactful documents were selected based on local and global citations to conduct content analysis. The content analysis allowed the researcher to identify the leading research scopes and trends. Further, it allows identifying the streams and recommendations for future studies [ 22 ]. A total of 36 documents were selected to conduct a comprehensive review and valuation of the documents.

Performance analysis

Performance analysis investigates the contributions of academic research elements to a particular discipline [ 24 ]. This analysis is naturally descriptive, which is the hallmark of bibliometric analysis [ 9 ]. It is a standard method in reviews to exhibit the performance of various research elements such as authors, countries, institutions and sources similar to the profile or background of respondents generally presented in empirical studies, albeit more statistically [ 9 , 18 ]. Many measures exist in the performance analysis; hence, the most protuberant measurements are publications number and citations per research constituent or year. The publication is considered productivity, whereby citation measures influence an impact [ 9 ]. Besides, citation per document and h -index associate both publications and citations with evaluating research performance [ 18 ].

Table ​ Table1 1 presents the publication’s performance of banking performance. The results show a total number of 1308 documents published from 1972 to the present. Among 2275 contributed authors, a total of 202 authors were solely, and 2106 authors collaborated to the publications. A total of 31,458 citations received by published documents lead to an average of 629.16 citations per year, while 775 in h -index and 1023 in g -index. Hence, the banking efficiency field acknowledged productivity of research published by an average of 26.16 documents per year whereby nearly two authors (CI = 1.9) published one article, and standardised collaboration is 0.43 (between 0 and 1).

Metrics for performance analysis

The annual production of scientific publications on banking efficiency is presented in Fig.  2 . The first research article related to banking performance was published by Fraser and Rose [ 25 ], who studied the effect of new bank appearance in the market on bank performance. The annual growth of publications on banking performance or banking efficiency is recorded to 12.39%. The publications are significantly increasing in recent periods, especially from 2016 to the present. However, the mandated growth in publications is observed between 2004 and 2015, while earlier periods (1972–2003) were quite sluggish. In these consequences, academic researchers have started to focus on banking performance or banking efficiency in the recent period. As a result, it can be concluded that banking performance and its sphere are shaping upwards through the research contributions.

Annual Scientific production

Science mapping

Science mapping investigates the connections between research elements [ 26 ] that relates to the intellectual connections and structural networks within research constituents [ 9 ]. The science mapping includes citation analysis, bibliographic coupling, co-citation analysis, co-occurrence network, collaboration techniques. When combined with network analysis, these techniques are instrumental in exhibiting the research area’s bibliometric edifice and intellectual structure [ 27 ].

Citation analysis

The citation analysis is a fundamental approach for science mapping that runs on the assumption that citations reproduce intellectual contributions and impact the research horizons [ 28 ]. This analysis shows the impact of published documents by measuring the number of citations they received [ 9 ]. Accordingly, it enables the discovery of the most influential and informative documents in a research constituent. Thus, it allows gathering insights into that constituent’s intellectual dynamics [ 9 ]. Table ​ Table2 2 presents the top 20 impactful and influential documents in the field of banking performance or efficiency. The analysis has discovered that a total of 1112 documents (85%) out of 1308 documents received global citations. The global citations refer to the number of citations received in the overall Web of Science citations. However, 196 documents (about 15%) have not received any citation; meanwhile, 130 documents (about 10%) received only one citation. A document written by Berger An received the highest number (665) of citations which was published in 1997. The second most influential document was written by Seiford [51] received a total of 549 citations, followed by the document written by Back (2013) received 512 citations. In fact, a total of four documents written by Berger An rank in the top 20 impactful research articles in the field of banking performance or efficiency.

Top 20 most cited papers.

Source : Biblioshiny R package

Factually, the majority of the documents without citations was published in a recent period. At the same time, the highly cited documents were published quite earlier. To detect the immediate influence of more recent documents is to apply the measurement of an average citation per year [ 29 ]. By evaluating the average citations per year, nine out of ten documents are also among the top 10 documents. Perpetually, Beck [45] holds the highest number of average citations per year (56.89), followed by Berger An (2013) ranked second position (51.44) and Beltratti A (2012) ranked the following position (48.40). Based on the citation analysis, it can be elucidated that Berger An is the most influential author in the banking efficiency research constituent.

Co-occurrence analysis

Co-occurrence analysis was projected by Callon et al. [ 30 ], considered as content analysis that is useful in plotting the strength of connotation within keywords in textual data. In other words, co-occurrence analysis is an approach that investigates the actual content of the document itself [ 9 ]. It maps the pertinent literature straight from the associations of keywords shared by research articles [ 24 , 27 , 31 , 32 ]. The co-occurrence analysis deduces words to appear recurrently in a cluster. It exhibits conceptual or semantic groups of various topics or sub-topics considered by research constituents [ 9 , 24 ]. Cobo and Herrera signified that spotted clusters could be applied with few objectives [ 24 ]. For instance, they can be applied to analyse their progression by gauging extension across successive subperiods and measuring the research area through performance analysis. Figure  3 displays the co-occurrence of keywords within the bank efficiency research constituent. As the focus of this research, bank performance represents the larger node associated with corporate governance, financial performance, financial crisis, nonperforming loans and others. In these scenarios, the red-coloured cluster depicts that these subtopics or variables are directly associated related to the bank performance theme due to repetitive co-occurrence of those words. Likewise, the green-coloured cluster represents a theme related to bank efficiency associated with performance and ownership. In the same cluster, the nonparametric data envelopment analysis is extensively used to measure commercial banks' technical and cost efficiency and productivity. Parametric stochastic frontier analysis is narrowly observed in efficiency measurements comparably. The green-coloured cluster depicts the determinants of bank profitability including other impactful variables such as risk, competition, corporate governance. This cluster applied panel data in order to examine performance, financial development as well as economic growth. Each of the cluster identifies the interacted themes used in the published documents using co-occurrence of keywords.

Co-occurrence of keywords, Tool: VOSviewer. Note the nodes represent the keywords, and the edges between words present their occurrence of interactions. Each colour of nodes represents a cluster/theme. The size of the node presents a greater frequency of occurrence

Collaboration networks

Collaboration analysis explores the associations within researchers in a particular constituent. It is a formal way of intellectual association among researchers [ 33 , 34 ]. Therefore, it is crucial to understand how researchers associate among themselves [ 9 ]. In the presence of growing theoretical and methodological complexity in research, intellectual networking (collaboration) has become commonplace [ 33 ]. Indeed, collaboration or interaction among researchers enables improvements in academic research; for instance, greater interactions among diverse researchers allow richer insights and greater clarity [ 35 ]. Researchers who collaborate form a network named ‘invisible collages’ whose research can help improve undertakings in the study field [ 36 ]. Figure  4 presents the collaboration network of authors those co-authored academic articles in banking efficiency. Based on the collaboration network, Wanke P (Universidade Federal do Rio de Janeiro) was the most collaborated author who co-authored with four authors from different institutions in different countries. At the same time, Matousek, R (University Kent), Hasan, I (Rensselaer Polytechnic Institute) and Mamatzakis, E (University of Sussex), have also exhibited as greater collaborative researchers. In these consequences, authors from different institutions and from different parts of the world are collaborating to the banking performance/efficiency field.

Authors’ collaboration networks.

Source : VOSviewer. Note the nodes represent the authors, and the size represents the frequency of contribution, the colour presents a cluster or a particular group, and the link shows the link among authors that collaborated for research articles

Bibliographic coupling

Co-authorship or collaborative networks within the authors and other crucial facets in the collaboration networks are the collaboration of author-affiliated countries and institutions [ 31 ]. Figure  5 exhibits the collaboration network within authors’ affiliated organisations. University Malaya and University Utara Malaysia, University Malaya and University Putra Malaysia, University Malaya and University Fed Rio de Janeiro all depict a strong collaboration network. In general, all the institutions display an embellishment among the institutions within the same region.

Bibliographic coupling of author-affiliated institutions.

Source : VOSviewer

Similar to co-authors’ affiliated institutions, the collaboration of authors’ country presents a steady association among authors’ connections that allow exploring comparative and concurrent research works. Figure  6 represents the network of collaborative authors’ affiliation countries. These countries include South Africa and the USA, England and the USA, Australia and the USA, Malaysia and the USA, Germany and the USA, representing a high proportion of authors’ affiliated institutions are in the USA with this country performing as a hub of co-authorship publications from 1972 to 2021.

Collaborative authors’ affiliated countries

This study discusses trending themes based on the bibliometric findings and reviews of highly cited and most recent documents (see Appendix 1 ). It also indicated the type of study, theories, methods and main findings to suggest comprehensive future studies.

Research directions

Between 1991 and 2010, studies related to banking performance have posited several antecedents to banking performance. Figure  7 displays the trend topics based on author keywords that appeared between 1972 and 2010. Studies in this period mainly focused on mergers and acquisitions, information technology and transition economies that emerged from universal banking deregulation and bank privatisation. The financial crisis during 2008–2009 drew the attention of scholars to evaluate the banking performance. Idiosyncratically, this phenomenon has been acknowledged by researchers from 2010 to 2015, focusing on the role of corporate governance in the performance of the banking industry, including compensation, risk management, determinants of stock returns, capital buffer, productivity. Idiosyncratically, a vast of studies were conducted on Chinese commercial banks and the effect on their economic growth.

Trend topics in different periods.

Source : Biblioshiny analysis. Note the frequency of terms selected 3 times for 1972–2010, 5 times for 2011–2015, 10 times for 2016–2021

In the recent period (2016–2021), diverse factors posited in the studies that dominantly present a significant interest from banking scholars. While studies earlier mainly focusing on efficiency and its contributing factors, recent periods extended research directions to multiple constituents. For example, how banks diversified their services and the role of human capital efficiency to the banking performance [ 37 ]. Bose et al. employed the effect of green banking on the performance that underpins the inclusion of the environmental sustainability approach by the banking industry [ 38 ]. Meanwhile, Bhattacharyya et al. showed the effect of CSR expenditures and financial inclusion on the performance that define the social sustainability indicator of the banks [ 39 ]. Repeatedly, the role and structure of the board, categorisation of deposits and loans, risk exposures (business cycle), macroeconomic factors were also acknowledged in recent banking performance studies [ 40 – 43 ]. Idiosyncratically, scholars recently focus the components of sustainability of the banking industry from economic, environmental and social aspects [ 44 ]. Furthermore, the effect of banking and its stability on economic growth has been broadly carried out in the recent period. Moreover, the development of studies was taken into account, which implies the contribution to the economic growth of particular regions. Based on the earlier and recent studies, it is precisely observed the diversification of research constituents in relation to bank performance studies. Earlier studies (up to 2015) mainly measured banking performance or efficiency based on accounting measurements, while recent studies started to include market measurements principally based on stock returns performance. On the other hand, the rise of Islamic banking and finance influenced academic researchers to compare the business models [ 45 ], banking efficiencies [ 46 ] between conventional and Islamic banks, and efficiency for Islamic banks [ 5 ].

Based on the review of impactful documents published from 1990 to 2010, two particular objectives were identified: the effect of the board of directors or ownership on the bank performance [ 47 – 49 ] and measurement of efficiency, including cost and profit efficiency [ 50 – 52 ]. These constituents extended during 2011–2020 by the inclusion of risk-taking management [ 53 ], CEO incentives [ 54 ], contributing factors including capital, banking crises on banking performance [ 42 , 55 – 57 ]. Meanwhile, the Islamic banking system got crucial attention from academic researchers. Accordingly, several studies evaluated and compared efficiency between Islamic and conventional banks [ 45 , 58 , 59 ]. Nevertheless, the role of the banking industry in economic growth was included in the research constituents in the recent decade. For example, Xu, Santana and a few more scholars investigated the correlation between financial intermediation and economic growth [ 57 , 60 , 61 ]. In recent years, scholars extended the banking-related research constituents to diverse areas. The effect of human capital efficiency [ 37 ], green banking [ 38 ], CSR expenditures [ 39 ] and bank stability was included to measure banking performance. These extensions of research themes within banking performance studies posited a significant interest by academic researchers.

Apparently, almost all documents adopted the quantitative method in measuring banking performance research constituents. However, studies that measured banking efficiency mainly applied nonparametric analysis DEA [ 5 , 51 ], while SFA was adopted by limited studies [ 37 , 42 , 43 ]. On the other hand, regression analysis was predominantly applied to investigate banking performance from 1990 to 2010 [ 49 , 50 ]. In recent studies, academic researchers have vastly adopted GMM (generalised method of moments) to examine the contributing factors on banking performance [ 39 , 42 , 57 , 60 ]. These methods are dominating the banking-related studies throughout the publication periods. Over the periods, scholars have developed DEA applications in several categories, such as bootstrap, networking. Meanwhile, GMM with different approach (dynamic and system) techniques exploited panel data primarily extracted from Bankscope, Datastream, annual reports etc.

Main findings

Earlier, banking inefficiencies were substantially observed low, negatively affecting profitability and marketability [ 50 , 51 ]. This trend was continuously depicted in studies [ 52 ]. However, Berger et al. evidenced better efficiency for larger banks than smaller banks [ 50 ]. On the contrary, Seiford and Zhu posited an adverse effect of bank size on marketability [ 51 ]. More so, Rehman et al. found larger banks are less efficient than smaller banks [ 40 ]. Hence, Moudud-Ul-Huq posited diverse impacts of bank size and competition on performance [ 62 ]. So, banking size is deemed to have a substantial effect on the overall performance of banks. However, Adesina embellished that diversification of services and choices of management decisions on loans (nonperforming, debt issuances) [ 63 , 64 ] and deposits [ 41 ] affect the banking performance [ 37 ]. Moreover, board structure affects banking performance [ 40 , 65 ], while higher human capital efficiency enhances banking performance [ 37 ].

Generally, foreign-owned banks provide better service, greater profitability and are better efficient than local banks. This phenomenon was evidenced in several studies; for example, Bonin et al. and other scholars demonstrated that foreign-owned banks are more cost-efficient than other banks [ 48 ]. However, this trend did not exist for Islamic banks as local banks showed better efficiency than foreign peers [ 58 ] and more efficient than conventional [ 59 ]. Meanwhile, state-owned or government-owned commercial banks were less efficient and provided poorer services [ 48 , 49 , 52 ]. But these banks’ efficiency was higher than urban/rural banks during credit risk shock [ 41 ]. Nevertheless, banking efficiency and performance substantially depend on diversification of services, managerial adequacy, ownership, types and size.

Studies have evidenced financial development and thus the banking industry’s role in economic growth [ 60 ]. In the nineteenth century, the establishment of the savings bank demonstrated city growth in Prussia [ 66 ]. Potentially, banks provide investment capital to increase per capita GDP [ 43 ]. However, Haini documented a contrasting effect of banking development on economic growth through a push out of private investment due to high levels of the banking sector [ 67 ]. However, Stewart and Chowdhury proved that a stable banking sector lessens the negative impact of a crisis on GDP growth and provides economic resilience in both developed and developing countries. Overall, findings elaborated a crucial link between banking sector development and economic growth.

Future study suggestions

This study has recommended several scopes for future studies in the hybrid review, mainly through bibliometric findings and the structured review of impactful articles [ 11 ]. In other words, the recommendations for future studies are made by observing and analysing discussions on highly cited and recent cited documents. Overall findings and analyses raised several questions that need to be addressed for future studies.

Firstly, does the banking sector improve economic growth in the least developed countries? Prior studies mainly focused on developed and developing economies, but less attention was given to least developed countries. Secondly, vast studies investigated contributing factors of banking performance, while political instability has been ignored. Future studies might include political instability on the banking performance. Apart from it, nonperforming loans can be another addition to future studies, and even few studies documented it. Thirdly, how do banks perform during the pandemic crisis, for instance, COVID-19? The current pandemic crisis can be a significant factor in banking performance related to future studies, including efficiency, mortgages, loan recovery, deposits and business services. The studies can include consumer behaviour (due to restricted movements, safety measurements), green banking (online transaction and services), financial technologies (inclusion of nonbanking services) and the contribution or continuance of economic activities in the country during and after the pandemic crisis.

Significantly, prior studies have ignored the current trend of FinTech inclusion in banking performance. Fourthly, will FinTech takeover the banking services and diminish banks in the near future? Future studies may investigate the effect of FinTech applications on banking. More so, future studies may explore the banking industry’s barriers, challenges and threats due to FinTech growth. Fifthly, almost all studies employed quantitative analysis related to banking performance. Therefore, future studies may use qualitative methods to explore the opportunities and practices of banks and their performance. Sixthly, the majority of the studies either applied parametric or econometric techniques to investigate the bank performance. Recent developments in technologies and methods may provide easy and robust results in such related studies as using machine learning for data analysis and predicting banking efficiency and productivity determinants. Seventhly, past studies mostly followed the intermediation approach, which scarcely included production and operating approach measurement. Future studies may extend the efficiency analysis using productivity growth analysis. Further, the majority of the studies observed efficiency only. Future studies can include a productivity change index along with an efficiency analysis. Finally, GMM and regression were broadly applied to investigate the effect of antecedents of banking performance and link to economic growth. Future studies may adopt other advanced data analysis techniques such as partial least squares, structural equations and other econometric techniques.

Conclusions

The main purpose of this study is to explore the trends and research activities in banking performance and the economic growth research domain. To achieve this objective, a bibliometric analysis was applied and performed several analyses, namely citation, co-occurrence of keywords, the collaboration between authors and coupling between institutions and countries, and discussion by reviewing most cited and most recent influential research articles. This study presents the most common themes, sub-themes associated with highly cited documents and authors; furthermore, the content analysis identified the research directions, research objectives, methodologies, topics and findings.

Based on the reviewing literature, the efficiency theory, banking theory mainly intermediation approach and nonparametric technique, namely data envelopment analysis along with econometric method, regression was used in the published documents. The findings of this study, along with future study suggestions, could be beneficial to bankers as well as academic researchers and students studying banking performance and its role in the economy.

Limitations

The most crucial limitation in any bibliometric analysis is the database selection. It means selecting the data and the limits of its interpretation [ 68 ]. This study has three key limitations; firstly, it has chosen ‘Web of Science’, one of the largest online databases to gather data on banking performance research articles from 1972 to 2021 and refined based on subject categories and language (English). The database could be improved if other databases were included and also if book chapters and conference proceedings were added. Secondly, the selection of keywords; although selected keywords are deemed to be most relevant to encompass the majority of articles related to banking performance, there is always an opportunity to search further articles by using additional keywords. Lastly, this study could not conduct co-citation analysis due to the unavailability of cited documents in Web of Science data format.

Acknowledgements

Abbreviations.

Appendix 1: Reviewed documents

Authors' contributions

MAMC conducted the data analysis. SMSA prepared the manuscript by contributing literature and discussion for this study. DBAR managed the data and edited the manuscript. All authors read and approved the final manuscript.

Availability of data and materials

Declarations.

We have no conflicts of interest to disclose.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Literature review on financial technology and competition for banking services

This version.

The extent to which financial technology will shape the banking industry depends in part on the nature of competition for banking services that arises from innovation by incumbent banks and the entry of new players. This paper presents a review of the growing body of economic literature on financial technology and competition for banking services. The review highlights that fintech has spurred innovation and competition across banking services including in payments, lending, deposit taking and advisory services. Research finds that entry by new fintech-based service providers has expanded access to financial services and put pressure on the market share and pricing power of incumbent banks. The evidence also suggests that fintech-based firms that started out as lenders or payments providers have evolved to offer a broader range of services. We cannot fully know how ongoing innovation will affect business models, but so far, the literature highlights some enduring strengths for the model that bundles a variety of banking services in a single firm, ie, a bank.

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It is felt that Modern Banking has become wholly customer – driven and technology driven. During the last decade, technology has been dramatically transforming the banking activities in India. Driven by challenges on competition, rising customer expectation and shrinking margins, banks have been using technology to reduce cost. Apart from competitive environment, there has been deregulation as to rate of interest, technology intensive delivery channel like Internet Banking, Tele Banking, Mobile banking and Automated Teller Machines (ATMs) etc. have created a multiple choice to user of the bank. The banking business is becoming more and more complex with the changes emanating from the liberalization and globalization. For a new bank, customer creation is important, but an established bank it is the retention is much more efficient and cost effective mechanism. Customer Relationship Management (CRM) would also make Indian bankers realize that the purpose of their business is to create and keep a customer and to view the entire business process as consisting of Highly Integrated effort to discover, create and satisfy customer needs. But it is surprising to note that much of the activities of the banking and financial remain focused on customer creation not retention.

Revisiting expectation confirmation model to measure the effectiveness of multichannel bank services for elderly consumers

PurposeThis study aims to understand the expectations of elderly bank customers with mobile banking services and to measure its impact on their long-term satisfaction and continued intention. The study is based on two theories, expectations-confirmation theory (ECT) and hedonic adaptation theory.Design/methodology/approachA self-administered longitudinal survey was completed with a sample of 208 elder customers who do not use mobile banking services. Latent growth curve modelling approach was used to determine the change in their post-adoption experience over four time points.FindingsResults of the study confirm that the use of mobile banking services prolongs the duration of customer satisfaction and continued intention level, post-adoption, reinforcing the hedonic adaptation theory.Research limitations/implicationsMobile banking services are going to be a significant component of the multichannel banking agenda. But it might be interesting to review other digital channels of banking services. The key contribution of this study is that it measures the expectation-confirmation link of elderly customers with mobile banking services. The study sheds light on factors that positively influence customer inclination and adoption of multichannel banking services in the long run, which is important for the commercial success of such channels.Practical implicationsThe study highlights the importance of elder customers' pre-expectations, related dimensions which are important for post-adoption experiences of mobile banking services to improve customers' satisfaction and continued intention in the long run. This is crucial for the commercial success of banks.Originality/valueThis is the first such study that used the expectation confirmation model (ECT) and related it with hedonic adaptation theory to assess elderly customer's post-adoption satisfaction and continued usage of mobile banking services over time.

Understanding the Predictors of Rural Customers’ Continuance Intention toward Mobile Banking Services Applications during the COVID-19 Pandemic

Panel evidence from rural uganda on mobile money, risk sharing, and educational investment.

We investigate the influence of the rapidly developing mobile banking service "mobile money" on rural households' capacity to smooth their investment in education following a negative shock. We find that a negative shock reduces per school-age kid educational spending by 9.3 percentage points in families that do not utilize mobile money but by 8.3 percentage points in homes that have used mobile money. The underlying process is a rise in remittance receipts and sender variety as a result of the lower transaction costs afforded by mobile money. We demonstrate that our findings are resistant to alternative processes. We utilize the extension of the mobile money agent network as an exogenous variable in mobile money access.

Sikap Konsumen dan Kinerja Atribut Produk Mobile Banking (Studi Pengguna Mobile Banking di Kota Surabaya)

Mobile Banking sebagai salah satu bentuk layanan sistem informasi perbankan, yang dapat menjadi solusi terbaik untuk nasabah  dengan tingkat kesibukkan yang tinggi. Banyaknya keluhan nasabah terkait aplikasi layanan mobile banking, maka penelitian ini penting dilakukan dengan tujuan untuk menganalisis atribut-atribut apa saja yang mempengaruhi sikap nasabah terhadap penggunaan layanan Mobile Banking, serta bagaimana kinerja atribut Mobile Banking. Metode penelitian yang digunakan adalah pendekatan deskriptif kuantitatif. Teknik pengambilan sampel menggunakan simple random sampling, diperoleh 100 responden pengguna mobile banking. Metode analisis data menggunakan Model Multiatribut Fishbein dan Importance Performance Analysis (IPA). Hasil penelitian menunjukkan sikap konsumen terhadap atribut Mobile Banking secara keseluruhan adalah positif, yakni dengan nilai 218,81. Kinerja atribut Mobile Banking dianalisis dengan Importance Performance Analysis, menunjukkan hasil bahwa atribut yang perlu diprioritaskan untuk ditingkatkan kinerjanya yaitu atribut yang termasuk dalam kuadran I (Prioritas Utama): Website Mobile Banking dan kemudahan Mobile Banking dioperasikan saat bertransaksi.

Quality factors in technology system capability decision interest in transactions using mobile banking

The purpose of this study was to determine the effect of ease of use, transaction success rate, and technological system capability on trust and to determine the effect of ease of use, transaction success rate, technology system capability and interest in transaction using mobile banking. This research was conducted at PT Bank NIAGA which is located in Denpasar. The data collection technique used a questionnaire to 160 PT Bank NIAGA customers who were selected as samples. Data were analyzed by Structural Equation Modeling (SEM) with AMOS program. Ease of use, transaction success rate, and technology system capability have a positive and significant influence on trust and interest in transacting using mobile banking. This means that the better the ease of use, the success rate of transactions, and the capability of the technology system, the higher the customer trust and interest in transactions using mobile banking.

Exploring the determinants of mobile banking adoption in the context of Saudi Arabia.

Rapid advances in mobile technologies and innovations have made mobile banking progressively significant in mobile business and monetary administrations. Our study used TAM and perceived risk as a theoretical base to develop a conceptual model that can explain the main factors affecting users intentions to adopt mobile banking in Saudi Arabia context. SEM/AMOS techniques were used to analyse the data collected from mobile banking users. the results indicated that perceived relative advantages, perceived ease of use, perceived compatibility have a significant positive effect on attitude towards mobile banking adoption, while perceived risk has a negative effect on intention to adopt. Furthermore, attitude has a significant influence on intentions to adopt mobile banking. The study offers meaningful implications to theory and practice.

Mobile Banking: A Study on Adoption Stages Using Government Adoption Model (GAM) and the Role of Demographic Moderators

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Banking and Finance Dissertation Topics – Selected for Business Students

Published by Owen Ingram at January 2nd, 2023 , Revised On August 16, 2023

Looking for an interesting banking and finance research idea for your dissertation? Your search for the best finance and banking dissertation topics ends right here because, a t ResearchProspect, we help students choose the most authentic and relevant topic for their dissertation projects.

Bank taxes, financial management, financial trading, credit management, market analysis for private investors, economic research methods, the economics of money and banking, international trade and multinational business, the wellbeing of people and society, principles and practices of banking, management and cost accounting, governance and ethics in banking, investment banking, introductory econometrics, and capital investment management are among the many topics covered in banking and finance.

Without further ado, here is our selection of the besting banking and finance thesis topics and ideas.

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The following dissertation topics for banking will assist students in achieving the highest possible grades in their dissertation on banking finance:

List of Banking and Finance Dissertation Topics

• A Comprehensive Analysis of the Economic Crisis as It Relates to Banking and Finance
• A Critical Review of Standard Deviation in Business
• The Political and Economic Risks Involving National Bank Transactions
• A Study of Corporate Developments in European Countries Regarding Banking and Finance
• Security Measures Implemented in Financial Institutions Around the World
• Banking and Finance Approaches from Around the World
• An in-depth study of the World Trade Organization’s role in banking and finance
• A Study of the Relationship Between Corporate Strategy and Capital Structures
• Contrasting global, multinational banks with regional businesses
• Preventing Repetitive Economic Collapse in National and Global Finances
• The Motivations for Becoming International Expats All Over the World
• The Difference Between Islamic Banking and Other Religious Denominations in Banking and Financial Habits
• How Can Small-Scale Industries Survive the Global Banking Demands?
• A Study of the Economic Crisis’s Impact on Banking and Finance
• The Impact of the International Stock Exchange on Domestic Bank Transactions
• A 2025 Projected Report on World Trade and Banking Statistics
• How Can We Address the Issue of the Government’s Financial Deficit in Banking?
• A Comparison of Contemporary and Classic Business Models and Companies’ Banking and Financial Habits
• Which of the following should be the principal area of money investment that has arrived at the bank in the form of deposits?
• How to strike a balance between investing money in various plans to generate a profit and managing depositor trust
• What are banks’ responsibilities to their depositors, and how may such liabilities be managed without jeopardising depositor trust?
• How the new banking financing laws enacted by governments throughout the world are better protecting depositors’ rights?
• What is the terminology related to banking finance, which oversees the investment of deposited funds as well as the banks’ responsibilities to depositors?
• Explain the most recent developments in research related to the topic of banking finance
• How research in the banking finance industry assists governments and banking authorities in properly managing their finances?
• What is the most recent credit rating software that assists in determining the rewards and dangers of investing bank funds in the stock market? 
• How banking finance assists the world’s top banks in managing consumer expectations and profit?
• The negative impact of a manager’s poor management of a bank’s banking financing
• Is it feasible to conduct a banking firm without the assistance of banking finance management?
• What are the most significant aspects of banking financing that allow businesses to develop without constraints?

The importance of banking finance cannot be overstated. These are only a few of the most extensive subjects on which you may write a banking and finance dissertation. Remember that if you want to succeed in your studies, you must be able to offer reliable numbers and facts on the history and current state of banking and finance throughout the world. Otherwise, you will very certainly be unable to justify your study effectively. We hope you can take some inspiration and ideas from the above banking and finance dissertation topics .

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• Analyze financial markets.
• Investigate risk management.
• Consider ethical and global aspects.

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Five ways to drive experience-led growth in banking

Customers’ needs are changing. They expect more from service providers in the form of fast, frictionless, and personalized journeys. Their banking practices have also altered, with many of them now using digital and looking for it from their banks. Customer experience (CX) is proving to be the strategic differentiator for banks, with experience leaders outperforming laggards.

In this article, we explore how banks can improve customer experience, identify five bold moves they can make to gain the competitive advantage, and why they must act now, given the current dynamic macroeconomic environment.

Our research shows that banks that are frontrunners in customer satisfaction lead in financial metrics such as total shareholder return (TSR), increased growth, and decreased costs (Exhibit 1). We also see a positive correlation between customer satisfaction and purchasing decision—customers who are satisfied with their banking experiences say they will purchase more of that bank’s products. And satisfied customers are six times more likely to say they'll remain with a bank than dissatisfied customers are.

In this uncertain economic environment, excelling in customer experience is more important than ever for banks—the past year has seen one of the most dynamic macroeconomic conditions in the past several decades. Over the past 12 months, interest rates have risen by more than 300 basis points, mortgage originations have dropped by 60 percent, and the flow of money between financial institutions has increased four times.

Confidence is waning—more than 65 percent of customers are pessimistic about the economic outlook for the coming year, about a ten percentage point increase compared to last year. Their biggest concerns are inflation, the rising cost of goods, and savings for emergency funds.

With the dynamic macroeconomic environment and the overall pessimism consumers are feeling, customers are thinking to the future, shifting their financial practices, and reevaluating relationships with their financial institutions. We notice a move toward increasing household spending and accelerating paying down credit card debt, as well as reducing savings for retirement and emergency funds. New financial accounts are being opened at twice the average rate, and new banking relationships and switching banks are being considered (Exhibit 2).

Five critical CX moves banks can make to get ahead

Here are five get-right moves for those that want to seize the moment and become industry leaders.

Reimagine, not just de-friction, priority journeys

A typical regional bank has over 1,500 customer journeys (across business units, product lines, and customer interactions). 1 McKinsey analysis. Looked at simply, these journeys can be categorized into two broad categories—those that a bank needs to “de-friction” and those that need to be reimagined. Most journeys fall into the de-friction bucket, as streamlined, seamless experiences still matter and drive customer satisfaction. However, our research shows that the “bookend” journeys of shopping, onboarding, and problem resolution disproportionally drive the overall experience that a customer has with their bank. It is here that a bank could consider flexing its reimagination muscle (Exhibit 3).

To truly reimagine a given journey, banks can take the following steps:

• Assemble a cross-functional group that can bring diversity of experiences and thinking.
• Understand the competition, including recognizing that experience leaders also come from adjacent and other B2C industries outside purely banking—for example, a mobile payment application.
• Take inspiration from other industries as a customer’s bar for great experiences is driven by interactions and experiences outside banking.
• Leverage the concept of a zero-based design (“clean sheeting”): start with a blank canvas and imagine a new journey without considering the current state or any constraints; layer on (technical and operational) constraints afterwards.
• Co-create with customers to increase the chances of success, especially for novel signature moments.
• Push innovation to the next level. For example , how could something happen with no user-inputted data, with one click (or even no clicks)?

As a case in point, a large North American bank established an innovation factory to redesign critical banking processes and digital journeys. This brought together cross-functional teams—across product, business, technology, design, marketing, risk and compliance, legal, operations, finance, etcetera—to work on reimagining key customer journeys. Over the course of two years, more than 30 reimagined journeys were developed and rolled out. The resulting impact was a 25 to 50 percent increase in customer satisfaction of those journeys.

Radical shifts in customer behavior can be disruptive, but by delivering differentiated value for their customers, banks can take advantage of this defining moment to stand out.

Help customers migrate to digital

Most banks have highly inconsistent digital adoption. Even for banks that have similar levels of digital migration, McKinsey’s proprietary Digital Migration Index shows a two to four times variation in digital adoption of the underlying products and journeys. Our research reveals that customers who regularly use a bank’s mobile app or website (or both) have the highest average satisfaction compared to customers who use other interaction channels or infrequently use the digital channels (Exhibit 4).

So, while banks have correctly focused on building digital experiences to enable customers to bank in their channel of choice and self-serve for many interactions, there is still an opportunity for banks to actively help customers migrate to digital channels. This, in turn, will likely not only drive higher customer satisfaction, but result in a lower cost-to-serve and convenience.

Banks can actively migrate customers to digital in several ways:

• Enable: Banks can streamline enrollment into digital, seamless login, pre-authentication, and more.
• Educate: They can drive awareness of new digital offerings or features with marketing and communications, such as “how-to” videos on the website and mobile app.
• Redirect: Banks can utilize in-branch and call center or IVR intercepts to direct customers to digital channels, for example, in-branch digital and co-browse tutorials.
• Motivate: They can consider charging fees for using non-digital channels, and reward employees who redirect customers to digital channels, for instance.
• Nudge: Lastly, they can encourage customers to migrate with messaging on statements, reminders in emails or mails, gamified experiences, and so forth.

A leading Latin American bank launched a holistic digital adoption campaign to drive digital migration for its new web and mobile experiences. The bank rolled out a broad advertising campaign to encourage customers to download the new mobile app, developed incentives for recurring digital users (such as digital payments), sent out targeted customer messages after non-digital transactions were completed (for instance, in branch transfers), and thoroughly trained its front-line branch employees so they could redirect customers to digital. This broad campaign resulted in a 20 percent increase in customer satisfaction, a 5 percent increase in digitally active customers, a 25 percent increase in digital payments, and a 10 percent reduction in branch costs.

(Re)establish and (re)fortify trust

Our research shows that around 60 percent of customers currently trust that their primary bank will be helpful in navigating the next financial downturn. And this number jumps to more than 80 percent for customers who report high satisfaction with the experience their bank delivers.

Banks can take several actions to establish (or reestablish) trust

First, they can be transparent for emotionally charged interactions such as the ways fees are charged and explained (for example, on statements), the status of a loan application, and how disputes are handled. One leading payments player recently underwent a company-wide program to dramatically simplify its customer communications—from everything such as statements to the terms of loan applications to product offers on its mobile app. This program resulted not only in higher customer satisfaction (CSAT) scores, but also fewer calls coming into the contact centers (for example, for customers not understanding bills or terms and conditions clauses).

Second, they can deeply know how customers want to bank and then give them the power to interact across any channel. For example, the marketing messages they want to opt into, what channel with which they prefer to interact (email, mail, phone call, or text message), and what data they would like the bank to use when making them product offers.

Third, banks can proactively identify and help customers resolve fraud by leveraging advanced analytics. Fraud resolution is one of the most emotionally charged journeys for customers, and anything that can help them feel at ease dramatically drives trust, as well as “advocacy” by the bank on their behalf. Several banks now send text messages or emails and phone customers at the first sign of potential fraud—offering customers an opportunity to “dismiss” the alert or follow through with a fraud claim. Many banks also use this to drive advocacy by removing the charge from statements while they investigate (versus charging customers first and then refunding the charge).

And last, banks can offer a window into a customer’s financial wealth, based on customer spend and transaction history, credit bureau data, balance information, interest charges, fees, and so forth. This opens the space for banks to offer a “financial-health” score for their customers. For example, a fintech company took this to the next level by not only showing a financial-health score for its clients, but also offering advice on how to improve that score (for instance, through paying off high-interest debts and savings strategies). With this move, they aimed to become more customer-centric and develop clients’ trust.

Close the loop on measurement

“You cannot manage what you don’t measure” is a common adage in business. This is especially true for customer experience. Traditionally banks have relied on surveys, which are necessary but not sufficient to achieve these capabilities. In fact, only 16 percent of chief customer experience officers believe surveys are granular enough to act on, and only 4 percent think that surveys allow them to calculate the ROI of a decision.

Organizations that measure up well do so across four capabilities: capture (how feedback is collected and integrated), interpret (how feedback is analyzed and insights produced), act (how insights are implemented), and monitor (how dashboards are updated in near real time).

• Leaders in the industry use predictive analytics, machine learning, and big data (augmenting survey data with operational data) to overcome the well-known limitations of customer feedback. 2 “Experience DNA data and analytics platform,” McKinsey, February 2023. For example, only 7 percent on average complete surveys, 25 percent believe surveys provide timely insights to act on, while just 4 percent allow banks to quantify ROI. 3 “ Prediction: The future of CX ,” McKinsey, February 24, 2021. Banks can leverage the analytics-driven customer feedback system to personalize the experience by identifying unique customer needs and trends at scale that may go unnoticed with one-off surveys.
• They can also proactively resolve issues by ensuring that drivers of customer experience are updated in real time from all available data—as opposed to the limited survey questions that are only updated sporadically to quickly resolve trouble areas.
• Banks can predict with confidence the satisfaction for 100 percent of customers with a “single source of truth” versus the 7 to 10 percent in typical survey responses.
• Lastly, they can improve “hidden” customer interaction points; that is, quickly see how customer experience changes along a customer’s interaction with various parts of a given journey.

For example, a global bank is building a capability that scores the experience of every customer based on data such as transactions, balances, recent branch and contact center experiences, and location. It then uses machine learning to predict customer satisfaction for each customer based on their individual experience. This new capability allows the bank to dramatically improve its follow-up with customers immediately after poor service experiences and identify opportunities to deepen relationships.

Ingrain the philosophy of “customer success” in every part of the organization

Customer success is a proactive, data-led, and client-centric approach that seeks to understand client priorities and help B2B customers optimize their outcomes. The customer-success discipline is well developed in high-tech companies and the software-as-a-service (SaaS) industry but is still only slowly finding relevance in banking. Customer success equates to understanding existing B2B customers’ needs and helping them achieve their objectives (which often includes improved outcomes or experience for an end consumer or user). As a result, customer success can be successful in driving growth and reducing churn, while also increasing adoption and usage of products and services.

To implement an effective customer success model, banks can consider taking the following steps:

Build customer success capabilities: Like sales, customer success is a discipline with established practices. Setting up a customer success function requires dedicated capability building, especially if a bank is converting a team of existing client-relationship executives (such as bankers or account managers) to become customer-success managers.

Create capacity for high-value activities: In many organizations, an existing account or relationship manager is inundated with servicing requests and has limited capacity to be proactive. To create space, banks could find a way to reduce the demand on these teams to react to client “problems”, through product improvements, automation, or off-loading servicing activities to lower-cost teams.

Define the operating model with sales: Successful customer success representatives will uncover upsell and cross-sell opportunities as they work with clients to help them achieve their objectives. Therefore, it is critical to have a defined operating model and success-to-sales motion, which may differ based on the customer segmentation and coverage model (for example, teams of customer success and sales reps working together on accounts or using a model that “passes on” customer success opportunities to the sales team).

Measure customer health: A deep understanding of customer health is beneficial to customer success as it helps indicate likely-to-churn customers and assists customer success teams to prioritize how to invest their time across their customer portfolio. Banks can use all the data they have available for a customer—such as financial performance, industry trends, engagement with product and digital journeys, customer satisfaction (for instance, NPS, CSAT), product performance, and the ability to meet customer service level agreements—to develop a predictive measure of “customer health” as a key enabler of customer success.

For instance, a large wealth management player is moving to a customer-success model for its B2B business. It has introduced a “teaming” coverage model, in which large customers each have a dedicated representative for sales and customer success. The company has also defined an operating model for how sales, customer success, and sales operations will work together throughout the customer’s lifecycle. This new model has helped it better understand the needs of its customers and increased the opportunities to pursue new products and services with its existing customer base.

Bringing it all together

So how can banks achieve CX success in a competitive environment where customers want more, quickly? The good news is that we have seen companies attain leading positions by addressing three core building blocks of customer experience: a clearly defined, strong aspiration; a disciplined transformation journey; and thoughtful deployment of new capabilities such as analytics (Exhibit 5). 4 Victoria Bough, Ralph Breuer, Nicolas Maechler, and Kelly Ungerman, “ The three building blocks of successful customer experience transformation ,” McKinsey, October 27, 2020.

McKinsey research shows that this approach has delivered powerful results: a 15 to 20 percent increase in sales conversion rates, a 20 to 50 percent decline in service costs, and a 10 to 20 percent improvement in customer satisfaction. 5 Victoria Bough, Ralph Breuer, Nicolas Maechler, and Kelly Ungerman, “ The three building blocks of successful customer experience transformation ,” McKinsey, October 27, 2020.

By using these building blocks to achieve successful customer-centric transformations, and embedding the five bold moves described above, banks can take gold in the customer-experience race and attain a competitive advantage that boosts growth, lowers costs, and provides superior customer satisfaction.

Shital Chheda is a partner in McKinsey’s Chicago office; Jonathan Goldstein is an associate partner in the San Francisco office, where Robert Schiff is a senior partner; and Tim Natriello is an associate partner in the New York office.

The authors wish to thank Tim Bail, Anubhav Choudhury, Kate Ford, Alex Lapides, and Adrian Nelson for their contributions to this article.

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What is the impact and value of hurricane forecasts? We study this question using newly-collected forecast data for major US hurricanes since 2005. We find higher wind speed forecasts increase pre-landfall protective spending, but erroneous under-forecasts increase post-landfall damage and rebuilding expenditures. Our main contribution is a new theoretically-grounded approach for estimating the marginal value of forecast improvements. We find that the average annual improvement reduced total per-hurricane costs, inclusive of unobserved protective spending, by $700,000 per county. Improvements since 2007 reduced costs by 19%, averaging $5 billion per hurricane. This exceeds the annual budget for all federal weather forecasting.

Funding for this project was provided by Grant NA20OAR4320472 from the National Oceanic and Atmospheric Administration. This manuscript benefited from discussions by Jeff Shrader and Manuel Linsenmeier, and comments from Christopher Costello, Gabriel Lade, Derek Lemoine, Cynthia Lin-Lawell, Antony Millner, Christopher Parmeter, Christopher Timmins, and Jinhua Zhao, as well as from feedback by seminar participants at Cornell University, St. John’s University, the University of Miami, the Colorado Environmental Economics Workshop, the Kansas City Fed, the Northeast Environmental Workshop, the Occasional Workshop, the Seminar Series of the National Oceanic and Atmospheric Administration, and the Summer Conference of the Association of Environmental and Resource Economists. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• 06 Jun 2023

The Opioid Crisis, CEO Pay, and Shareholder Activism

In 2020, AmerisourceBergen Corporation, a Fortune 50 company in the drug distribution industry, agreed to settle thousands of lawsuits filed nationwide against the company for its opioid distribution practices, which critics alleged had contributed to the opioid crisis in the US. The $6.6 billion global settlement caused a net loss larger than the cumulative net income earned during the tenure of the company’s CEO, which began in 2011. In addition, AmerisourceBergen’s legal and financial troubles were accompanied by shareholder demands aimed at driving corporate governance changes in companies in the opioid supply chain. Determined to hold the company’s leadership accountable, the shareholders launched a campaign in early 2021 to reject the pay packages of executives. Should the board reduce the executives’ pay, as of means of improving accountability? Or does punishing the AmerisourceBergen executives for paying the settlement ignore the larger issue of a business’s responsibility to society? Harvard Business School professor Suraj Srinivasan discusses executive compensation and shareholder activism in the context of the US opioid crisis in his case, “The Opioid Settlement and Controversy Over CEO Pay at AmerisourceBergen.”

• 16 May 2023
• In Practice

After Silicon Valley Bank's Flameout, What's Next for Entrepreneurs?

Silicon Valley Bank's failure in the face of rising interest rates shook founders and funders across the country. Julia Austin, Jeffrey Bussgang, and Rembrand Koning share key insights for rattled entrepreneurs trying to make sense of the financing landscape.

• 27 Apr 2023

Equity Bank CEO James Mwangi: Transforming Lives with Access to Credit

James Mwangi, CEO of Equity Bank, has transformed lives and livelihoods throughout East and Central Africa by giving impoverished people access to banking accounts and micro loans. He’s been so successful that in 2020 Forbes coined the term “the Mwangi Model.” But can we really have both purpose and profit in a firm? Harvard Business School professor Caroline Elkins, who has spent decades studying Africa, explores how this model has become one that business leaders are seeking to replicate throughout the world in her case, “A Marshall Plan for Africa': James Mwangi and Equity Group Holdings.” As part of a new first-year MBA course at Harvard Business School, this case examines the central question: what is the social purpose of the firm?

• 25 Apr 2023

Using Design Thinking to Invent a Low-Cost Prosthesis for Land Mine Victims

Bhagwan Mahaveer Viklang Sahayata Samiti (BMVSS) is an Indian nonprofit famous for creating low-cost prosthetics, like the Jaipur Foot and the Stanford-Jaipur Knee. Known for its patient-centric culture and its focus on innovation, BMVSS has assisted more than one million people, including many land mine survivors. How can founder D.R. Mehta devise a strategy that will ensure the financial sustainability of BMVSS while sustaining its human impact well into the future? Harvard Business School Dean Srikant Datar discusses the importance of design thinking in ensuring a culture of innovation in his case, “BMVSS: Changing Lives, One Jaipur Limb at a Time.”

• 18 Apr 2023

What Happens When Banks Ditch Coal: The Impact Is 'More Than Anyone Thought'

Bank divestment policies that target coal reduced carbon dioxide emissions, says research by Boris Vallée and Daniel Green. Could the finance industry do even more to confront climate change?

The Best Person to Lead Your Company Doesn't Work There—Yet

Recruiting new executive talent to revive portfolio companies has helped private equity funds outperform major stock indexes, says research by Paul Gompers. Why don't more public companies go beyond their senior executives when looking for top leaders?

• 11 Apr 2023

A Rose by Any Other Name: Supply Chains and Carbon Emissions in the Flower Industry

Headquartered in Kitengela, Kenya, Sian Flowers exports roses to Europe. Because cut flowers have a limited shelf life and consumers want them to retain their appearance for as long as possible, Sian and its distributors used international air cargo to transport them to Amsterdam, where they were sold at auction and trucked to markets across Europe. But when the Covid-19 pandemic caused huge increases in shipping costs, Sian launched experiments to ship roses by ocean using refrigerated containers. The company reduced its costs and cut its carbon emissions, but is a flower that travels halfway around the world truly a “low-carbon rose”? Harvard Business School professors Willy Shih and Mike Toffel debate these questions and more in their case, “Sian Flowers: Fresher by Sea?”

Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide

More companies are bringing seemingly unrelated businesses together in new ways, challenging traditional stock categories. MarcAntonio Awada and Suraj Srinivasan discuss how applying machine learning to regulatory data could reveal new opportunities for investors.

• 07 Apr 2023

When Celebrity ‘Crypto-Influencers’ Rake in Cash, Investors Lose Big

Kim Kardashian, Lindsay Lohan, and other entertainers have been accused of promoting crypto products on social media without disclosing conflicts. Research by Joseph Pacelli shows what can happen to eager investors who follow them.

• 31 Mar 2023

Can a ‘Basic Bundle’ of Health Insurance Cure Coverage Gaps and Spur Innovation?

One in 10 people in America lack health insurance, resulting in $40 billion of care that goes unpaid each year. Amitabh Chandra and colleagues say ensuring basic coverage for all residents, as other wealthy nations do, could address the most acute needs and unlock efficiency.

• 23 Mar 2023

As Climate Fears Mount, More Investors Turn to 'ESG' Funds Despite Few Rules

Regulations and ratings remain murky, but that's not deterring climate-conscious investors from paying more for funds with an ESG label. Research by Mark Egan and Malcolm Baker sizes up the premium these funds command. Is it time for more standards in impact investing?

• 14 Mar 2023

What Does the Failure of Silicon Valley Bank Say About the State of Finance?

Silicon Valley Bank wasn't ready for the Fed's interest rate hikes, but that's only part of the story. Victoria Ivashina and Erik Stafford probe the complex factors that led to the second-biggest bank failure ever.

• 13 Mar 2023

What Would It Take to Unlock Microfinance's Full Potential?

Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better.

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