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• 22 Apr 2024
• Research & Ideas

When Does Impact Investing Make the Biggest Impact?

More investors want to back businesses that contribute to social change, but are impact funds the only approach? Research by Shawn Cole, Leslie Jeng, Josh Lerner, Natalia Rigol, and Benjamin Roth challenges long-held assumptions about impact investing and reveals where such funds make the biggest difference.

• 23 Jan 2024

More Than Memes: NFTs Could Be the Next Gen Deed for a Digital World

Non-fungible tokens might seem like a fad approach to selling memes, but the concept could help companies open new markets and build communities. Scott Duke Kominers and Steve Kaczynski go beyond the NFT hype in their book, The Everything Token.

• 12 Sep 2023

How Can Financial Advisors Thrive in Shifting Markets? Diversify, Diversify, Diversify

Financial planners must find new ways to market to tech-savvy millennials and gen Z investors or risk irrelevancy. Research by Marco Di Maggio probes the generational challenges that advisory firms face as baby boomers retire. What will it take to compete in a fintech and crypto world?

• 17 Aug 2023

‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy Crypto

Bitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain?

• 17 Jul 2023

Money Isn’t Everything: The Dos and Don’ts of Motivating Employees

Dangling bonuses to checked-out employees might only be a Band-Aid solution. Brian Hall shares four research-based incentive strategies—and three perils to avoid—for leaders trying to engage the post-pandemic workforce.

• 20 Jun 2023
• Cold Call Podcast

Elon Musk’s Twitter Takeover: Lessons in Strategic Change

In late October 2022, Elon Musk officially took Twitter private and became the company’s majority shareholder, finally ending a months-long acquisition saga. He appointed himself CEO and brought in his own team to clean house. Musk needed to take decisive steps to succeed against the major opposition to his leadership from both inside and outside the company. Twitter employees circulated an open letter protesting expected layoffs, advertising agencies advised their clients to pause spending on Twitter, and EU officials considered a broader Twitter ban. What short-term actions should Musk take to stabilize the situation, and how should he approach long-term strategy to turn around Twitter? Harvard Business School assistant professor Andy Wu and co-author Goran Calic, associate professor at McMaster University’s DeGroote School of Business, discuss Twitter as a microcosm for the future of media and information in their case, “Twitter Turnaround and Elon Musk.”

• 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.

• 16 Feb 2023

ESG Activists Met the Moment at ExxonMobil, But Did They Succeed?

Engine No. 1, a small hedge fund on a mission to confront climate change, managed to do the impossible: Get dissident members on ExxonMobil's board. But lasting social impact has proved more elusive. Case studies by Mark Kramer, Shawn Cole, and Vikram Gandhi look at the complexities of shareholder activism.

• Open access
• 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.

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Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis

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Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance. In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. We find that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Furthermore, we show that the selected articles fall into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk and default evaluation, cryptocurrencies, derivatives, credit risk in banks, investor sentiment analysis and foreign exchange management, respectively. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.

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Introduction

The first two decades of the twenty-first century have experienced an unprecedented way of technological progress, which has been driven by advances in the development of cutting-edge digital technologies and applications in Artificial Intelligence (AI). Artificial intelligence is a field of computer science that creates intelligent machines capable of performing cognitive tasks, such as reasoning, learning, taking action and speech recognition, which have been traditionally regarded as human tasks (Frankenfield 2021 ). AI comprises a broad and rapidly growing number of technologies and fields, and is often regarded as a general-purpose technology, namely a technology that becomes pervasive, improves over time and generates complementary innovation (Bresnahan and Trajtenberg 1995 ). As a result, it is not surprising that there is no consensus on the way AI is defined (Van Roy et al. 2020 ). An exhaustive definition has been recently proposed by Acemoglu and Restrepo ( 2020 , p.1), who assert that Artificial Intelligence is “(…) the study and development of intelligent (machine) agents, which are machines, software or algorithms that act intelligently by recognising and responding to their environment.” Even though it is often difficult to draw precise boundaries, this promising and rapidly evolving field mainly comprises machine learning, deep learning, NLP (natural language processing) platforms, predictive APIs (application programming interface), image recognition and speech recognition (Martinelli et al. 2021 ).

The term “Artificial intelligence” was first coined by John McCarthy in 1956 during a conference at Dartmouth College to describe “thinking machines” (Buchanan 2019 ). However, until 2000, the lack of storage capability and low computing power prevented any progress in the field. Accordingly, governments and investors lost their interest and AI fell short of financial support and funding in 1974–1980 and again in 1987–1993. These periods of funding shortage are also known as “AI winters Footnote 1 ”.

However, the most significant development and spread of AI-related technologies is much more recent, and has been prompted by the availability of large unstructured databases, the explosion of computing power, and the rise in venture capital intended to support innovative, technological projects (Ernst et al. 2018 ). One of the most distinctive The term AI winter first appeared in 1characteristics of AI technologies is that, unlike industrial robots, which need to receive specific instructions, generally provided by a software, before they perform any action, can learn for themselves how to map information about the environment, such as visual and tactile data from a robot’s sensors, into instructions sent to the robot’s actuators (Raj and Seamans 2019 ). Additionally, as remarked by Ernst et al. ( 2018 ), whilst industrial robots mostly perform manual tasks, AI technologies are able to carry out activities that, until some years ago, were still regarded as typically human, i.e. what Ernst and co-authors label as “mental tasks”.

The adoption of AI is likely to have remarkable implications for the subjects adopting them and, more in general, for the economy and the society. In particular, it is expected to contribute to the growth of the global GDP, which, according to a study conducted by Pricewater-house-Coopers (PwC) and published in 2017, is likely to increase by up to 14% by 2030. Moreover, companies adopting AI technologies sometimes report better performance (Van Roy et al. 2020 ). Concerning the geographic dimension of this field, North America and China are the leading investors and are expected to benefit the most from AI-driven economic returns. Europe and emerging markets in Asia and South America will follow, with moderate profits owing to fewer and later investments (PwC 2017 ). AI is going to affect labour markets as well. The demand for high-skilled employees is expected to increase, whilst the demand for low-skilled jobs is likely to shrink because of automation; the resulting higher unemployment rate, however, is going to be offset by the new job opportunities offered by AI (Ernst et al. 2018 ; Acemoglu and Restrepo 2020 ).

AI solutions have been introduced in every major sector of the economy; a sector that is witnessing a profound transformation led by the ongoing technological revolution is the financial one. Financial institutions, which rely heavily on Big Data and process automation, are indeed in a “unique position to lead the adoption of AI” (PwC 2020 ), which generates several benefits: for instance, it encourages automation of manufacturing processes which in turn enhances efficiency and productivity. Next, since machines are immune to human errors and psychological factors, it ensures accurate and unbiased predictive analytics and trading strategies. AI also fosters business model innovation and radically changes customer relationships by promoting customised digital finance, which, together with the automation of processes, results in better service efficiency and cost-saving (Cucculelli and Recanatini 2022 ). Furthermore, AI is likely to have substantial implications for financial conduct and prudential supervisors, and it also has the potential to help supervisors identify potential violations and help regulators better anticipate the impact of changes in regulation (Wall 2018 ). Additionally, complex AI/machine learning algorithms allow Fintech lenders to make fast (almost instantaneous) credit decisions, with benefits for both the lenders and the consumers (Jagtiani and John 2018 ). Intelligent devices in Finance are used in a number of areas and activities, including fraud detection, algorithmic trading and high-frequency trading, portfolio management, credit decisions based on credit scoring or credit approval models, bankruptcy prediction, risk management, behavioural analyses through sentiment analysis and regulatory compliance.

In recent years, the adoption of AI technologies in a broad range of financial applications has received increasing attention by scholars; however, the extant literature, which is reviewed in the next section, is quite broad and heterogeneous in terms of research questions, country and industry under scrutiny, level of analysis and method, making it difficult to draw robust conclusions and to understand which research areas require further investigation. In the light of these considerations, we conduct an extensive review of the research on the use of AI in Finance thorough which we aim to provide a comprehensive account of the current state of the art and, importantly, to identify a number of research questions that are still (partly) unanswered. This survey may serve as a useful roadmap for researchers who are not experts of this topic and could find it challenging to navigate the extensive and composite research on this subject. In particular, it may represent a useful starting point for future empirical contributions, as it provides an account of the state of the art and of the issues that deserve further investigation. In doing so, this study complements some previous systematic reviews on the topic, such as the ones recently conducted by Hentzen et al. ( 2022b ) and (Biju et al. 2020 ), which differ from our work in the following main respects: Hentzen and co-authors’ study focuses on customer-facing financial services, whilst the valuable contribution of Biju et al. poses particular attention to relevant technical aspects and the assessment of the effectiveness and the predictive capability of machine learning, AI and deep learning mechanisms within the financial sphere; in doing so, it covers an important issue which, however, is out of the scope of our work.

From our review, it emerges that, from the beginning of the XXI century, the literature on this topic has significantly expanded, and has covered a broad variety of countries, as well as several AI applications in finance, amongst which Predictive/forecasting systems, Classification /detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Additionally, we show that the selected articles can be grouped into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk & default evaluation, cryptocurrencies, derivatives, credit risks in banks, investor sentiment analysis and foreign exchange management, respectively.

The balance of this paper is organised as follows: Sect. “ Methodology ” shortly presents the methodology. Sect. “ A detailed account of the literature on AI in Finance ” illustrates the main results of the bibliometric analysis and the content analysis. Sect. “ Issues that deserve further investigation ” draws upon the research streams described in the previous section to pinpoint several potential research avenues. Sect. “ Conclusions ” concludes. Finally, Appendix 1 clarifies some AI-related terms and definitions that appear several times throughout the paper, whilst Appendix 2 provides more information on some of the articles under scrutiny.

Methodology

To conduct a sound review of the literature on the selected topic, we resort to two well-known and extensively used approaches, namely bibliometric analysis and content analysis. Bibliometric analysis is a popular and rigorous method for exploring and analysing large volumes of scientific data which allows us to unpack the evolutionary nuances of a specific field whilst shedding light on the emerging areas in that field (Donthu et al. 2021 ). In this study, we perform bibliometric analysis using HistCite, a popular software package developed to support researchers in elaborating and visualising the results of literature searches in the Web of Science platform. Specifically, we employ HistCite to recover the annual number of publications, the number of forward citations (which we use to identify the most influential journals and articles) and the network of co-citations, namely, all the citations received and given by journals belonging to a certain field, which help us identify the major research streams described in Sect. “ Identification of the major research streams ”. After that, to delve into the contents of the most pertinent studies on AI in finance, we resort to traditional content analysis, a research method that provides a systematic and objective means to make valid inferences from verbal, visual, or written data which, in turn, permit to describe and quantify specific phenomena (Downe-Wambolt 1992 ).

In order to identify the sample of studies on which bibliometric and content analysis were performed, we proceeded as follows. First, we searched for pertinent articles published in English be-tween 1950 and March 2021. Specifically, we scrutinised the “Finance”, “Economics”, “Business Finance” and “Business” sections of the “Web of Science” (WoS) database using the keyword “finance” together with an array of keywords concerning Artificial Intelligence (i.e. “Finance” AND (“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning” OR “Neural Networks*” OR “Natural Language Processing*” OR “Algorithmic Trading*” OR “Artificial Neural Network” OR “Robot*” OR “Automation” OR “Text Mining” OR “Data Mining” OR “Soft Computing” OR “Fuzzy Logic Analysis” OR “Biometrics*” OR “Geotagging” OR “Wearable*” OR “IoT” OR “Internet of Thing*” OR “digitalization” OR “Artificial Neutral Networks” OR “Big Data” OR “Industry 4.0″ OR “Smart products*” OR Cloud Computing” OR “Digital Technologies*”). In doing so, we ended up with 1,218 articles. Next, two researchers independently analysed the title, abstract and content of these papers and kept only those that address the topic under scrutiny in a non-marginal and non-trivial way. This second step reduced the number of eligible papers to 892, which were used to perform the first part of the bibliometric analysis. Finally, we delved into the contents of the previously selected articles and identified 110 contributions which specifically address the adoption and implications in Finance of AI tools focussing on the economic dimension of the topic, and which are employed in the second part of the bibliometric analysis and in the content analysis.

A detailed account of the literature on AI in Finance

In this section, we explore the patterns and trends in the literature on AI in Finance in order to obtain a compact but exhaustive account of the state of the art. Specifically, we identify some relevant bibliographic characteristics using the tools of bibliometric analysis. After that, focussing on a sub-sample of papers, we conduct a preliminary assessment of the selected studies through a content analysis and detect the main AI applications in Finance. Finally, we identify and briefly describe ten major research streams.

Main results of the bibliometric analysis

First, using HistCite and considering the sample of 892 studies, we computed, for each year, the number of publications related to the topic “AI in Finance”. The corresponding publication trend is shown in Fig.  1 , which plots both the annual absolute number of sampled papers (bar graph in blue) and the ratio between the latter and the annual overall amount of publications (indexed in Scopus) in the finance area (line graph in orange). We also compute relative numbers to see if the trend emerging from the selected studies is not significantly attributable to a “common trend” (i.e. to the fact that, in the meantime, also the total number of publications in the financial area has significantly increased). It can be noted that both graphs exhibit a strong upward trend from 2015 onwards; during the most recent years, the pace of growth and the degree of pervasiveness of AI adoption in the financial sphere have indeed remarkably strengthened, and have become the subject of a rapidly growing number of research articles.

Publication Trend, 1992–2021

After that, focussing on the more pertinent (110) articles, we checked the journals in which these studies were published. Table 1 presents the top-ten list of journals reported in the Academic Journal Guide-ABS List 2020 and ranked on the basis of the total global citation score (TGCS), which captures the number of times an article is cited by other articles that deal with the same topic and are indexed in the WoS database. For each journal, we also report the total number of studies published in that journal. We can notice that the most influential journals in terms of TGCS are the Journal of Finance (with a TGCS equal to 1283) and the Journal of Banking and Finance (with a TGCS of 1253), whilst the journals containing the highest number of articles on the topic are Quantitative Finance (68 articles) and Intelligent Systems in Accounting, Finance and Management (43).

Finally, Fig.  2 provides a visual representation of the citation-based relationships amongst papers starting from the most-cited papers, which we obtained using the Java application CiteSpace.

Source: authors’ elaboration of data from Web of Science; visualisation produced using CiteSpace

Citation Mapping and identification of the research streams.

Preliminary results of the content analysis

In this paragraph, we shortly illustrate some relevant characteristics of our sub-sample made up of 110 studies, including country and industry coverage, method and underpinning theoretical background. Table 2 comprises the list of countries under scrutiny, and, for each of them, a list of papers that perform their analysis on that country. We can see that our sample exhibits significant geographical heterogeneity, as it covers 74 countries across all continents; however, the most investigated areas are three, that is Europe, the US and China. These results corroborate the fact that the above-mentioned regions are the leaders of the AI-driven financial industry, as suggested by PwC ( 2017 ). The United States, in particular, are considered the “early adopters” of AI and are likely to benefit the most from this source of competitive advantage. More lately, emerging countries in Southeast Asia and the Middle East have received growing interest. Finally, a smaller number of papers address underdeveloped regions in Africa and various economies in South America.

The most investigated sectors are reported in Table  3 . We can notice that, although it primarily deals with banking and financial services, the extant research has addressed the topic in a vast array of industries. This confirms that the application potential of AI is very broad, and that any industry may benefit from it.

Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature. As shown in Table  4 , 73 (out of 110) papers explicitly refer to some theoretical framework. Specifically, ten of them (14%) resort to computational learning theory; this theory, which is an extension of statistical learning, provides researchers with a theoretical guide for finding the most suitable learning model for a given problem, and is regarded as one of the most important and most used theories in the field. Specific theories concerning types of neural networks and learning methods are used too, such as the fuzzy set theory, which is mentioned in 8% of the sample, and to a lesser extent, the Naive Bayes theorem, the theory of neural networks, the theory of genetic programming and the TOPSIS analytical framework. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973 ) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers. Finally, bankruptcy theories support business failure forecasts, whilst other theoretical underpinnings concern mathematical and probability concepts.

The content analysis also provides information on the main types of companies under scrutiny. Table 5 indicates that 30 articles (out of 110) focus on large companies listed on stock exchanges, whilst only 16 studies cover small and medium enterprises. Similarly, trading and digital platforms are examined in 16 papers that deal with derivatives and cryptocurrencies.

Furthermore, Table  6 summarises the key methods applied in the literature, which are divided by category (note that all the papers employ more than one method). Looking at the table, we see that machine learning and artificial neural networks are the most popular ones (they are employed in 41 and 51 articles, respectively). The majority of the papers resort to different approaches to compare their results with those obtained through autoregressive and regression models or conventional statistics, which are used as the benchmark; therefore, there may be some overlaps. Nevertheless, we notice that support vector machine and random forest are the most widespread machine learning methods. On the other hand, the use of artificial neural networks (ANNs) is highly fragmented. Backpropagation, Recurrent, and Feed-Forward NNs are considered basic neural nets and are commonly employed. Advanced NNs, such as Higher-Order Neural network (HONN) and Long Short-Term Memory Networks (LSTM), are more performing than their standard version but also much more complicated to apply. These methods are usually compared to autoregressive models and regressions, such as ARMA, ARIMA, and GARCH. Finally, we observe that almost all the sampled papers are quantitative, whilst only three of them are qualitative and four of them consist in literature reviews.

A taxonomy of AI applications in Finance

After scrutinising some relevant features of the papers, we make a step forward and outline a taxonomy of AI applications used in Finance and tackled by previous literature. The main uses of AI in Finance and the papers that address each of them are summarised in Table  7 .

Many research papers (39 out of 110) employ AI as a predictive instrument for forecasting stock prices, performance and volatility. In 23 papers, AI is employed in classification problems and warning systems to detect credit risk and frauds, as well as to monitor firm or bank performance. The former use of AI permits to classify firms into two categories based on qualitative and quantitative data; for example, we may have distressed or non-distressed, viable–nonviable, bankrupt–non-bankrupt, or financially healthy–not healthy, good–bad, and fraud–not fraud. Warning systems follow a similar principle: after analysing customers’ financial behaviour and classifying potential fraud issues in bank accounts, alert models signal to the bank unusual transactions. Additionally, we see that 14 articles employ text mining and data mining language recognition, i.e. natural language processing, as well as sentiment analysis. This may be the starting point of AI-driven behavioural analysis in Finance. Amongst others, trading models and algorithmic trading are further popular aspects of AI widely analysed in the literature. Moreover, interest in Robo-advisory is growing in the asset investment field. Finally, less studied AI applications concern the modelling capability of algorithms and traditional machine learning and neural networks.

Identification of the major research streams

Drawing upon the co-citation analysis mentioned in Sect. " Methodology ", we detected ten main research streams: (1) AI and the stock market; (2) AI and Trading Models; (3) AI and Volatility Forecasting; (4) AI and Portfolio Management; (5) AI and Performance, Risk, and Default Valuation; (6) AI and Bitcoin, Cryptocurrencies; (7) AI and Derivatives; (8) AI and Credit Risk in Banks; (9) AI and Investor Sentiments Analysis; (10) AI and Foreign Exchange Management. Some research streams can be further divided into sub-streams as they deal with various aspects of the same main topic. In this section, we provide a compact account for each of the aforementioned research streams. More detailed information on some of the papers fuelling them is provided in Appendix 2.

Stream 01: AI and the stock market

The stream “AI and the Stock Market” comprises two sub-streams, namely algorithmic trading and stock market, and AI and stock price prediction. The first sub-stream deals with the impact of algorithmic trading (AT) on financial markets. In this regard, Herdershott et al. ( 2011 ) argue that AT increases market liquidity by reducing spreads, adverse selection, and trade-related price discovery. This results in a lowered cost of equity for listed firms in the medium–long term, especially in emerging markets (Litzenberger et al. 2012 ). As opposed to human traders, algorithmic trading adjusts faster to information and generates higher profits around news announcements thanks to better market timing ability and rapid executions (Frino et al. 2017 ). Even though high-frequency trading (a subset of algorithmic trading) has sometimes increased volatility related to news or fundamentals, and transmitted it within and across industries, AT has overall reduced return volatility variance and improved market efficiency (Kelejian and Mukerji 2016 ; Litzenberger et al. 2012 ).

The second sub-stream investigates the use of neural networks and traditional methods to forecast stock prices and asset performance. ANNs are preferred to linear models because they capture the non-linear relationships between stock returns and fundamentals and are more sensitive to changes in variables relationships (Kanas 2001 ; Qi 1999 ). Dixon et al. ( 2017 ) argue that deep neural networks have strong predictive power, with an accuracy rate equal to 68%. Also, Zhang et al. ( 2021 ) propose a model, the Long Short-Term Memory Networks (LSTM), that outperforms all classical ANNs in terms of prediction accuracy and rational time cost, especially when various proxies of online investor attention (such as the internet search volume) are considered.

Stream 02: AI and trading models

From the review of the literature represented by this stream, it emerges that neural networks and machine learning algorithms are used to build intelligent automated trading systems. To give some examples, Creamer and Freund ( 2010 ) create a machine learning-based model that analyses stock price series and then selects the best-performing assets by suggesting a short or long position. The model is also equipped with a risk management overlayer preventing the transaction when the trading strategy is not profitable. Similarly, Creamer ( 2012 ) uses the above-mentioned logic in high-frequency trading futures: the model selects the most profitable and less risky futures by sending a long or short recommendation. To construct an efficient trading model, Trippi and DeSieno ( 1992 ) combine several neural networks into a single decision rule system that outperforms the single neural networks; Kercheval and Zhang ( 2015 ) use a supervised learning method (i.e. multi-class SVM) that automatically predicts mid-price movements in high-frequency limit order books by classifying them in low-stationary-up; these predictions are embedded in trading strategies and yield positive payoffs with controlled risk.

Stream 03: AI and volatility forecasting

The third stream deals with AI and the forecasting of volatility. The volatility index (VIX) from Chicago Board Options Exchange (CBOE) is a measure of market sentiment and expectations. Forecasting volatility is not a simple task because of its very persistent nature (Fernandes et al. 2014 ). According to Fernandes and co-authors, the VIX is negatively related to the SandP500 index return and positively related to its volume. The heterogeneous autoregressive (HAR) model yields the best predictive results as opposed to classical neural networks (Fernandes et al. 2014 ; Vortelinos 2017 ). Modern neural networks, such as LSTM and NARX (nonlinear autoregressive exogenous network), also qualify as valid alternatives (Bucci 2020 ). Another promising class of neural networks is the higher-order neural network (HONN) used to forecast the 21-day-ahead realised volatility of FTSE100 futures. Thanks to its ability to capture higher-order correlations within the dataset, HONN shows remarkable performance in terms of statistical accuracy and trading efficiency over multi-layer perceptron (MLP) and the recurrent neural network (RNN) (Sermpinis et al. 2013 ).

Stream 04: AI and portfolio management

This research stream analyses the use of AI in portfolio selection. As an illustration, Soleymani and Vasighi ( 2020 ) consider a clustering approach paired with VaR analysis to improve asset allocation: they group the least risky and more profitable stocks and allocate them in the portfolio. More elaborate asset allocation designs incorporate a bankruptcy detection model and an advanced utility performance system: before adding the stock to the portfolio, the sophisticated neural network estimates the default probability of the company and asset’s contribution to the optimal portfolio (Loukeris and Eleftheriadis 2015 ). Index-tracking powered by deep learning technology minimises tracking error and generates positive performance (Kim and Kim 2020 ). The asymmetric copula method for returns dependence estimates further promotes the portfolio optimization process (Zhao et al. 2018 ). To sum up, all papers show that AI-based prediction models improve the portfolio selection process by accurately forecasting stock returns (Zhao et al. 2018 ).

Stream 05: AI and performance, risk, default valuation

This research stream comprises three sub-streams, namely AI and Corporate Performance, Risk and Default Valuation; AI and Real Estate Investment Performance, Risk, and Default Valuation; AI and Banks Performance, Risk and Default Valuation.

The first sub-stream examines corporate financial conditions to predict financially distressed companies (Altman et al. 1994 ). As an illustration, Jones et al. ( 2017 ) and Gepp et al. ( 2010 ) determine the probability of corporate default. Sabău Popa et al. ( 2021 ) predict business performance based on a composite financial index. The findings of the aforementioned papers confirm that AI-powered classifiers are extremely accurate and easy to interpret, hence, superior to classic linear models. A quite interesting paper surveys the relationship between face masculinity traits in CEOs and firm riskiness through image processing (Kamiya et al. 2018 ). The results reveal that firms lead by masculine-faced CEO have higher risk and leverage ratios and are more frequent acquirers in MandA operations.

The second sub-stream focuses on mortgage and loan default prediction (Feldman and Gross 2005 ; Episcopos, Pericli, and Hu, 1998 ). For instance, Chen et al. ( 2013 ) evaluate real estate investment returns by forecasting the REIT index; they show that the industrial production index, the lending rate, the dividend yield and the stock index influence real estate investments. All the forecasting techniques adopted (i.e. supervised machine learning and ANNs) outperform linear models in terms of efficiency and precision.

The third sub-stream deals with banks’ performance. In contradiction with past research, a text mining study argues that the most important risk factors in banking are non-financial, i.e. regulation, strategy and management operation. However, the findings from text analysis are limited to what is disclosed in the papers (Wei et al. 2019 ). A highly performing NN-based study on the Malaysian and Islamic banking sector asserts that negative cost structure, cultural aspects and regulatory barriers (i.e. low competition) lead to inefficient banks compared to the U.S., which, on the contrary, are more resilient, healthier and well regulated (Wanke et al. 2016a, b, c, d; Papadimitriou et al. 2020 ).

Stream 06: AI and cryptocurrencies

Although algorithms and AI advisors are gaining ground, human traders still dominate the cryptocurrency market (Petukhina et al. 2021 ). For this reason, substantial arbitrage opportunities are available in the Bitcoin market, especially for USD–CNY and EUR–CNY currency pairs (Pichl and Kaizoji 2017 ). Concerning daily realised volatility, the HAR model delivers good results. Likewise, the feed-forward neural network effectively approximates the daily logarithmic returns of BTCUSD and the shape of their distribution (Pichl and Kaizoji 2017 ).

Additionally, the Hierarchical Risk Parity (HRP) approach, an asset allocation method based on machine learning, represents a powerful risk management tool able to manage the high volatility characterising Bitcoin prices, thereby helping cryptocurrency investors (Burggraf 2021 ).

Stream 07: AI and derivatives

ANNs and machine learning models are accurate predictors in pricing financial derivatives. Jang and Lee ( 2019 ) propose a machine learning model that outperforms traditional American option pricing models: the generative Bayesian NN; Culkin and Das ( 2017 ) use a feed-forward deep NN to reproduce Black and Scholes’ option pricing formula with a high accuracy rate. Similarly, Chen and Wan ( 2021 ) suggest a deep NN for American option and deltas pricing in high dimensions. Funahashi ( 2020 ), on the contrary, rejects deep learning for option pricing due to the instability of the prices, and introduces a new hybrid method that combines ANNs and asymptotic expansion (AE). This model does not directly predict the option price but measures instead, the difference between the target (i.e. derivative price) and its approximation. As a result, the ANN becomes faster, more accurate and “lighter” in terms of layers and training data volume. This innovative method mimics a human learning process when one learns about a new object by recognising its differences from a similar and familiar item (Funahashi 2020 ).

Stream 08: AI and credit risk in banks

The research stream labelled “AI and Credit Risk in Banks” Footnote 2 includes the following sub-streams: AI and Bank Credit Risk; AI and Consumer Credit Risk and Default; AI and Financial Fraud detection/ Early Warning System; AI and Credit Scoring Models.

The first sub-stream addresses bank failure prediction. Machine learning and ANNs significantly outperform statistical approaches, although they lack transparency (Le and Viviani 2018 ). To overcome this limitation, Durango‐Gutiérrez et al. ( 2021 ) combine traditional methods (i.e. logistic regression) with AI (i.e. Multiple layer perceptron -MLP), thus gaining valuable insights on explanatory variables. With the scope of preventing further global financial crises, the banking industry relies on financial decision support systems (FDSSs), which are strongly improved by AI-based models (Abedin et al. 2019 ).

The second sub-stream compares classic and advanced consumer credit risk models. Supervised learning tools, such as SVM, random forest, and advanced decision trees architectures, are powerful predictors of credit card delinquency: some of them can predict credit events up to 12 months in advance (Lahmiri 2016 ; Khandani et al. 2010 ; Butaru et al. 2016 ). Jagric et al. ( 2011 ) propose a learning vector quantization (LVQ) NN that better deals with categorical variables, achieving an excellent classification rate (i.e. default, non-default). Such methods overcome logit-based approaches and result in cost savings ranging from 6% up to 25% of total losses (Khadani et al. 2010 ).

The third group discusses the role of AI in early warning systems. On a retail level, advanced random forests accurately detect credit card fraud based on customer financial behaviour and spending pattern, and then flag it for investigation (Kumar et al. 2019 ). Similarly, Coats and Fant ( 1993 ) build a NN alert model for distressed firms that outperforms linear techniques. On a macroeconomic level, systemic risk monitoring models enhanced by AI technologies, i.e. k-nearest neighbours and sophisticated NNs, support macroprudential strategies and send alerts in case of global unusual financial activities (Holopainen, and Sarlin 2017 ; Huang and Guo 2021 ). However, these methods are still work-in-progress.

The last group studies intelligent credit scoring models, with machine learning systems, Adaboost and random forest delivering the best forecasts for credit rating changes. These models are robust to outliers, missing values and overfitting, and require minimal data intervention (Jones et al. 2015 ). As an illustration, combining data mining and machine learning, Xu et al. ( 2019 ) build a highly sophisticated model that selects the most important predictors and eliminates noisy variables, before performing the task.

Stream 09: AI and investor sentiment analysis

Investor sentiment has become increasingly important in stock prediction. For this purpose, sentiment analysis extracts investor sentiment from social media platforms (e.g. StockTwits, Yahoo-finance, eastmoney.com) through natural language processing and data mining techniques, and classifies it into negative or positive (Yin et al. 2020 ). The resulting sentiment is regarded either as a risk factor in asset pricing models, an input to forecast asset price direction, or an intraday stock index return (Houlihan and Creamer 2021 ; Renault 2017 ). In this respect, Yin et al. ( 2020 ) find that investor sentiment has a positive correlation with stock liquidity, especially in slowing markets; additionally, sensitivity to liquidity conditions tends to be higher for firms with larger size and a higher book-to-market ratio, and especially those operating in weakly regulated markets. As for predictions, daily news usually predicts stock returns for few days, whereas weekly news predicts returns for longer period, from one month to one quarter. This generates a return effect on stock prices, as much of the delayed response to news occurs around major events in company life, specifically earnings announcement, thus making investor sentiment a very important variable in assessing the impact of AI in financial markets. (Heston and Sinha 2017 ).

Stream 10: AI and foreign exchange management

The last stream addresses AI and the management of foreign exchange. Cost-effective trading or hedging activities in this market require accurate exchange rate forecasts (Galeshchuk and Mukherjee 2017 ). In this regard, the HONN model significantly outperforms traditional neural networks (i.e. multi-layer perceptron, recurrent NNs, Psi sigma-models) in forecasting and trading the EUR/USD currency pair using ECB daily fixing series as input data (Dunis et al. 2010 ). On the contrary, Galeshchuk and Mukherjee ( 2017 ) consider these methods as unable to predict the direction of change in the forex rates and, therefore, ineffective at supporting profitable trading. For this reason, they apply a deep NN (Convolution NNs) to forecast three main exchange rates (i.e. EUR/USD, GBP/USD, and JPY/USD). The model performs remarkably better than time series models (e.g. ARIMA: Autoregressive integrated moving average) and machine learning classifiers. To sum up, from this research stream it emerges that AI-based models, such as NARX and the above-mentioned techniques, achieve better prediction performance than statistical or time series models, as remarked by Amelot et al. ( 2021 ).

Issues that deserve further investigation

As shown in Sect. " A detailed account of the literature on AI in Finance ", the literature on Artificial Intelligence in Finance is vast and rapidly growing as technological progress advances. There are, however, some aspects of this subject that are unexplored yet or that require further investigation. In this section, we further scrutinise, through content analysis, the papers published between 2015 and 2021 (as we want to focus on the most recent research directions) in order to define a potential research agenda. Hence, for each of the ten research streams presented in Sect. " Identification of the major research streams ", we report a number of research questions that were put forward over time and are still at least partly unaddressed. The complete list of research questions is enclosed in Table  8 .

AI and the stock market

This research stream focuses on algorithmic trading (AT) and stock price prediction. Future research in the field could analyse more deeply alternative AI-based market predictors (e.g. clustering algorithms and similar learning methods) and draw up a regime clustering algorithm in order to get a clearer view of the potential applications and benefits of clustering methodologies (Law, and Shawe-Taylor 2017 ). In this regard, Litzenberger et al. ( 2012 ) and Booth et al. ( 2015 ) recommend broadening the study to market cycles and regulation policies that may affect AI models’ performance in stock prediction and algorithmic trading, respectively. Footnote 3 Furthermore, forecasting models should be evaluated with deeper order book information, which may lead to a higher prediction accuracy of stock prices (Tashiro et al. 2019 ).

AI and trading models

This research stream builds on the application of AI in trading models. Robo advisors are the evolution of basic trading models: they are easily accessible, cost-effective, profitable for investors and, unlike human traders, immune to behavioural biases. Robo advisory, however, is a recent phenomenon and needs further performance evaluations, especially in periods of financial distress, such as the post-COVID-19 one (Tao et al. 2021 ), or in the case of the so-called “Black swan” events. Conversely, trading models based on spatial neural networks (an advanced ANN) outperform all statistical techniques in modelling limit order books and suggest an extensive interpretation of the joint distribution of the best bid and best ask. Given the versatility of such a method, forthcoming research should resort to it with the aim of understanding whether neural networks with more order book information (i.e. order flow history) lead to better trading performance (Sirignano 2018 ).

AI and volatility forecasting

As previously mentioned, volatility forecasting is a challenging task. Although recent studies report solid results in the field (see Sermpinis et al. 2013 ; Vortelinos 2017 ), future work could deploy more elaborated recurrent NNs by modifying the activation function of the processing units composing the ANNs, or by adding hidden layers and then evaluate their performance (Bucci 2020 ). Since univariate time series are commonly used for realised volatility prediction, it would be interesting to also inquire about the performance of multivariate time series.

AI and portfolio management

This research stream examines the use of AI in portfolio selection strategies. Past studies have developed AI models that are capable of replicating the performance of stock indexes (known as index tracking strategy) and constructing efficient portfolios with no human intervention. In this regard, Kim and Kim ( 2020 ) suggest focussing on optimising AI algorithms to boost index-tracking performance. Soleymani and Vasighi ( 2020 ) recognise the importance of clustering algorithms in portfolio management and propose a clustering approach powered by a membership function, also known as fuzzy clustering, to further improve the selection of less risky and most profitable assets. For this reason, analysis of asset volatility through deep learning should be embedded in portfolio selection models (Chen and Ge 2021 ).

AI and performance, risk, default valuation

Bankruptcy and performance prediction models rely on binary classifiers that only provide two outcomes, e.g. risky–not risky, default–not default, good–bad performance. These methods may be restrictive as sometimes there is not a clear distinction between the two categories (Jones et al. 2017 ). Therefore, prospective research might focus on multiple outcome domains and extend the research area to other contexts, such as bond default prediction, corporate mergers, reconstructions, takeovers, and credit rating changes (Jones et al. 2017 ). Corporate credit ratings and social media data should be included as independent predictors in credit risk forecasts to evaluate their impact on the accuracy of risk-predicting models (Uddin et al. 2020 ). Moreover, it is worth evaluating the benefits of a combined human–machine approach, where analysts contribute to variables’ selection alongside data mining techniques (Jones et al. 2017 ). Forthcoming studies should also address black box and over-fitting biases (Sariev and Germano 2020 ), as well as provide solutions for the manipulation and transformation of missing input data relevant to the model (Jones et al. 2017 ).

AI and cryptocurrencies

The use of AI in the cryptocurrency market is in its infancy, and so are the policies regulating it. As the digital currency industry has become increasingly important in the financial world, future research should study the impact of regulations and blockchain progress on the performance of AI techniques applied in this field (Petukhina et al., 2021 ). Cryptocurrencies, and especially Bitcoins, are extensively used in financial portfolios. Hence, new AI approaches should be developed in order to optimise cryptocurrency portfolios (Burggraf 2021 ).

AI and derivatives

This research stream examines derivative pricing models based on AI. A valuable research area that should be further explored concerns the incorporation of text-based input data, such as tweets, blogs, and comments, for option price prediction (Jang and Lee 2019 ). Since derivative pricing is an utterly complicated task, Chen and Wan ( 2021 ) suggest studying advanced AI designs that minimise computational costs. Funahashi ( 2020 ) recognises a typical human learning process (i.e. recognition by differences) and applies it to the model, significantly simplifying the pricing problem. In the light of these considerations, prospective research may also investigate other human learning and reasoning paths that can improve AI reasoning skills.

AI and credit risk in banks

Bank default prediction models often rely solely on accounting information from banks’ financial statements. To enhance default forecast, future work should consider market data as well (Le and Viviani 2018 ). Credit risk includes bank account fraud and financial systemic risk. Fraud detection based on AI needs further experiments in terms of training speed and classification accuracy (Kumar et al. 2019 ). Early warning models, on the other hand, should be more sensitive to systemic risk. For this reason, subsequent studies ought to provide a common platform for modelling systemic risk and visualisation techniques enabling interaction with both model parameters and visual interfaces (Holopainen and Sarlin 2017 ).

AI and investor sentiment analysis

Sentiment analysis builds on text-based data from social networks and news to identify investor sentiment and use it as a predictor of asset prices. Forthcoming research may analyse the effect of investor sentiment on specific sectors (Houlihan and Creamer 2021 ), as well as the impact of diverse types of news on financial markets (Heston and Sinha 2017 ). This is important for understanding how markets process information. In this respect, Xu and Zhao ( 2022 ) propose a deeper analysis of how social networks’ sentiment affects individual stock returns. They also believe that the activity of financial influencers, such as financial analysts or investment advisors, potentially affects market returns and needs to be considered in financial forecasts or portfolio management.

AI and foreign exchange management

This research stream investigates the application of AI models to the Forex market. Deep networks, in particular, efficiently predict the direction of change in forex rates thanks to their ability to “learn” abstract features (i.e. moving averages) through hidden layers. Future work should study whether these abstract features can be inferred from the model and used as valid input data to simplify the deep network structure (Galeshchuk and Mukherjee 2017 ). Moreover, the performance of foreign exchange trading models should be assessed in financial distressed times. Further research may also compare the predictive performance of advanced times series models, such as genetic algorithms and hybrid NNs, for forex trading purposes (Amelot et al. 2021 ).

Conclusions

Despite its recent advent, Artificial Intelligence has revolutionised the entire financial system, thanks to advanced computer science and Big Data Analytics and the increasing outflow of data generated by consumers, investors, business, and governments’ activities. Therefore, it is not surprising that a growing strand of literature has examined the uses, benefits and potential of AI applications in Finance. This paper aims to provide an accurate account of the state of the art, and, in doing so, it would represent a useful guide for readers interested in this topic and, above all, the starting point for future research. To this purpose, we collected a large number of articles published in journals indexed in Web of Science (WoS), and then resorted to both bibliometric analysis and content analysis. In particular, we inspected several features of the papers under study, identified the main AI applications in Finance and highlighted ten major research streams. From this extensive review, it emerges that AI can be regarded as an excellent market predictor and contributes to market stability by minimising information asymmetry and volatility; this results in profitable investing systems and accurate performance evaluations. Additionally, in the risk management area, AI aids with bankruptcy and credit risk prediction in both corporate and financial institutions; fraud detection and early warning models monitor the whole financial system and raise expectations for future artificial market surveillance. This suggests that global financial crises or unexpected financial turmoil will be likely to be anticipated and prevented.

All in all, judging from the rapid widespread of AI applications in the financial sphere and across a large variety of countries, and, more in general, based on the growth rate exhibited by technological progress over time, we expect that the use of AI tools will further expand, both geographically, across sectors and across financial areas. Hence, firms that still struggle with coping with the latest wave of technological change should be aware of that, and try to overcome this burden in order to reap the potential benefits associated with the adoption of AI and remain competitive. In the light of these considerations, policymakers should motivate companies, especially those that have not adopted yet, or have just begun to introduce AI applications, to catch up, for instance by providing funding or training courses aimed to strengthen the complex skills required by employees dealing with these sophisticated systems and languages.

This study presents some limitations. For instance, it tackles a significant range of interrelated topics (in particular, the main financial areas affected by AI which have been the main object of past research), and then presents a concise description for each of them; other studies may decide to focus on only one or a couple of subjects and provide a more in-depth account of the chosen one(s). Also, we are aware that technological change has been progressing at an unprecedented fast and growing pace; even though we considered a significantly long time-frame and a relevant amount of studies have been released in the first two decades of the XXI century, we are aware that further advancements have been made from 2021 (the last year included in the time frame used to the select our sample); for instance, in the last few years, AI experts, policymakers, and also a growing number of scholars have been debating the potential and risks of AI-related devices, such as chatGBT and the broader and more elusive “metaverse” (see for instance Mondal et al. 2023 and Calzada 2023 , for an overview). Hence, future contributions may advance our understanding of the implications of these latest developments for finance and other important fields, such as education and health.

Data availability

Full data are available from authors upon request.

The term AI winter first appeared in 1984 as the topic of a public debate at the annual meeting of the American Association of Artificial Intelligence (AAAI). It referred to hype generated by over promises from developers, unrealistically high expectations from end users, and extensive media promotion.

Since credit risk in the banking industry remarkably differs from credit risk in firms, the two of them are treated separately.

As this issue has not been addressed in the latest papers, we include these two papers although their year of publication lies outside the established range period.

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Conceptualization: MC and SB. Methodology: SB. Investigation: MC, XG, SB. Writing: Marco Cucculelli, Xhoana Goga, Salman Bahoo and JM. Writing – Review and Editing: JM. Supervision: MC . Project Administration: MC.

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Bahoo, S., Cucculelli, M., Goga, X. et al. Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Bus Econ 4 , 23 (2024). https://doi.org/10.1007/s43546-023-00618-x

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Title: comparative study of bitcoin price prediction.

Abstract: Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance.

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