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On the Fintech Revolution: Interpreting the Forces of
Innovation, Disruption and Transformation in Financial Services
PETER GOMBER
Faculty of Economics and Business Administration, Goethe University
Theodor-W.-Adorno-Platz 4, RuW, Room 2.204, P.O. Box 69
60629 Frankfurt am Main, Germany
Email: gomber@wiwi.uni-frankfurt.de
ROBERT J. KAUFFMAN
School of Information Systems
Singapore Management University
80 Stamford Road, Singapore 198702
Email: rkauffman@smu.edu.sg
CHRIS PARKER (CORRESPONDING AUTHOR)
Supply Chain and Information Systems Department
Smeal College of Business, 411 Business Building
Pennsylvania State University
University Park, PA 16802, USA
Email: chris.parker@psu.edu
BRUCE W. WEBER
Office of the Dean, 303 Alfred Lerner Hall
Alfred Lerner College of Business and Economics, University of Delaware
20 Orchard Rd, Newark, DE 19716 USA
Email: bweber@udel.edu
Last revised: December 20, 2017
__________________________________________________________________________________
ABSTRACT: Firms in the financial services industry have been faced with the dramatic and relatively recent
emergence of new technology innovations, and process disruptions. The industry as a whole, and many new
fintech start-ups are looking for new pathways to successful business models, the creation of enhanced customer
experience, and new approaches that result in services transformation. Industry and academic observers believe
this to be more of a revolution than a set of less impactful changes, with financial services as a whole due for
major improvements in efficiency, in customer centricity and informedness. The long-standing dominance of
leading firms that are not able to figure out how to effectively hook up with the “Fintech Revolution” is at stake.
This article presents a new fintech innovation mapping approach that enables the assessment of the extent to
which there are changes and transformations in four key areas of the financial services industry. We discuss: (1)
operations management in financial services, and the changes that are occurring there; (2) technology
innovations that have begun to leverage the execution and stakeholder value associated with payments
settlement, cryptocurrencies, blockchain technologies, and cross-border payment services; (3) multiple fintech
innovations that have impacted lending and deposit services, peer-to-peer (P2P) lending and the use of social
media; (4) issues with respect to investments, financial markets, trading, risk management, robo-advisory and
related services that are influenced by blockchain and fintech innovations.
KEYWORDS AND PHRASES: Business models, digital banking, financial services, Fintech Revolution, lending,
markets, operations, payments, process transformation, technology disruption and innovation
__________________________________________________________________________________________
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Brief Bios of the Authors
PETER GOMBER holds the Chair of e-Finance, Department of Business Informatics and Information Economics,
Faculty of Economics and Business Administration, at Goethe University of Frankfurt, Germany. He graduated
in Business Administration and acquired his Ph.D. from the Institute of Information Systems at the University
of Giessen, Germany. He is Co-Chairman and Member of the Board of the E-Finance Lab, an industry-
academic partnership between Frankfurt and Darmstadt Universities, as well as leading industry partners that
include Deutsche Börse, IBM, DZ Bank, Finanz Informatik, and FactSet. He a Member of the Exchange
Council of the Frankfurt Stock Exchange, and the Consultative Working Group (CWG) of the Secondary
Market Standing Committee of the European Securities and Markets Authority (ESMA). His academic work
focuses on digital finance and fintech, IS in financial markets, market microstructure theory, regulatory impact
on financial markets / regtech, and innovative concepts for electronic trading systems. He serves as Editor-in-
Chief of EFL Quarterly, Associate Editor of Journal of Trading and International Journal of Electronic
Banking, and Member of the Editorial Board of Journal of Financial Transformation and Electronic Markets.
His past research publications have appeared in Journal of Business Economics, Journal of Information
Technology, Decision Support Systems, European Financial Management, Business & Information Systems
Engineering, Electronic Markets, and many research books and conference proceedings.
ROBERT J. KAUFFMAN currently serves as Associate Dean (Faculty), and is a Professor of Information Systems,
School of Information Systems, Singapore Management University (SMU). He holds a B.A. from the
University of Colorado, an M.A. from Cornell University, and a Ph.D. from Carnegie Mellon University. He
previously was Associate Dean (Research) at SMU. He was a Visiting Professor at the Lee Kong Chian School
of Business in 2011-2012, and held the Lee Kuan Yew Faculty Fellowship for Research Excellence, also both at
SMU. He also was a Distinguished Visiting Fellow at the Center for Digital Strategies, Tuck School of Business
at Dartmouth in 2012. Earlier, he was the W.P. Carey Chair in Information Systems at Arizona State, and served
as Professor and Director of the MIS Research Center at the University of Minnesota, while chairing the
Department of Information and Decision Science. His research focuses on technology and strategy, the
economics of IT, financial services and technology, managerial decision-making, data analytics, and e-
commerce. His work has appeared in: Information Systems Research; Journal of Management Information
Systems; MIS Quarterly; Telecom Policy; Decision Sciences; Resources, Conservation and Recycling; IBM
R&D Journal; IEEE Transactions on Engineering Management; Management Science; Review of Economics
and Statistics; and Applied Geography. He is currently Editor-in-Chief of Electronic Commerce Research and
Applications. He has served in senior leadership roles for conferences and funded industry research projects, as
well as on research review panels in Canada, Singapore, Netherlands, Finland, Hong Kong, and the U.S.
CHRIS PARKER is an Assistant Professor of Supply Chain Management in the Department of Supply Chain and
Information Management in the Smeal College of Business of Pennsylvania State University. He is also an
Institute for CyberScience (ICS) Associate and works with the Information Communication Technology for
Development (ICT4D) Consortium, housed in the College of Communications. His research addresses questions
at the interface of Operations Management and Information Systems. Prior to his arrival at State College, he
earned a Ph.D. at the London Business School in the Management Science and Operations Department, a M.S.
in Computational Finance at the Centre for Computational Finance and Economic Agents, University of Essex,
and a B.S. in Business Economics and Quantitative Studies at the Spears School of Business, Oklahoma State
University. His prior publications appear in Management Science, Journal of Management Information Systems,
and Transportation Science.
BRUCE W. WEBER is Dean of the Lerner College of Business and Economics at the University of Delaware,
where he is a Professor of Business Administration and an affiliated faculty member of the Institute for
Financial Services Analytics. He serves on the Advisory Council of the SWIFT Institute. Before becoming dean
in 2011, he was a Professor of Information Management and Subject Area Chair for Management Science and
Operations at London Business School, and earlier was on the faculty of the Stern School of Business at New
York University, and Baruch College, City University of New York, where he was the founding director of the
Subotnick Financial Services Center. He holds a Ph.D. in Decision Sciences from the Wharton School of the
University of Pennsylvania and an A.B. in Applied Math from Harvard University. His research on IT strategy,
financial sector computerization, and compliance systems has been published in leading academic journals and
has been cited in the Wall Street Journal and the Financial Times. His recent books are The Equity Trader
Course (Wiley, 2006) and Mastering Equity Trading Through Simulation (Wiley, 2010). His work evaluates the
economic advantages of digital markets, and highlights the new challenges facing traders, managers, and
regulators in an increasingly computerized financial services industry.
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In every major financial market innovation, there is a lag between early adoption and
regulatory acceptance. That is true of fintech, just as it was true of derivative products … The
maturation of fintech from its relative infancy to a more robustly understood and regulated
set of products and services will be a process, and it will take time and effort. That fintech
offers great promise in creating economic value is not in dispute; the issue is whether
fintech products and services are transparent enough for regulatory oversight and
understanding. This is going to be a challenge, as it always is for financial market
innovators. But it is essential for the long-term success
and public acceptance of fintech.
Arthur Levitt, Board Member, Bloomberg, and Ex-Chairman,
Securities Exchange Commission [140]
What is ‘Fintech’? Fintech is a portmanteau of financial technology that describes an emerging
financial services sector in the 21st century. Originally, the term applied to technology applied
to the back-end of established consumer and trade financial institutions. Since the end of the
first decade of the 21st century, the term has expanded to include any technological
innovation in the financial sector, including innovations in financial literacy and
education, retail banking, investment and even crypto-currencies like Bitcoin.
A definition of fintech [81]
Introduction
The firms in the financial services industry perform a range of functions to facilitate financial
economic activity in modern economies. Hatzakis et al. [74, p. 633] provide an authoritative
definition of what the term financial services firms means, as follows: “primarily firms in retail
banking, commercial lending, insurance (other than health), credit cards, mortgage banking,
investment advisory, and asset management (mutual funds, hedge funds, etc.).” In 2016, financial
services and insurance accounted for 7.3% of gross domestic product (GDP) (US$ 1.4 trillion) in the
American economy [127], and peaked at 8.4% in 2011 [98]. Also in 2016 in the European economies,
financial services and insurance represented approximately 5.1% of total GDP with about 2.6 million
people employed in financial services and insurance [55].
For decades and at least until the 2008 financial crisis, the financial services industry and the firms
that comprise it have traditionally been a bastion of corporate stability and safe employment. Today
the industry is being buffeted by new forces of technological innovation, process disruption, and
business transformation, resulting in new economy playbook for the services produced and the
operational capabilities that are built.1 Financial firms lend to business and household borrowers,
provide accounts and transactions services, offer investments, debt and equity financing, and provide
risk management services such as insurance policies and foreign exchange (FX) forward contracts. To
do this in cost-effective ways, during the 1970s and 1980s the financial industry built mainframe
computing in the commercial banking, securities, and insurance sectors that led them to the most
intensive and innovative users of information technology (IT). By the 1990s, expanding expenditures
on IT was more the norm than the exception, as the industry moved into the Internet economy. An
example is the merged FleetBoston, which announced in 1999 that it would spend in excess of
US$100 million to launch Internet banking services to cover its main commercial and retail banking
services [85]. In contrast, by 2015, leading American banks, Bank of America and J.P. Morgan, filed
reports with the Securities Exchange Commission (SEC) that they would spend US$400 million+ and
US$500 million+, respectively, to defend against cyberattacks, and ensure secure accounting,
1 The IS literature offers many theory-based explanations of technology-driven transformations in the financial services
industry. For studies on strategy and market transformation, the interested reader should see, among others: Clemons et al.
[33] on the competitive disadvantage associated with market dominance; Han et al. [73] on IT ownership amid market trans-
formation; [65] on firm strategy and the Internet in commercial banking; and Clemons et al. [35] on the information-driven
transformation of strategy and society, including financial services.
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operations, data processing and other core banking functions [113]. Overall, banking industry IT
expenditures have been forecast to rise to US$480 billion in 2016 in total as a portion of the annual
projected spend on IT of US$2.7 trillion around the world [80].
What Is the Fintech Revolution?
The new developments associated with the Fintech Revolution are best understood based on recent
statistics from VentureScanner (www.venturescanner.com), which has tracked new ventures in many
different start-up areas, including fintech start-ups (as well as energy, transportation, Internet of
Things (IoT), 3D printing, healthcare, virtual reality, among others). As of the late fourth quarter
2017, VentureScanner [144] listed on the order of 1,537 companies in 64 different countries having
received US$80.4 billion in venture capital funding for their fintech innovation start-up activities. In
addition, it reports that 291 other companies in 74 countries have raised US$4.5 billion for Bitcoin-
related initiatives. Another related fintech start-up category is insurance technology, with 61 countries
and 449 companies involved, and US$19.5 billion in capital raised. The fintech start-ups are only
matched in capital formation by these other areas in global entrepreneurship: transportation
technology (642 companies, US$95 billion), energy technology (544 companies, US$US58.9 billion),
retail technology (1,151 companies, US$54.5 billion), healthcare technology (1,497 companies,
US$54.2 billion), and IoT (1,291 companies, US$45.4 billion). These numbers represent a major
expansion in start-up funding since the recession in the 2000s. They were only seen during the e-
commerce and Internet era of the late 1990s, and then only in a much more modest scale. These
capital formation activities represent one of the largest historical expansions in entrepreneurship to
date among modern economies.
The foundations of the Fintech Revolution rest on three separate pillars of innovation. First, as we
have noted above are the large amounts of capital available for technology innovation for financial
services in a highly fertile area of the global economy. To wit, The Economist [137] noted that the
“magical combination of geeks in T-shirts and venture capital that has disrupted other industries has
put financial services in its sights. From payments to wealth management, from peer-to-peer lending
to crowdfunding, a new generation of startups is taking aim at the heart of the industry—and a pot of
revenues that Goldman Sachs estimates is worth $4.7 trillion.” Second, these start-ups have
developed new technologies and designed new services for the finance industry that are different from
what the industry has been able to offer, and typically address the needs of financial services
consumers in very direct, valuable, and future-forward ways.2 Third, they have transformed business
models, financial intermediation and customer access by extending consumer access outside branches
and beyond normal banking business hours; achieved higher levels of personalization based on digital
sensing and big data analytics; substituted for traditional banks and their services in new ways;
financialized business processes that were heretofore operated in different ways (e.g., making lending
a crowd-funded, platform-based business; and hybridized services that are delivered in new and more
attractive non-banking channels [69].
Key Forces: Technology Innovation, Process Disruption, and Services Transformation
Three aspects of the Fintech Revolution that are at work in our times deserve additional comment to
set the stage for our area by area analysis in this article: technology innovation, process disruption,
and services transformation.
Technology innovation. Technology innovation is widely recognized as being the main engine behind
economic growth and industrial transformation. And if anything is true in the technology industry, it
is that the pace of technology change is ever-increasing, inexorable in its advance, and more
transformative in its effects. Brynjolfsson and McAfee [19] have argued that we now are living in the
“Second Machine Age,” which was jump-started in 1997, when Russian chess master, Gary
Kasparov, was beaten by a computer chess program, IBM’s Deep Blue, in a chess match conducted
2 These developments are described in a National Public Radio show on the “Digital Industrial Revolution” [136].
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under the standards for chess master competitions. The authors’ reference to the words digital,
exponential, and combinatorial are indicative of why everything seems to be moving so fast, one
innovation feedings on another before it, and these and other new emerging technologies coming
together to create heretofore unseen and unimplemented new capabilities in leading global industries.
Prior research has shown that technology innovation can be described in terms of three basic
technology elements: components, products and services, and infrastructures [3, 4]. Together, these
can be used to characterize the manner in which they can connected in nearly “Lego building-block
style” to create the new technology capabilities that characterize our world. One should view these
elements as being sociotechnical in nature, and not purely technical. This is because, although the
development of new technology components is mostly based on electrical and computer or software
engineering (e.g., the miniaturized cameras in mobile phones, application-specific integrated circuit
boards for ASIC Bitcoin mining hardware, or algorithms for pattern recognition in image detection),
products, services and infrastructures all are based on societal demand. Products and services are
created for consumers to buy and use them. And yet, usage and diffusion often can be dramatically
enhanced by creating digital infrastructures that bring them to the wider public via the Internet and
streaming music and video services (e.g., Spotify, GooglePlay, Last.fm and others that drove the
diminution of music album and movie sales in CD form in lieu of AAC, AIFF, MP3, MP4, AVI and
others). In the financial services arena, we have seen this over the years with digital brokerage and
online stock trading services, that are networked for trade execution in the market.
Process disruption. Although we believe that many different processes have been disrupted by
emerging fintech innovations, it is also fair to say that the entire financial services industry has been
disrupted in fundamental ways. A hopeful view of this suggests that it is time for financial institutions
to “embrace disruption” and prepare for the development of more effective organizational strategy in
financial services by leveraging the breakthrough fintech innovations of our time. A number of
technology-driven disruptions suggest how the financial service landscape will look in the Year 2020,
according to PwC [121, p. 6]. The disruptors they have identified include the following:
(1) The fintech approach will be the new model in the industry, the sharing economy will become
more fully embedded in financial services, and the mainstream of products and services will
become more fully digitalized and offered through technology platforms.
(2) Blockchain will take root beyond cybercurrency applications; public cloud services will
dominate; robotics and AI will continue to achieve critical mass in their diffusion in financial
services, resulting in more localized services; and cybersecurity will become a more critical
risk to financial services firms than fraud and money laundering.
(3) Customer intelligence is likely to emerge as the most important driver of financial firm
profitability, and regulators will become as active with fintech innovations as the firms it
oversees.
Amid the technology platform-driven disruption, Dhar and Stein [48, p. 32] asked a key question
regarding trust for fintech platform success: “[W]ill future investors trust fintech platforms to the
degree that previous generations have trusted traditional banks? [W]hat will it take for fintech
platforms to be trusted sufficiently by future generations?” Similarly, in the push of industry firms to
achieve deeper customer intelligence, what can financial services customers do to defend themselves,
and not lose trust related to the extent to which their product and service preferences are data-mined,
digitally-tracked, and technologically recovered [86]? Will “know your customer” (KYC) take on a
new offensive meaning, beyond its standard defensive meaning for anti-money laundering, and
customer-as-counterparty risk management?
Services transformation. The result of these changes and transformations are that financial services
will operate in new and different ways to those that have been observed before. During the prior and
present eras, large financial institutions have mostly been in charge of how things work, with clearing
float in bank checking transactions, delays in the transfer of funds to customers in electronic payment-
related transactions, slower processes for opening bank and brokerage accounts, and costly and
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expensive FX currency pair trades and financial remittances overseas.
And yet, what many of us have observed over the years is less-than-best outcomes associated with
innovations with respect to financial technologies before they came to be called “fintech innovations.”
A fair argument to make in light of the historical developments we have observed in the past is that
not many of them have been transformational or successful in the long run. Some that were less
successful than was hypothesized in academic research come to mind, especially computerized loan
origination systems (CLOS) [64]. Hess and Kemerer [78] sought to understand why the traditional
predictions of fundamental changes to the financial services had not proven to be true, according to
the electronic markets and hierarchies hypothesis [107]. Some of the main players included First
Boston Corporation’s Shelternet, Citicorp’s Mortgage Power Plus, American Financial Network’s
Rennie Mae, Prudential’s CLOS, and PRC’s Loan Express. On the wholesale financial markets side,
J.P. Morgan’s CapitaLink electronic bond issuance systems in the early 1990s failed to create much
impact before the Glass-Steagall shelf registration rules for debt issues by non-investment bank
financial services firms were repealed [73].
Some other well-known names in financial technology entrepreneurship had similar experiences,
including Mondex in the U.K. for cash-alternative retail transactions, and Financial Engines for
corporate subscribers’ employee pension fund portfolio construction. Another example is Optimark
for institutional trading by price-time-quantity bids for large blocks of shares, which did not survive in
spite of its high innovation of a three-dimensional order book. The same can be said for WIT Capital,
the first Internet-based equity issuance firm in the U.S., which was always close to contravening SEC
rules for inappropriate investor solicitation and inappropriate use of the Internet.
Therefore, we ask: What is likely to raise the percentage of market successes of newly-marketed and
venture capital-funded fintech innovations? Will the Fintech Revolution yield a better track record
compared to the past? We think that this will come to pass in the future. Financial services are created
and delivered through complex systems in business with processes, organizational and operational
structures, human capital and talent, and a variety of choice behaviors, subject to ethical, regulatory
and legal restrictions. Incumbents and new entrants continuously seek to improve the customers’ and
users’ experience and raise the performance of their products, services and capabilities, for example,
related to ATMs, online brokers, and index and exchange-traded funds (ETFs). The fundamental
difference today is the new abundance of data, the increasing maturity of the data infrastructures and
integrated systems that have been deployed to process it, as well as the emergence of pattern
recognition, data mining, machine learning (ML) and other digital-sensing tools in the financial
services environment that can utilize it. In addition, there is much broader awareness that financial
institutions and fintech start-ups can be mutually supportive, and not just competitors in the same
space [49]. We will argue that the preconditions are now in place to create and co-create business
value through changed cost and benefit flows, and to permit the core entrepreneurial actions of start-
up firms enable to achieve high performance in competitive markets. Fintech innovation strategies
will continue to develop, involving continual experimentation, learning, and adaptation –
experimenting with experimentation [35, 86].
Mapping the Fintech Innovation Landscape
We next present a new mapping to enable readers and researchers to understand the fintech
innovation landscape better. Our representation is based on key elements of several prior frameworks,
all of which enable us to make a clearer assessment of how technological innovation, process
innovation, and financial services transformed have been occurring over the past seven or eight years,
as the Fintech Revolution has intensified.
We begin with the observations that Chesbrough [28] made in his book, Open Innovation. He offered
a 2 x 2 innovation matrix that consists of the actions and innovations that can be achieved when both
the problem definition and the domain definition are either well-defined or not well-defined. He began
by noting that basic research typically is conducted to establish new scientific and technological
outcomes when both the problem and the domain are not well-defined. He further observed that, when
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the problem is well-defined but the domain is not, there is an opportunity for a breakthrough
innovation that can be widely used in many settings. By the same token, Chesbrough refers to a
disruptive innovation as one for which the problem is not well-defined, while the domain is. When
both the domain and the problem are well-defined, it is a sustaining innovation.
This conceptualization of technology innovations is useful in terms of the main constructs, but less
well-suited for application in the fintech innovation context. Consider the following example. In 2008,
the P2P electronic cash system proposal related to Bitcoin was presented in a technical article that
focused on the underlying digital cryptography for ensuring that digital coins could not be spent twice
[115], something that other digital cash proposals from the 1990s and earlier 2000s were not able to
do. The widespread belief of computer scientists, banking technologists, and technology entrepreneurs
has been that the idea of a blockchain – in laymen’s terms at least – is perhaps viewed as the defining
“disruptive, breakthrough technological innovation” of the Fintech Revolution overall [111]. And yet
these words are used in Chesbrough’s innovation matrix for settings in which the domain of
application is not well-defined (breakthrough innovation) or the problem that is targeted for solution
is not well-defined (disruptive innovation). So although the innovation matrix clearly identifies
blockchain as being disruptive (which is appropriate, in our view), the domain of its application in the
first publication that demonstrates its use is to cashless digital payments, and no claims are made with
respect to its generality. Those would only come later.
For our own evaluative approach for identifying the different kinds of fintech innovations related to
the kinds of impacts and effects they have, we further considered the more recent contribution by
Pisano [119], who presented an enhanced 2 x 2 technological innovation map. Based on
Chesbrough’s work, Pisano’s approach is what we believe is needed to effectively characterize the
innovations of the Fintech Revolution. Pisano’s first dimension gauges whether a new innovation
permits the leveraging of existing competences versus requires new technical competences in the
organization. The second dimension, in contrast, assesses whether an existing business model can be
leveraged or a new business model must be created.
The author refers to routine innovation as that which permits existing technical competences to be
utilized within an existing business model. A radical innovation, however, permits the firm to still use
its existing business model, while it must create new technical competences; and a disruptive
innovation is one for which the firm is able to continue to use its existing technical competences, but
must shift gears to create a new business model. As we will show, fintech innovations seem to fit into
both categories of innovation – radical and disruptive, yet many seem more well-suited to being
recognized as what Pisano refers to as architectural innovations; in other words, fintech innovations
often involve new technical competences and a new business model for effective value appropriation.
With these observations in mind, we further consider two kinds of effects that occur with the
introduction of fintech innovations. One is based on the manner in which the customer’s experience
(first dimension) is affected either by improved functionality or new products, services and
functionality, and the second is based on whether there are complementary or disruptive effects from
the new technology that are felt in the marketplace and in the context of firm-to-firm competition
(second dimension). An older business model typically requires modest enhancement to accommodate
updated processes, while a new business model may be required to accommodate the transformation
of the related business process into business value. Through the application of the customer
experience dimension, we are able to assess whether existing functionality is sufficient or new
functionality is needed to support new products and new services. See Figure 1.
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Figure 1. Fintech Innovation Landscape: The Market-Competition-Customer Experience View
Contrasts Between
the Effects of
Fintech Innovations
That Are Observed
CUSTOMER EXPERIENCE WITH
THE NEW FINANCIAL SERVICES
Enhancing Experience
with New Products, New
Services, and New
Functionality
Supplementing Experience
with Improvements
in Existing Functionality
MARKETS AND
COMPETITION
Disruptive Effects from:
• New business models
• New market mechanisms
• Shared tech infrastructures
• Disintermediation of banks
• Segment-of-one marketing
• Cross-border innovations
Blockchain-based markets,
initial coin offerings (ICOs),
cryptocurrencies, global
remittances, FX applications,
high-frequency trading
(HFT), crowdfunding, P2P
lending, online-brokerage,
cross-border payments, open
banking
Open banking/APIs, faster
payment settlement,
increased settlement efficiency
with blockchain, smart contracts
in trade services and lending,
cross-border custody services,
e-services piggybacking, higher
functionality payment cards
Complementary Effects from:
• Enhanced business models
• Extended access by customers
• Hybridized services by firms
• Shared tech infrastructures
• Open APIs
Social trading, digital wallets,
robo-advisory services,
branchless banking services,
big data-supported customer
intimacy, personal financial
management (PFM), financial
research exchanges
Investment communities,
mobile payments, blockchain-
based general ledger
functionality, risk management
technology, regtech solutions,
account deposits without
branches, fintech trade support
In the following sections of this article, we will apply this new framework to the following fintech
innovation areas: financial services operations, payment services, deposit and lending services, and
financial market and investment-related services.
How the Fintech Revolution Is Changing Financial Services Operations
OPERATIONS ARE A KEY FEATURE OF FINANCIAL SERVICES including systems design, performance
analysis and productivity, forecasting, inventory and cash management, waiting line analysis for
capacity planning, personnel scheduling, operational risk management, and pricing and revenue
management Hatzakis et al. [74] Key components of financial services operations include high
volumes and significant customer heterogeneity, repeated services interactions, and use of technology
in the service encounter. The new fintech approaches are changing all of these things in dramatic
ways: by creating a new basis for harmonizing investments across business partners and competitors
too; through the new availability of products and services that have a different operational basis, with
diminished human involvement on the purely transactionsal aspects, supported by machine
intelligence where that is appropriate, and human involvement when that can add value; via the
reduction of cross-border breaks in transaction servicing; and with new approaches to the
management of customer and operational risk through new ML and artificial intelligence (AI)
applications.3
Relatively recently, the cost to launch a tech start-up began dropping because of open source software
(e.g., the Tensorflow library to support machine intelligence, www.tensorflow.org), and easily
scalable infrastructure (via Amazon Web Services, Google Cloud, etc.). This has allowed new
entrants into the market to create niche products that target very specific groups of customers based
3 Financial services operations, along with the business value and profitability that they can create, have been studied by
researchers and practitioners since the 1980s [109, 132]. The respective themes include: the economics of electronic banking
strategy and shared ATM networks [30, 87]; process variation as a basis for service quality and performance [58]; retail
banking strategy when electronic distribution technology costs influence bank competition and performance [23]; the eco-
nomic effects of technological progress on banking [15]; system design and process performance in trade finance operations
[43]; customer intimacy strategies with IT for small bank-offered financial services [134]; nowcasting machine-based fore-
casts for GDP [60]; new management science approaches to credit card risk scoring [27]; and support vector machines [14]
and other ML algorithms [94] for consumer credit scoring.
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on their characteristics. The problem for incumbents is that the highly-targeted companies are
attacking each product they have on offer, and that this leads to the unbundling of financial services
[31, 100]. These new fintech business models that have forced businesses to become more customer-
centric by offering customers just what they want through more effective bundling [71], and also have
changed how researchers think about operations within financial services. We focus on four core
aspects of financial services in which large operational changes are driving significant improvements:
transitioning to branchless banking, real-time transaction and credit monitoring, credit scoring and
approval, and transformations in customer acquisition and retention.
Transitioning to Branchless Banking
Traditionally, physical bank branches have served as the primary point of contact for facilitating retail
banking and customer transactions. As technology improves, customers are switching from in-person
to digital transactions through a complementary effect delivered by the enhanced access to digital
banking services, and an enhanced experience of new digital access products, services, and function-
ality. For example, many banks now allow for physical checks to be deposited through a mobile
application on a smart phone with a camera. The transition has also opened up the market for non-
bank firms to offer financial services. Millenials are particularly susceptible to new entrants as 84%
now would consider obtaining their banking services from a major tech company such as Google or
Apple [97].
Given the relatively recent innovations, very little is known about how mobile banking impacts
consumer behavior. Campbell and Frei [24] have reported that the adoption of online banking can
result in substitution by alternative service channels, changes that require services to be provided by
them, as well as an overall increase in the number of transactions that the alternative channels have to
process. These three levers combine to actually increase the short-term costs of serving a customer,
but online banking services increase the likelihood of long-term customer retention. Liu et al. [104]
have shown that consumer use of mobile apps has led to increased demand for other digital services,
and complementary effects between mobile and tablet use. They also report that mobile phone and
tablet users are less likely to pay credit card fees. The combined effect of the changes in customer
behavior is a net benefit to the bank of about US$0.07 per month for the average customer. The extent
to which these factors impact bank profitability and retention appear to be moderated by the
customers’ efficiency of use of the digital offerings Xue et al. [152]. Trust also plays an important
moderating role in facilitating the transition from offline to online transactions Balasubramanian et al.
[10].
Lower transaction volumes at physical branches and the reduced costs of facilitating transactions
digitally create a strong incentive for banks to reduce their branch footprints. This is because bank
branch systems are known to facilitate relationship-based banking better than transaction-based
banking, which has paved the way for technology substitution [65]. It has also been reported that
banks with extensive branch systems may underperform others that are more committed to operating
in multiple channels [47]. Combined with bank consolidation in the wake of the 2008 financial crisis
in the U.S., the changing economics of physical branch networks led to significant declines in the
number of branches throughout the country [2], and this trend seems to be continuing unabated as the
Fintech Revolution has advanced.
Researchers are now beginning to get a better picture of the impact of branch network changes on
customer banking behavior as well. Abhishek et al. [1] show that branch openings increase digital and
non-digital transactions, while branch closures generally lead to increases in cost-effective transitions
of costs to digital channels. Interestingly, there is significant customer heterogeneity in response to
changes in branch networks though. Customers who are heavy branch users, and who may reasonably
be expected to be the most impacted, are less impacted by changes to the branch network.
In addition to the transition to more digital transactions, banks are continually experimenting with
new models to reduce the cost of their physical bank branches. For example, in the past USAA
collaborated with UPS to allow check deposits at any UPS store, thus, in principle, reaching
9
customers who did not have a smart phone. Although USAA canceled this program due to operational
problems, this situation demonstrates that business models targeted at bringing cash and physical
checks into the digital banking system are needed. In fact, more research is worthwhile to undertake
so we can better understand how relatively underserved customers can be brought into the formal
financial system in a cost-effective manner.
Real-Time Transaction and Credit Monitoring
From the customer perspective, monitoring transactions and credit has historically been stressful and
subject to significant time delays. Customers generally had to rely on fraud detection algorithms and
notifications from their bank. Through their history (e.g., Fawcett and Provost [56] and Kou et al.
[96]) and substantial effort into improving the models, the high costs associated with missing fraudu-
lent transactions continue to create the impetus for significant investments in developing sophisticated
algorithms for detection. The advent of mobile banking means that ML models being deployed by
financial services firms today are able to leverage real-time location data to improve detection of
fraudulent transactions.
Despite improvements in individual firms’ algorithms, consumers seeking to track their transactions
still must monitor multiple different websites to remain alert to possible fraudulent activity. Business-
es have responded by providing their customers with free access to a consolidated view of their trans-
actions, via services like Mint.com (www.mint.com) or offered within the retail banks themselves,
and credit monitoring via www.creditkarma.com. In both cases, the business models deployed rely
upon creating a holistic view of an individual’s personal finances, and selling targeted ad space to
potential creditors. Personal financial management (PFM) services are a major element in contempo-
rary fintech-enabled services innovation, and a way to create a greater degree of customer informed-
ness in the marketplace. Mint and CreditKarma enhance existing bank and credit monitoring business
models by introducing easily-accessible and understandable products.
The creation of a one-stop-shop for transaction monitoring and the combination of traditional credit
and transaction data in one location makes for interesting opportunities in detecting fraudulent behav-
ior. Location-based rules for credit monitoring have prevented a card from being used in one location
and then again 15 minutes later over 100 miles away. Simple rules can allow for monitoring cards
jointly for fraudulent transactions based on when another card was used. Careful profiling of customer
purchase patterns on one card can also help develop purchase patterns for infrequently used or poten-
tially new cards. It is possible that fraudulent credit applications can also be denied or put into a pro-
cess for further customer verification by determining the types of credit a customer may apply for.
The consolidation of data also creates a single point of failure where hackers can gain access to all of
a customer’s financial data. The rapid uptake of monitoring services shows that consumers currently
value the consolidation more than the unknown future cost of a breach, despite large security failures
at, among others, Equifax. Furthermore, little is known about how they impact consumer behavior.
On the one hand, CreditKarma reduces the cost of monitoring credit and therefore may lead to more
responsible credit behavior. On the other hand, the cost of applying for new credit is also reduced
possibly resulting in an increase in revolving-credit accounts and a generally degradation of credit.
This is a difficult empirical question that requires a careful consideration of selection into CreditKar-
ma, selection into applying for new credit, and access to a rich dataset.
Credit Scoring and Approval
Credit scoring is the process a company uses to decide whether to grant or deny credit to a potential
borrower.4 Consolidated data has also led to changes in credit scoring itself. In developing economies,
alternative credit scores are being built based on mobile phone usage (such as the call detail record
and billing data). Alternatives are also appearing that combine mobile phone, browser, social network,
and traditional transaction and credit data to create credit scores, such as Lenddo in Singapore
4 For an early review of models, see Capon [25].
10
(www.lenddo.com).
In developed economies, data mining has the potential to revolutionize mortgage approvals. Despite
an estimated US$1.8 trillion in mortgage originations in 2017 [58], much of the process of applying
for and being approving for a mortgage is still manual. Several companies are attempting to disrupt
this market by moving manual processes online in an attempt to reduce both financial and time costs
of obtaining a loan. SoFi (www.sofi.com) moves much of the approval process online and
incorporates an applicant’s education in their determination of whether to provide a loan. Rocket
Mortgage (www.rocketmortgage.com) from Quicken Loans has regional mortgage application
reviewers that are familiar with local regulations and standardize the process. Sindeo [127]
(www.sindeo.com) claims to be able to close in 15 days compared to an average of 46 days.
Businesses are setting up to simplify the data collection and verification processes more broadly than
just for mortgages. For example, Plaid (www.plaid.com) has created a backend platform and
Application programming interfaces (API) that allow users to connect their bank accounts seemlessly
within another app. Plaid’s platform can then verify income based on historical deposits, streamline
automated clearing house (ACH) authentication, and verify the identity of a customer to ensure KYC
protocols are adhered to. Two processes that are being streamlined involve getting data directly from
a customer’s bank and then to use it to validate the person’s income. Companies such as Waddle
(www.waddle.com.au) and Wave (www.waveapps.com) do something similar for business loans
based on invoice data, while Mint, Earnest (www.earnest.com), and CreditKarma use transaction
and/or credit data to recommend financial products to individuals with some level of confidence that
the application will be approved.
Loftium (www.loftium.com) is an example of another emerging business model targeted at making
mortgages easier. Loftium provides up to US$50,000 toward a down payment in exchange for a
fraction of 12-36 months of shared AirBnB income in the new home. The requirement is typically to
have your room listed for all but eight days of the year with Loftium, with approximately 65%
occupancy expected.
Advances in the credit scoring market are broadly complementary due to the relatively behind-the-
scenes nature of new credit scoring and approval models. With the exception of Loftium, much of the
innovations in this area supplement the customer experience. Researchers need to understand how
simplifying credit scoring and approval impacts customers. Primarily, search and transaction costs are
dropping, and this has been leading to a more efficient market for PFM services. Small business can
also more easily smoothe their cashflows. Both of these should lead to lower defaults for individuals
and businesses. However, current research shows that online lending made available by Lending Club
has resulted in an 8% increase in personal bankruptcy filings [145]. More research is needed to
understand the short- and long-term consequences of ease of access to credit and how this interacts
with customer heterogeneity.
Transformations in Customer Acquisition and Retention
One of the major promises of the Fintech Revolution has been to create the ability to reliably reduce
customer acquisition costs. For example, companies such as CreditKarma and Mint were built to mine
data in order to build highly-targeted recommendations to consumers in an attempt to reduce
acquisition costs.5 They partner with credit card companies, banks offering traditional and/or online-
only checking and savings accounts, a large range of investment firms, and auto and life insurance
providers. Major companies leverage the platforms to attain new customers at a relatively low cost
compared to traditional physical mailings. In doing so, they improve customer intimacy through big
data as the result of new, hybridized products services.
However, the same forces that have led to a reduction in customer acquisition costs have also reduced
the cost of a customer switching to a competitor. For example, a customer on Mint is constantly
5 There is an interesting similarity here to how the credit card lender, Capital One got its own start, with the slogan “Don’t
call us, we’ll call you,” based on their own effort to do data mining on potential customers to figure out what card-related
interest rate would match the levels of risk of default on loans that were involved [36].
11
bombarded with ads from competitors, and is even shown ads for a different card within the same
firm, creating intra-firm competitive forces. This makes customer retention a more difficult problem,
and firms now must become much more coordinated and customer-centric. Operational costs such as
increased call-center volume or the creation of intelligent AI chat bots for customer service are
needed to retain customers.
The need to be customer-centric is especially important among fintech firms, which usually
implement business applications that require high levels of self-service by their customers. Scherer et
al. [126] have reported that the shift from personalized-service to self-service initially leads to
decreases in customer defection, but later exhibits an inverted U-shape, such that defections begin to
increase again as the extent of customer self-service continues to climb. They also found that the
effect diminishes over time, and recommend that the beginning of a financial services relationship
should be more personal-service oriented, followed by a transition to self-service later. This is
inconsistent with the way many fintech firms are set up and must be further explored in research.
One way that firms can distinguish themselves is to make traveling and purchasing abroad easier. For
example, 45% of Gen Y professionals in Australia surveryed by KPMG [97] responded that they find
easy access and management of funds while traveling abroad to be extremely important. Another
option is to take advantage of network effects and socialization like those seen in the P2P money
transfer platform Venmo (venmo.com). Nevertheless, all firms will need to carefully monitor their
key metrics in this area, as the acquisition and retention landscape will become more competitive as
big tech companies such as Apple and Google wade into the personal finance space.
Toward the Cashless Society: The Fintech Transformation of Payment Services
PAYMENT SERVICES HAVE ALWAYS BEEN AT THE FOREFRONT OF TECHNOLOGICAL CHANGE AND OF
innovative approaches that have been used to transform the nature of payment processes for
consumers. For example, in the 1970s and 1980s, the cooperative Society for Worldwide Interbank
Financial Telecommunication (then, and now called SWIFT) was a leader in implementing a global
telecom network that carried different kinds of short pre-formatted messages in support of
international banking communications. Later, stand-alone bank-connected ATMs were deployed,
followed by shared networks of multiple banks’ ATMs [90], and later network universality
capabilities that brought CIRRUS and PLUS together in the U.S. [92], and then around the world. So,
when it comes to considering the application of financial technology solutions in the payments arena,
it is not a matter of a handful of isolated events, but instead a developing stream of innovations that
eventually led to the transformation of services access, higher functionality, and ultimately new
revenue strategies and sources for commercial banks that deployed credit card services [62].
McKinsey & Co. [12] have commented that the payments sector is currently undergoing digital
disruptions via the entry of non-bank digital competitors, the modernization of payments
infrastructures around the world, the move to establish new efficiencies in cross-border payments, and
the broader digitalization of domestic banking transactions.6
The Forces at Work for Fintech Innovation to Address Faster Settlement of Payments
One of the leading targets of the Fintech Revolution has been to speed up the settlement of domestic
payments. This has been recognized as an increasingly important public policy issue for banks and the
governments that regulate them. The definition of faster payments is that they involve “[d]domestic,
inter-bank …, purely electronic payment systems in which irrevocable funds are transferred from one
6 Research on issues associated with payments in the global economy have been covered by: Clemons et al. [34] on how
Mondex re-engineering money with non-debit, non-credit plastic cards in the 1990s: Au and Kauffman [8] on how electronic
billing infrastructures developed; and Au and Kauffman [9] on the economics of mobile payments; Staykova and Damsgaard
[131] and Kazan et al. [93] on market competition for mobile payments platform; and [76] and Liu et al. [105] on coopera-
tion, competition and regulation related to mobile payments market changes and ecosystem changes. There have been many
others, though these are representative.
12
bank account to another and where confirmation back to the originator and receiver of the payment is
available in one minute or less” [57, p. 12]. Faster payments (also referred to as faster settlement)
typically means that funds are transferred from one financial institution to another, such that the payee
benefits from the receipt of irrevocable funds that can be immediately deployed for any purpose [11].
Different countries typically view “faster” in relevant terms (whether within minutes or hours during
the same day), since some already have relatively fast mechanisms to make funds available to their
customers, while others have achieved some form of “faster,” though there are no global standards
recognized by any payments standards or operational process regulators.
There are a variety of interpretations as to why this is so important. A simple explanation is that the
world has changed so much in the past fifty years, with faster computers, more capable networks,
competition, technological innovation, and social demands that change is inevitable amid the forces.
For example, an industry-leading payment services provider has commented on why e-commerce has
been so central to recent developments: “[m]any payment systems around the globe are undergoing
fundamental changes to reflect the new realities of digital commerce, especially when it comes to the
speed in which payment requests are processed. The electronic payment systems that were designed
in previous decades can no longer meet all of the expectations of a society where devices with
enormous computing power are literally in the hands of most adults and instantaneous response has
become the norm, even when it isn’t necessarily required for business reasons” [57, p. 9].
Yet, the norm in most of the world is that payment settlement is governed by substantive and complex
considerations about payment settlement mechanism design, incentive compatibility, and liquidity
management from the viewpoints of financial institutions and their regulators [70].7 Some countries
have sought to move aggressively to embrace faster payment, including Australia, Singapore, Sweden
and the U.K., where the regulatory and political situations are conducive to such change. In this
context, it is important to distinguish between delayed net settlement (DNS) and real-time gross
payment (RTGS) systems.8 DNS involves the accumulation of payments, so that one can be netted
against another, before irrevocable funds are made available, in a way that minimizes the need at any
time for additional liquidity to make settlement possible, but this also makes the delay of funds
availability likely at times. In contrast, the RTGS approach supports the individual processing and
settlement of payment transactions, resulting in the irrevocable availability of funds on an immediate
basis. The complications that arise are due to the need for central bank involvement to inject intraday
liquidity, so that the effects of payment imbalances are neutralized in the process of continuous, real-
time settlement, albeit with higher operational and monitoring costs [13, 125]. Central banks typically
have provided liquidity for a cost of funds-plus fee to make the payment settlement process smooth,
or they may require participating banks to back any liquidity loans with collateral, so the intraday
credit risk that the central bank assumes is strictly limited [5].
RTGS systems have been implemented since the 1990s, especially for the exchange of large payments
between banks [6], but their benefits have not been extended to retail banking customers. The currrent
work in the fintech arena is to make faster settlement of payments work for small-value retail
payments. The number of such transactions is far larger, though the total dollars that are transacted are
typically an order or magnitude or two less in value. Similar to large-value payments, it is also
possible sometimes for there to be payment transaction value imbalances that make it more difficult
for a settlement intermediary to operate without prior agreement with the central bank on how
liquidity injections will be handled. Recent research has aimed to test the effectiveness of a hybrid
7 Some of the key issues here involve the cohesiveness and performance of the faster payment settlement system as a busi-
ness network. In other theoretical analysis work, a variety of issues are highlighted that make procurement platforms, loyalty
networks, and group-buying systems all have difficult issues with network viability [35], including: the likelihood of long-
term network sustainability; the extent of process standards that support a network; the extent of firm and customer in-
formedness about network and system performance; demand-driven complementary network value; sustainable network
value; performance monitoring and fair value sharing [88].
8 For several reviews of payment, clearing and settlement systems around the world by the Committee on Payment and
Settlement Systems (CPSS) economists of the Bank for International Settlements (BIS), the interested reader should see the
series of white papers that the BIS published in the 2010s [40, 41, 42].
13
near real-time gross settlement system, first proposed by Willison [148] in simulation research
conducted at the Bank of England in the early 2000s. This approach makes it possible for the
intermediary to obtain prioritization information from bank participants on payments that can be
settled via the RTGS approach, while holding back other payments for handling via the DNS
approach [70]. The benefit of a hybridized approach [69] is that it will permit faster payment
settlement to be continued after the normal operating hours of the central bank in a country, much as
cross-networks permitted after hours equity trade matches in the financial markets.
Major real-world experiments have been occurring with faster settlement for smaller-value payments
since the mid-2000s. They include: the U.K.’s implementation of the Faster Payments Service
(www.fasterpay-ments.org.uk), the Swish mobile payments system (www.getswish.se) implemented
in 2012 in Sweden [16, 118], and the 2012 rollout of the Giro 3 (G3) Clear2Pay and MEPS+ systems
in Singapore [135]. These real-world investments in faster payments infrastructure emphasize the
variety of research issues that are in play: network sustainability and value; an effective process for
joint public-private shared payment infrastructure investments in an economy; whether the various
participants have sufficient compatible incentives to bear their fair share of the investment required to
achieve network and settlement mechanism viability; and how well they do with respect to the
multiple goals of consumer access and handling the costs of liquidity. In addition, Australia’s New
Payments Platform (NPP) is currently in the process of implementation, and has one of the most
comprehensive approaches to the realization of faster payment settlement. Both Australia’s NPP and
Singapore’s G3 are viewed as having appropriate sharing of responsibility between private
commerical banks and the central bank regulators for encouraging but not mandating participation.9
The fintech examples are representative of the following categories in our fintech innovation
mapping: disruption from new business models and market mechanisms, and shared technology
infrastructures crossed with effects for supplementing customer experience. This is occurring through
improvements to the existing functionality of payment settlement, and the changed expectations
consumers have about how banks handle electronic payments in small-value retail transaction-
making.
Blockchain, Distributed Ledger Technology, and New Cybercurrencies
One of the characteristic technological innovations for financial services is the blockchain, which
Underwood [143, p. 15] has argued enables prior applications to be improved, and new applications to
be deployed that were heretofore uneconomical or impractical. She also commented that this “distrib-
uted ledger technology is expected to revolutionize industry and commerce and drive economic
change on a global scale because it is immutable, transparent, and redefines trust, enabling secure,
fast, trustworthy, and transparent solutions that can be public or private. It could empower people in
developing countries with recognized identity, asset ownership, and financial inclusion; and it could
avert a repeat of the 2008 financial crisis, support effective healthcare programs, improve supply
chains and, perhaps, clean up unethical behavior in high-value businesses such as diamond trading.”
The central technical innovation associated with blockchain is digital ledger technology (DLT), which
is definted as the use of decentralized digital trust verification through encrypted digital signatures.
An important positive assessment of blockchain technology was made a couple years ago in The
Economist [138] that one should view the technology as a “trust machine.” The article also
encourages the reader to distinguish among three key things that need to be more fully understood:
blockchain in the form of the Bitcoin cybercurrency; the construction and details of the blockchain
9 In an interview that the SWIFT Institute [133] conducted with NPP’s CEO, Chris Hamilton in 2015, he pointed out the
contrast between the low-value payment focus of NPP and its approach to managing liquidity, in comparison to the hybrid
settlement system. “Each participant would keep a pool of funds at the central bank segregated for the NPP. The liquidity
inside each pool would be managed by a set of highly automated and effective tools that monitor the transactions of each
participant in their respective pool and allow draw-down and top-up on parameters set by the participant. Each individual
payment is settled through the pools in real time, creating an account-netting effect in each pool. The netting efficiency
would essentially depend on the size of participants, relative to the nature of their transaction flows.”
14
that makes it possible to use it to represent and record value in transactional exchanges;10 and the
more general idea behind the technology innovation that it offers for more widespread use.
The first application for the cybercurrency, Bitcoin, in late 2017, spiked to values of nearly
US$20,000. Yet due to the degree of its volatility over the prior five years [120] and recent meteoric
price rise, many observers have questioned the rationality behind its valuation. For example, Nobel
Prize-winning economist, Robert Shiller, has indicated that Bitcoin may be subject to widespread
misunderstanding because its applications are out of synch with its current valuation [53]. In addition,
“the story” that underlies Bitcoin’s market acceptance is similar to other stories that have been
associated with market bubbles. They include the Dow-Jones Industrial Average run-up from October
1927 to August 1929 before the crash that began the Great Depression in the U.S., the 3,000% rise in
the value of Qualcomm stock from February 1998 to December 1999, and the rapid rise in silver
prices from March 1978 to January 1980 [51]. Another Nobel Prize economist, Joseph Stiglitz, stated
that Bitcoin should be outlawed because it is likely to repeat the pattern of the U.S. stock market crash
[53], while Jamie Dimon, CEO of J.P. Morgan Chase, has referred to Bitcoin valuation as a fraud and
a Ponzi scheme [130].
And yet, there have been other interesting developments with cryptocurrencies that are both worthy of
study so we can do better to theorize about what seems to be happening, and interesting to examine
with empirical research designs, because large-scale datasets have become widely available. One such
example is an article on Bitcoin research that goes beyond the earlier empirical work of Polasik et al.
[120]. Mai et al. [106] explore the extent to which social media chatter influences Bitcoin price vola-
tility, and whether longer blog posts or brief tweets have differential impacts. Using data mining and
explanatory econometrics methods, they report that the silent minority of social media participants
who are fewer and who post more rarely seem to wield greater influence on Bitcoin price changes
than do the vocal majority with more voices and more frequent posts. They further found that brief
Tweets of 128 characters did not create as much influence on Bitcoin value changes as did more
thoughtful, in-depth blog posts. Another recent study was motivated by the catastrophic failure of the
Mt. Gox Bitcoin Exchange platform in Japan in 2014. Geng and Kauffman [63] used cross-sectional
data on Bitcoin transaction penetration in 217 countries around the world to estimate a base geospatial
model, as well as pre-security event and post-security event transaction penetration performance.
They reported for the time period of their study that Bitcoin’s global diffusion was jointly influenced
by economic, technological and policy issues, while its penetration was held back from penetrating
more widely by cybersecurity and Bitcoin exchange issues. This, the authors argued, is of greater
general interest, since fintech cybersecurity issues and questions about platform viability over time are
likely to affect many such fintech innovations.
In spite of these concerns, the more general applicability of the technology has reached extraordinari-
ly high interest among technologists, entrepreneurs and investors alike The Economist [138].11 There
is nothing to hold back changing the manner in which blockchains work, for example, by reducing
their security levels, shifting them from having public and transparent contents to making them avail-
able for private viewing and interaction only, and thereby reducing the expense for the digital cryp-
tography hash key. Some of the blockchain applications beyond Bitcoin that we have been tracking
include initial coin offerings (ICOs) involving Bitcoin and Ethereum blockchain apps, in which inves-
tors are able to acquire ownership of cybercurrency tokens in a start-up venture, enabling the entre-
preneurs to bypass the typical reporting, due diligence and regulations associated with initial public
offerings (IPOs) of stock for equity ownership. Other financial applications include the representation
10 For details on the technical underpinnings of blockchain technology, the reader should see Mueller-Eberstein [114].
11 Blockchain has also garnered the interest of central bankers. For example, He [75, p. 3] proposed the use of a central bank
digital currency (CBDC), defined as “a digital form of central bank money that can be exchanged in a decentralized man-
ner. In other words, it can be transferred or exchanged peer-to-peer, directly from payer to payee without the need for an
intermediary. … Such a CBDC would be exchanged at par with the central bank’s other liabilities (its cash and reserves) –
either through banks or directly at the central bank.” The ramifications of such a choice on the part of central banks – to
issue their own digital currencies – is fraught with technological and public policy issues that parallel the ones that faced in
other complex and sensitive domains, such as cybersecurity, the control of fake news, the patenting of digital innovations,
and the control of sharing economy innovation [35].
15
in smart contracts for lending, trade finance, online identity management for transactions, pollution
credit trading, as well as non-financial applications such as time-stamping, the tokenization of digital
assets, the sharing of secure medical and legal records, the tracking of art works and diamonds,
uniquely identifying cultural and regional products in international trade, and more.12
Relative to the fintech innovation mapping that we have proposed, blockchain and DLT are widely
recognized as having disruptive effects, by supporting new business models and transforming market
mechanism services. They also have an impact on commercial banks and even central banks, due to
their capacity to support financial transaction-making disintermediation. They also support an im-
proved experience for consumers, especially due to the new services that they are making possible,
while improving the existing functionality of general ledger accounting, by tying it to digital cryptog-
raphy.
New Directions in Cross-Border Payments, Global Remittances, and Foreign Exchange
One of the “high frontiers” of fintech application is to diminish the high transaction costs associated
with conducting business (e.g., trade contracts, FX trades, paycheck remittances, etc.) across national
borders. Cross-border transactions of all sorts tend to have higher standard costs to set them up, track
their progress and monitor the attendant risks, and bring them to a successful conclusion. And yet, all
international trade, by definition, occurs on a cross-border basis, and it acts as one of the main engines
for global and domestic economic growth. The first interest in cross-border fintech innovations area
focuses on their impacts on payments, although there are applications for cross-border risk
management, smart contracts, structured finance deals, and global securities custodian applications.
We briefly discuss cross-border payments, global remittances, and FX market applications, with our
fintech innovation mapping in mind.
Cross-border payments. The International Monetary Fund (IMF) is among the most incisive observers
and analysts who have sought to understand the tranformation of cross-border payment services with
fintech innovations. In a speech by Dong He [75], Deputy Director of the Money and Capital Markets
Department, he indicated that that the new technological approaches associated with fintech
innovation are likely to affect both the nature of the services that are offered, as well as the market
structure for how the service providers are organized. He also argued that a payment is just a
payment, regardless of whether it crosses a border, much like a package is just a package whenever
FedEx or global next-day shipping firms contract to deliver it.
To date, many observers have suggested that a panacea for reducing the costs and streamlining cross-
border can be found in blockchain-based cryptocurrencies, such as Bitcoin and Ethereum. The staff
economists at the IMF have been investigating whether DLT-based solutions will work well enough,
but they concluded that their price volatility is a concern, as is the lack of trust in decentralized DLT
operations, which may affect the value of the cryptocurrency, and the lack of cryptocurrency
interoperability is likely to be detrimental to value, and will lead to undesirable network effects.
Global remittances. The World Bank [142] estimated that the total market size for global remittances
was about US$596 billion in 2017. The business area associated with global remittances is widely
recognized as the domain of “finance for the poor,” even though regulations have been increasing
costs on the margin for sending money overseas to relatives [139]. So the large size of the market is a
strong wake-up call for social entrepreneurs who wish to support the United Nations’ Sustainable
Development Goals, especially the first one: “No Poverty.” Fintech innovations for remittances
provide much lower cost and more accessible services for moving money from one country to
another, for family maintenance, repayment of family and community loans, sharing of expenses for
international travel and education, and many other purposes. And they are creating enormous
disruption for the entrenched incumbents of the global remittance industry, Western Union and
12 These kinds of innovations reflect Chesbrough’s [28] argument about breakthrough innovations for which the general
problem is identified (digital cryptography, in this instance), but the domain will only become known over time as entrepre-
neurs develop new ideas for their application.
16
Moneygram. Meanwhile, the average cost for transferring the benchmark remittance of US$200, has
fallen dramatically over the years, involving the average percentage of the transaction amount charged
by an average of banks and money transfer operators (MTOs), or specialty remittance services firms
[141]. See Figure 2. The general business model approaches of the main start-ups in this fintech area
are to create a different basis for competitive advantage, by disintermediating the highly-expensive
and slower turnaround bank and non-bank players, while transforming the customers’ service
experience with much faster and cheaper remittance deliveries.
Figure 2. Average Cost for Sending a US$200 Remittance, 2009-2017
Cost as a percent of total remittance value
Quarter and Year
Source: World Bank [141]
We observe from the linear global average for all remittance service providers that that the percentage
prices for the value of a transaction fell at roughly the same rate, but are higher for the MTOs. Their
prices seem to have leveled off at 8.16%, while the average overall was 7.21%, both in Q3 2017.
These represent large price reductions in comparison to earlier, when MTOs charged about 10.6% in
Q1 2009 on a US$200 remittance and the overall average was 9.61%.
There are ample opportunities for research in the remittance area, in order to gauge the effects that the
fintech sector firms are having over time as their entry into the market creates a greater share of
transactions. It will be interesting to empirically test how the costs of intermediated remittance
services change for country origins and destinations, and the extent to which the G8, G20 and
developing countries experience somewhat different, though changing costs. In addition, with the
many new fintech start-ups, there also will be new contexts in which to develop theoretical insights on
the basic of very large fintech-based remittance datasets, with the idea that some of the naturally-
experimental events that country-pairs are experiencing (e.g., the entry of new fintech firms, the
changes in percentage rates over time, and the extent to which causal research designs can be
leveraged). Empirical cases involving longitudinal tracking of firm performance, changes, and market
adaptations are also possible, since many firms track the extent to which the start-ups succeed with
venture capital and other forms of fund-raising. These offer IS researchers new contexts for studying
fintech organizational performance that will go beyond what we see with the emphasis to date on P2P
lending and crowdsourcing firms.
FX applications. Although there are many FX applications that individuals need, the bulk of the
activities in the FX area involve business-to-business (B2B) applications. The marketplace for
correspondent banking services is recognized to be global and widespread, but also relatively
inefficient due to the difficulty associated with generating profitability in markets that cannot easily
sustain revenue-producing correspondent banking relationships. This offers opportunities for new
non-bank FX fintech start-ups to enter the market and create disintermediating P2P FX transaction
platforms. These can be nearly instantaneous in their production of FX trades between counterparties,
so long as there is a willingness for the paying counterparty to have some form of escrow or external
17
guarantees.13 American Express recently coined the term conditional payments, for use specifically in
situations where FX cross-border payments need to be carefully scrutinized to avoid counterparty
fraud and default [39]. Another interesting direction is the combination of enterprise resource
planning (ERP) capabilities with big data for a firm’s payables and receivables that are denominated
in FX, that enables dynamic hedging strategies to be undertaken, so that it is possible for a firm to
strictly limit its FX exposures by adopting currency pair by currency pair hedging strategies.14
Fintech innovations for cross-border payments, global remittances, and FX applications also can be
viewed through the analytical lens of our innovation mapping approach. Most observers will agree
that cross-border payments are due for major process, pricing, and functionality changes in the
coming years, due to the force of fintech entrepreneurs, who are focused on developing new products
and services, new experiences for consumers, and new ways to create value in financial services. So
we see a blend of disruptive and complementary effects in markets and firm-to-firm competition,
especially due to the cross-border business services transformations that are already occurring.
Consumers for these services will also experience the gamut of beneficial effects: from new products,
new services, and new functionality that will lead to more customer-centric services. Combined with
big data approaches, we expect that consumers will enjoy a new level of intimacy in their experience
with payment services.
Fintech Approaches to Deposit Services, P2P Lending, and Charitable Giving
THE AREAS OF LENDING AND DEPOSIT SERVICES ARE CORE TO COMMERCIAL BANKING AT THE RETAIL
and wholesale levels, and have both felt the impacts of fintech innovation. They have created a
significant challenge to bankers, as P2P lending has opened up new services for many kinds of
clienteles (especially artists and musicians, ethnic minorities and immigrants, and social
entrepreneurs) who lack credit fundamentals, and small business people who are unable to offer
appropriate collateral or do not have sufficient personal assets or business experience. As a result,
they typically are not viewed as “bankable” in credit-scoring terms: the likelihood of their loan’s
default is greater than the likelihood of their loan’s profitability to the lender. Fintech lending has
opened up new avenues for charitable giving in the form of loans and affinity lending, to people who
are “unbankable” but nevertheless offer interesting value propositions for the craftwork they do, for
example. Others may need funds due to the damage caused by natural events beyond their control
(e.g., hurricanes and typhoons, agricultural droughts, tsunamis and electrical damage to their homes
and businesses).15
The situation on the deposit services side is different. Traditional KYC restrictions limit the ability of
banks to permit new customers to open accounts or make fixed deposits, or existing customers to
13 This is much like the University of Pennsylvania Wharton School start-up, buySAFE did. It originally teamed with eBay
to diminish the all-in transaction costs between buyers and sellers, who had asymmetric information about the demeanor and
performance reliability of of their counterparties [32].
14 This parallels what was observed with airline price forecasting tools like FareCast (acquired by Microsoft in April 2008),
and other oil and gasoline forecasting tools for energy producers and transportation fleet cost management.
15 Unlike several of the other fintech innovation areas that we have discussed, the applications in lending services have been
very well studied since the mid-2000s, especially P2P lending, and more recently, charitable crowdfunding and equity
crowdfunding. Some of the most interesting works that have appeared in the literature to date make contributions related to
fundamental and more advanced issues. For P2P lending, they include: the differences between alternate market mechanisms
in P2P lending relative to repayment and other aspects of their performance [147]; how borrower participation in friendship
networks may reveal their propensity for P2P loan repayment or default when there are lender-borrower information asym-
metries [102]; how loan default can be mitigated with borrower information from social media in P2P lending [61]; how
informal lender-borrower social communication influences default rates in P2P lending [151], and finally, the extent to
which platform choice, observed herding in decision-making, and regulation influence P2P lending returns [84]. Some of the
findings the interested reader may wish to see in the current literature on crowdfunding include: the impacts of home bias
effects, the tendency for transactions to be made by counterparties from the same geographic area instead of outside it, in
online campaigns [103]; the effects of provision point rule restrictions on the amount of funding an entrepreneur can draw
from a fund-raising campaign [22]; and the extent to which information hiding and participant contributions influence
crowdfunding campaign outcomes [21].
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become involved in other kinds of riskier deposit arrangements (e.g., structured deposit products that
have embedded derivatives and more complex risk profiles). The role of fintech innovation for the
latter is much more focused on financial education for consumers, since current legal codes protect
banking customers from signing up for structured deposit products without first gaining a solid
understanding about how they work. In the remainder of this section, we will examine recent
developments in these areas using our fintech innovation mapping perspective to identify the extent to
which they are transforming the market, the competition among the financial services players, and the
role of new technologies in these areas that are changing the customer experience. We will also assess
the extent to which new directions for IS research are opening up, beyond the high populatory of
mechanism design and performance in P2P lending.
Deposit Services Innovations
Providing deposit accounts, such as checking and savings accounts, and giving retail and commercial
customers access to their funds remain the most fundamental and pervasive banking functions. Histor-
ically, demand deposit accounts (DDAs) or current accounts have provided for check writing and
immediate on-demand withdrawals of funds including at ATMs. On the other hand, savings accounts
are not linked with checkbooks or debit cards, and may require advance notice for withdrawals, but
generally paid a higher interest rates to depositors.
Making deposit accounts more convenient has been a focus for innovators seeking to provide a better
customer service experience and user interface. While the PC and the Internet eras have produced
successful customer-facing products and services in many industries, many technologists complain
that banks have largely failed to deliver innovative services fueled by new technology to retail deposit
customers [121]. Until recently, the progress has been evolutionary rather than revolutionary. Im-
portant but incremental developments include ATMs and remote access so that the vast majority of
retail banking services no longer occur in a brick-and-mortar branch. Moreover, checks can be depos-
ited via smartphones, and retail deposit accounts can be opened and closed without a customer going
inside a bank branch.
Fintech innovations and entrants into the deposit services market are likely to become transformation-
al in the near future in this area. While some advances will be driven by new technology capabilities,
other areas of progress lie in new analytics frameworks for classification and prediction of accounts
and behavior that lead to improvements in performance compared to traditional bank methods [27].
Several examples show how this disruption might occur.
Channels for deposit services. Branch-based banking began its steady decline in the late 1980s. The
most successful branchless bank, First Direct, was launched in 1989 by Midland Bank in the U.K.
[79]. It was separately branded and operated independently of Midland’s branch banking network.
First Direct quickly demonstrated that superior banking services could be delivered over the tele-
phone, and later via SMS messaging and the Internet. Its distinctive presence within financial services
eventually enabled it to attract 1.4 million customers, or about 15% of the U.K. current account mar-
ket. Its levels of service and brand loyalty measure among the best of any financial institution. In the
U.S., ING Direct once pursued a branchless, Internet-only banking model, but it was acquired by Cap-
ital One in 2011. Currently, there are a few other players that operate in this space in the U.S. market
with mobile-only services, including Simple, Moven, BankMobile, and GoBank. As a channel for the
delivery of financial services, branches were once dominant, as we noted earlier. Today though,
through innovation and competition, and changing customer preferences, branches have been over-
taken but not eliminated as part of multi-channel strategies pursed by the major banks, as there is still
demand for deposit services via bank branches.
Digital-only entrants and challenger banks. Atom Bank (atombank.co.uk), a U.K. start-up, launched
in 2014 as a mobile-only bank that targets millennial consumers. It has offered savings accounts, and
small business and mortgage lending via a mobile-only platform. It also has received high ratings for
the simplicity of its applications, the rate of interest it has paid, and the ability to personalize its appli-
cations for customers, including its use of biometrics instead of passwords for account entry.
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Along with other challengers, Atom seeks to attract Gen Y and millennial customers by providing
engaging ways to help them manage their money, a characteristic of fintech deposit services. Accord-
ing to Retail Banking Strategies [108], 88% of millennials do their banking online and half use their
smartphone to bank; in addition, 73% of millennials report they are “more excited about a new offer-
ing in financial services from Google, Amazon, Apple, Paypal or Square than from a traditional
bank.” This is suggestive of the great threats to traditional banking firms: the banking services provid-
ers may not necessarily be banks. These customer-centric, technology-driven firms and mobile-only
challenger banks are capable of developing engaging and convenient ways for millennial customers to
manage their money. Banking is poised to move away from its traditional utility service role, reliant
on functioning as an unexciting necessity for other aspects of consumers lives. Millennials will be
attracted to challenger banks that make their services engaging, and that recommend and provide per-
sonalized advice proactively.
Open banking platforms for fixed deposits. Open banking allows customers to share access to their
financial data with third parties whose applications and features may give customers a better banking
experience. APIs enable the integration of a third-party firm’s software applications with those of a
bank [17]. Currently, such third parties as Facebook, Google and other leading technology firms are
working directly with leading financial services firms.
One such third-party and provider of open banking services is Paypal (www.paypal.com), which has
over 218 million accounts and is one of the world’s largest non-bank financial institutions. In 1998,
Paypal began as an independent provider of online money transfer services. Its growth was aided by
the growth of eBay, and more than 70%of all eBay auctions accepted PayPal payments by 2002. eBay
acquired Paypal in 2002, and later spun it off in 2015 due to the challenges of developing two
separate high-tech business infrastructures [82]. Today, a personal Paypal account can be set up with
a unique email address, and a bank account or credit card will generally be associated with it. After
opening a PayPal account, the user can receive funds from another bank account, or a credit or debit
card, and can send funds to another Paypal account holder or to someone with an email address but no
Paypal account. To access the funds, the recipient will need to open a Paypal account and then
transfer the funds to their own bank account.
Another interesting fintech challenger is the Europe-based Deposit Solutions (www.deposit-
solutions.com), an open banking platform for deposits that was founded in 2011 in Hamburg,
Germany. Its aggregation business model is based on providing APIs that enable any bank to easily
connect and attract deposits to expand its funding base, or by offering its own clients choices of
deposit products from other institutions. To its partners, Deposit Solutions maintains one master
account that aggregates the funds attracted. As of late 2017, its banking solution was used in 50
European banks by over 85,000 retail customers, who left EUR 4 billion in deposits at the firm.
As open banking and inter-institutional APIs become more mainstream, the deposit services market
will face substantial fintech-driven disruption [153]. Fundamental issues will include who controls
customer data, and what security controls are needed when a customer conducts financial transactions
through a linked network of fintechs, service providers, and bank deposit-takers. Participating in open
banking platforms creates opportunities and vulnerabilities as in any network market, with rent reap-
propriation threats counterbalancing the larger potential user bases on open banking platforms [52].
Regulatory risks and accommodations. A number of fintech approaches to new financial services
offerings have been shown to contravene critical regulations, such as the Patriot Act of 2001 in the
U.S., and its related KYC regulations. KYC compliance requires financial institutions to verify the
identity of their clients. Since 2001, all U.S. banks have had to demonstate KYC procedures, develop
customer identification programs (CIP), and implement detailed transaction monitoring. These
guidelines are intended to avoid bank involvement, however unintentional, in criminal money
laundering activities.
Financial institutions have generally found it difficult and expensive – not mention operationally risky
– to comply with KYC regulations that require acquiring and storing sensitive personal data, while
20
also meeting their customers’ expectations of privacy . Many fintechs have helped banks meet their
need to deliver increased security through enhanced KYC capabilities, identity validation, and fraud
detection. Examples include Fraugster, Mitel, Net Guardians, BillGuard, Bionym, and Feedzai,
among many others. Other fintechs have found it challenging to innovate and grow within the
regulatory framework that applies to retail deposit services.
Overall, the fintech-led disruption of deposit services is accelerating. Innovations to date in virtual
account services have primarily complemented encumbent and traditional offerings. More radical
innovations that can substitute for current deposit service offering are on the horizon, and are likely to
transform one of the major segments of the banking industry, even though they have not been able to
accomplish this to date.
P2P Lending and Financial Inclusion
The purpose of all lending, whether via banks, credit unions, finance companies, or the new fintech
lending platforms is to address the core problem of borrowers’ cash flow mismatches. This problem
arises in situations where artists, musics, crafts people and small business entrepreneurs need to
produce artefacts, create products, and find buyers to sell to before they can generate the cashflow to
purchase raw materials and supplies, pay for the advertising services, rent their workspace, or offer
payroll compensation to their employees. The prior research literature suggests that lenders (other
than those who charge exorbitant interest rates) are usually unwilling to step into these situations with
preferential lending services, lower-rate loans, or other terms that are likely to be attractive to the
people who wish to obtain pre-sales cash flow to power new small business activities. This is easier
for banks that specialize in small and medium enterprise (SMEs) banking.
One aspect of the current developments that is interesting in this space is the extent to which we
observe similarities – albeit in digital form today – to the social lending and account management
activities offered by microfinance providers in developing countries [150]. Best known among them
is the Grameen Bank of Bangladesh, and its founder in 1976, Mohamad Yunus, who was awarded the
Nobel Peace Prize in 2006 for proposing and implementing the microfinance approach to bring small
financial lending services to the unbanked poor people in his country. Over time, this financial
inclusion approach grew, resulting in non-profit and for-profit microfinance institutions (MFIs) in
many developing countries around the world, peer-to-peer (P2P) lending markets as we know them
today, and non-governmental organizations (NGOs) that sought to shine a spotlight on the need for
financial inclusion [91]. MFIs and P2P lending markets grew to focus on lending to women but also
toward creating lending platforms to address the financial needs of poor people in developing nations.
One such MFI which has had considerable success since its founding in 2005 is California-based Kiva
(www.kiva.org), which acts to make loans in association with network-based field partners who
identify and make the case for a borrower to be funded. The borrower’s reputation is derivative of that
of its field partner [112]. Kiva, as a result, works like a multi-level advocacy-assessing risk-taking, on
behalf of lenders who are willing to commit money to borrowers, with payback required in spite of
the enhanced social welfare that is the main intention of its market mechanism. This is an extension of
past approaches intended to reduce information asymmetry in the lender, borrower and intermediary
triangular relationship [102, 122], by adding an informediating lending agent. Since the IS discipline
has focused on the development of market mechanisms and trading microstructure designs that
mitigate the negative aspects of such asymmetries, it is natural for researchers to probe the limits of
fintech-driven market mechanism innovations that will move the market from concerns about efficient
financial resource allocation to new clienteles for social value and financial inclusion [35, 83].
Charitable Crowdfunding for Natural Disasters, Weather Events, and Social Needs
FinTech is not limited to the for-profit sector. In fact, new financial models have quickly emerged as
an opportunity for savvy non-profits and community groups. Crowdfunding is the most visible
innovation and refers to any effort to raise money with donations from a large number of people.
Leading platforms are GoFundMe and KickStarter, which offers donors non-financial rewards or
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products as encouragement. Initially entrepreneurs used payment-enabled websites to attract low
value investment into for-profit ventures. Crowdfunding grew from this, and provides a new platform
for fundraising for charitable nonprofits.Websites provide appeals tailored to showcase specific
projects or causes, or community-building events.
Hurricane Sandy along the U.S. East Coast in 2012 led the Red Cross to embrace crowdfunding with
two different websites, IndieGoGo and CrowdRise. They reported that the two sites generated more
than $2 million. Overall, the Red Cross received $170 million in total donations in 2012 of which
more than half went to long-term Sandy rebuilding. Some universities are also experimenting with
crowdfunding for encouraging donations, such as Oklahoma State University’s PhilanthroPete (phil-
anthropete.osugiving.com).
A Pew Research Center report on the digital economy found that contributions to help an individual in
need are the most common type of crowdfunding donation. [129]. It also noted that “Crowdfunding
donors value their personal connection to the projects they support and the ability to highlight causes
that might not get much attention from established charities.”
Charitable giving and donors’ motivations for philathropic gifts have been topics of interest to
economists for decades. Fintech, crowdfunding platforms have been shown to alter giving behavior.
In particular, Klinowski [95] found that donors in a charitable crowdfunding platform prefer to give,
and give more, when they are completing requests and enabling the recipient to reach their
fundraising target. The authors find crowdfunder “feel more impactful over recipients when they
complete requests versus when they donate at any other stage of the fundraising campaign.” They
also “find that other aspects of the recipients profiles also attract faster donations, particularly
making reference to difficult circumstances such as a personal crisis, incarceration, and disability.”
In a study of a crowdfunded market for online journalism projects, Burtch et al. [20] found
contributions in this marketplace are subject to a crowding out effect. That is potential donors are less
likely to give when it appears the gift has become less important to the recipient. Similar to Klinowski
[95], the authors found empirically that social influence and behavioral signals are highly influential
in crowd-funded marketplaces.
While financial regulations generally keep many countries’ consumer financial service markets
separate, crowdfunding has proven to have strong appeal in international setting. Donation and
reward-based crowdfunding platforms in Asia attracted funds to help with the the 2013 Typhoon
Haiyan in the Philippines. Online alternative finance has been a surprising success story of the Fintech
Revolution. With fewer regulatory obligations, crowdfunding platform can innovate extensively, and
will continue to alter charitable giving, and benefit individuals and communities in need by expanding
the scale of their appeals.
The Changing Landscape of Investments in the Fintech World
Underlying Technology Changes in the Financial Markets
WHILE INNOVATIONS IN THE DIGITAL FINANCING, INSURANCE AND PAYMENT-RELATED BUSINESS
functions mostly emerged in the last few years and primarily targeted retail financial services,
innovation and digitalization in the investment, trading and financial markets domain on the
institutional side have built a tradition of transformation for more than 20 years [66].16 The key
16 For studies on financial markets and the firms in the investment and trading industry, the interested reader should see:
Clemons and Weber [37, 38] on competition between exchange and off-exchange venues for equity trading; Weber [146] on
open-outcry and order-matching systems in futures markets; Levecq and Weber [101] on the strategic implications of finan-
22
disruptive trigger that significantly enhanced user experience based on completely new functionalities
was the electrification of major securities exchanges in the early and mid-1990s, providing order
matching and price determination in the markets’ back-ends without human intervention. Central
limit order books have made it possible to aggregate and store open limit orders in a transparent
ledger, and to match executable orders in real-time. Trading from remote locations with decentralized
market access made trading on the physical floor obsolete. Market users, specifically institutional
investors, mirrored the digitization of markets, by implementing electronic surveillance and
automated quote machine methods. From the early 2000s, institutional investors established electronic
trading desks to source liquidity from multiple brokers and trading venues, and implemented order
management systems (OMSs) based on the Financial Information eXchange (FIX) protocol, the de
facto messaging standard for pre-trade and trade communication. In parallel, the sell-side first
implemented algorithmic trading engines to speed up and reduce costs in proprietary executions, and
subsequently started to offer algorithmic services to buy-side clients by providing frontends for
clients’ individual algorithm creation and parameterization. This enabled buy-side trading desks to
back-source responsibility for trade execution from intermediaries, which created a disruption but
enhanced user experience.
Regulators triggered competition between exchanges and alternative trading systems – especially
electronic communication networks (ECNs) in the U.S. and multilateral trading facilities (MTFs) in
Europe – paving the way for today’s multiple-market system [67]. High-frequency trading (HFT)
emerged and was primarily debated in expert circles until the 2010 U.S. flash crash dramatically
changed the perceptions of policy-makers, regulators and the public toward electronic trading [110].
HFT strategies were also a disruptive force, but they enhanced the quality of the user experience
overall, and led to massive increases in turnover velocity. Meanwhile, they further enabled the
generation of significant total profits based on small profits per trade by instantaneously reacting to
profitable situations in a highly complex and fragmented trading environment. The empirical
literature, however, devoted more emphasis on the controversial aspects of HFT. For example,
Hendershott et al. [77] and Brogaard et al. [18], among others, typically analyzed single markets, and
hence underestimated the true impact of HFT on aggregated market quality by excluding cross-market
strategies.
Risk Management, Blockchain and Regulatory Considerations in Markets
Risk management technology. Financial markets are about managing risk. Technology in markets, in
contrast, acts as a central tool to make the management of risk economically feasible. Speed in
trading, low-latency technology, co-location and proximity services all enable firms that specialize in
HFT to minimize their exposure to market price risk and stay as close as possible to their positions
and open orders. But technology also may lead to new types of risk or exacerbate existing ones.
Multiple flash crash events have revealed that academic research has not provided a full
understanding yet why extreme, flash-type market situations occur in the first place, how market
participants can manage risks and exposures in these situations, and how regulators may be able to
pre-empt such events by imposing adequate regulation.
A further category of risk, and worthwhile avenue of research, that has not materialized yet in extreme
market events is cyber risk. The increasing concentration of infrastructure at a limited number of
participants (e.g., banks and broker-dealers providing trading technology and infrastructure to others),
or central counterparties that concentrate and manage risk, but also represent a potential bottleneck in
our markets, requires more detailed analysis.
Blockchain technology for financial markets. In the financial markets arena, blockchain technology is
perceived as a game changer [116], as it enables functional improvements, innovations in existing
business models or even disruption through the creation of new business models and truly new
cial market design choices; Han et al. [72] on J.P. Morgan’s partial divestment of RiskMetrics for value-at-risk metrics
infrastructure to Reuters; [117] on the effects of order-routing on new option market success; and Kauffman et al. [89] on
technology ecosystem transformation in high-frequency trading systems.
23
products and services (disruptive, enhancing user experience). Applications that primarily supplement
existing functions already exist in the area of securities settlement, for example, to increase settlement
efficiency, shorten settlement times and reduce reconciliation effort. However, concerning securities
clearing and trading, multiple problems in the technical (scalability, IT security) and non-technical
(legal, regulatory, and tax treatment) domains have yet to be solved. In clearing, for example, credit
and counterparty risks are inherent which requires backing by a legal entity and cannot solely be
solved by blockchain technologies. In trading, as well, high-throughput and low-latency requirements
hinder the usage of current blockchain implementations for high-volume asset classes such as equities
and listed derivatives. Here, research can serve to identify and integrate both technical and non-
technical solutions to unveil the full potential of blockchain technology for financial market
applications.
Regulatory considerations. Today, regulatory compliance and dealing with legacy systems are the
biggest operational pain points for investment firms active in financial markets. Regtech solutions
involve technologies that aim to ease regulatory compliance, and substitute for manual labor in
standard regulatory and compliance processes. They also apply AI and deep learning methods to
trigger alerts for exception handling, and thereafter, deeper human analysis. Regtech offers promising
complementary solutions that are viewed as being able to provide significant improvements in
functionalities and processes [99].
On the flip side of the regtech coin are the market regulators and surveillance authorities themselves.
They have to reflect the technology-driven changes in trading and investments in their rule-making,
enforcement, data acquisition, and analysis strategies. Regulation plays a critical role in facilitating
market-enhancing innovation. Yet given the nature of innovation in fintech and digital finance,
policymakers and regulators can only realize their key goals if they have suitable and modern tools,
frameworks, and regulatory approaches that help them to generate real information from petabytes of
data. In the last ten years, numerous new regulations were implemented that have required increased
transparency and have generated massive amounts of data. All of the data have to be absorbed by
regulators in order to meet their expanding duties and more challenging tasks, specifically the
requirement to establish more proactive, data-driven regulation and surveillance approaches.
Regulators often cannot adequately process and analyze these massive amounts of data, however, due
to a lack of people, skills and technological support that are needed. Moreover, it may take months to
derive conclusions out of the data that are acquired; for example, this was especially true in the case
of the U.S. Flash Crash [26]. Academic research (e.g., Chung and Cunanan [29] and Degryse et al.
[44]) has intensively analysed the impact of new regulations on market quality, financial stability and
investor protection, thereby supporting more effective regulatory impact analysis (RIA) [54]. Yet,
academic research often is only known to regulators after the respective projects are executed and
published leaving limited room for mutually shaping their research questions and directions.
Furthermore, most researchers implement a “one problem, one data-set, one publication” approach,
and target isolated events or use single-market data. They more rarely seek to carry out analyses over
longer periods of time with multi-market data.
Moreover, different methods, dissimilar national and regulatory contexts, and alternate time horizons
or data fields within the empirical or self-established datasets (for simulation and scenario analysis)
often lead to contradictory results. This lessens the generalizability of the findings, and thus limits
their value for actual policy-making and regulation. A prominent example of this phenomenon is the
analysis of HFT. Despite a research history into this topic of nearly ten years and hundreds of papers
that analyzed the impact of HFT on market quality and integrity [110], there is still no general
agreement in academia on whether HFT strategies are positive or dangerous for markets and
investors. In addition, there is no consistency in the views about whether and to what extent these
strategies need to be regulated. Other still heavily debated topics include, for example, the question of
whether the mandatory usage of central counterparties actually reduces, or possibly even increases
systemic risk in markets. A related question centers on the adequate levels of transparency required
for less liquid asset classes.
24
Academic researchers should play a central role in assisting regulators and policy makers in evidence
based policy-making (EBPM) before regulations are drafted [46] and sound RIA once regulations are
applied. Regulators need tools, methodologies and infrastructure to conduct rigorous EBPM and RIA,
and to identify solutions that serve the goal of better regulation. Systematic and joint academic-
regulatory topic identification, research agendas, and systematic exchanges of methodologies have the
potential to significantly improve the analysis of regulatory effects and financial regulation itself.
These also relate to the impact of new technologies (e.g., blockchain, new payment solutions) on
regulators’ work. They also will support the assessment of how technologies like ML or AI can assist
regulators in their tasks, including developing a deeper understanding of trade manipulation, market
abuses, and cross-market surveillance.
In the retail trading space, online-brokerage technology enabled retail investors to access markets and
market data similar to how professional traders do it. This democratization of markets and access
(disruptive, and enhancing user experience) was completely unthinkable in times of floor-based
trading. However, the digitization of markets and trading differs across asset classes. While in liquid
asset classes (e.g., equity, FX and futures trading) a high level of electrification and HFT is prevalent,
in the context of less liquid instruments (e.g., corporate bonds, small cap equities), mechanisms
design and adequate market models represent a promising route for academic research inquiry.
The Rise of Robo-Advisory Services and Social Trading Platforms in Investments
In the digital investments business function for retail customers, robo-advisors [7] (i.e., “algorithms to
develop automated portfolio allocation and investment recommendations tailored to the individual
clients” [45]), and social trading platforms [68] (i.e., “a combination of a social network and a
trading platform offering users the possibility to share their investment ideas [… and to …] copy
ideas and/or trades of other users” [123]) are discussed intensively. They promise to offer more cost-
efficient and customer-oriented ways to set up and manage investors’ portfolios. They offer
complementary value in tandem with other approaches and also enhance user experience. These
fintech offerings in digital investments cherry-pick point solutions which represent small parts of the
whole range of services that large financial service providers can provide. They also disintermediate
markets that incumbents – in this case financial advisors or asset managers – have created and
developed over decades.
Robo-advisors enable automated acquisition of information and data processing to provide investment
proposals with little or no human intervention based on pre-defined parameters based on customers’
investment goals, financial background and aversion to risk. They make use of established theories,
such as the perennially applicable modern portfolio theory of Harry Markowitz [124]. They also
usually offer a limited set of asset classes, such as equities or ETFs to provide investors with low-cost
solutions. However, they are typically not designed to consider the more personal aspects of financial
investments like real estate or individual tax situations. Furthermore, they focus solely on the
investment aspects of financial advice, and are not able to provide financial solutions that integrate
additional aspects of investing, financing, tax considerations, and insurance solutions. Such an
integration of a diverse range of fintech and non-fintech solutions would be able to generate cash flow
and risk structures that come closer to meeting customers’ specific situations and individual goals.
Furthermore, from the customer viewpoint, there has been considerable fragmentation among the new
and traditional services. The new services are represented by robo-advisors and social trading
platforms, while the traditional services occur as broker-based investment advice and market
newsletters. Customers are required to handle a multitude of providers with specific apps,
identification procedures, and fee structures, which generate complexity and reduce ease of service
usage. Testing innovative fintech solutions may be of specific interest for a technology-savvy
clientele and the innovativeness of individual applications may attract initial clients, however, most
providers of robo-advisory services and social trading platforms are hardly able to achieve sufficient
revenues or profits to sustain their business models. So the providers and those who undertake
research efforts in this direction should keep in mind that, for customers, financial products are not an
25
end in themselves but are often seen as a disliked but necessary means to fulfill life-driven goals.
Some of the common goals involve funding children’s education, new house purchases, and early
retirement portfolios.
We see high potential for academic research that contributes to sophisticated fintech aggregations that
include innovative and complex financial strategies designed for multiple customer age and wealth
groups including less affluent customers, as well as those with different device type experience
besides millennials. This approach to high automation finance makes use of innovative technologies,
such as neural networks, deep learning, and big data. It also applies DLT and smart contracts to
perform intelligent data collection and integration strategies, as well as innovative financial
management and financial engineering to combine fintech offerings that result in personal and
tailored customer solutions [149]. Specific research topics in this area relate to high-quality customer
profiling and client segmentation based on individual financial data and risk perceptions. The
identification of concepts and methodologies based on big data analytics to identify trigger points
within customer digital history and online search behavior records to actively propose tailored
financial services is also worthy of research. High automation finance methods can be further
enhanced through the study of innovative user experiences supported by knowledge for just-in-time
financial literacy to provide customers with personal digital financial twins that will be able to mimic
their personal circumstances and help them to identify appropriate investments.
Conclusion
ASSESSING THE INNOVATIONS, DISRUPTIONS AND TRANSFORMATIONS IN THE FINTECH REVOLUTION
will need to be done on a repeated basis going forward, since the extent of emerging technologies,
reengineered and new processes, and customer-friendly and value-bearing products and services seem
to be undergoing continuous development and implementation. The take-aways that we have to offer
the reader consist of several main points related to how the fintech sector will develop over time, and
what IS researchers can do in order to contribute new knowledge in this vibrant area of technology
innovation, process disruption, and services transformation. They are:
(1) It will be difficult for larger incumbent firms to match small entrepreneurial start-up firms at
producing value-creating fintech applications with high innovation, without major spending
to acquire knowledgeable human capital that is in such short supply in the marketplace.17 As a
result, it will be appropriate for larger firms to outsource the applications, instead of trying to
create them in-house.
(2) The fintech sector is likely to experience significant adjustment and evolution as time passes
and it matures into a typical industry sector, as opposed to one of the newest among them, and
probably sooner than many observers may expect.18
(3) The opportunities for developing a new research agenda for IS research in the fintech
application areas that we have reported on in this article have strong potential for creating
high-value academic knowledge. The new research agenda also can deliver important and
useful insights to practitioners and managers, as well as meaningful new observations and
ideas that can aid regulators in doing better to oversee the new developments in a way that
17 This will likely be caused by their lack of technical expertise in some domains of emerging technologies, including ML
and AI, natural language processing, blockchain app development, data science and IoT sensors, and the exploitation of open
APIs. It is unlikely that the demand for the software development and hardware specialists, along with cybersecurity experts,
will abate any time soon. As a result, financial services firms in many countries around the world will be forced to outsource
for application, product, and service development source that are not brought in-house.
18 Dietz et al. [50] have pointed to multiple kinds of changes that are likely to occur: expansion in the scope through the
value chain of fintech innovation application; fintech start-up diversification in technology, segment and geographic terms;
partnerships and alliances to achieve new functionality and higher business value, as well as industry consolidation due to
scale-focused acquisitions; a slowdown in value growth to more normal market valuations for fintech start-ups; regulators’
involvement in the development of fintech clusters; and finally, the emergence of much larger fintech ecosystems that span
industries and geography.
26
will maximize their positive potential to support economic growth, new jobs for the high-tech
workforce, and improved profitability around more customer-centric and value-bearing
services.
Some of the new research questions that we envision the new agenda for fintech-focused IS research
will include are:
(1) What will be the aggregate value of the fintech sector’s contribution to financial services
GDP? What will be the drivers for success among fintech start-ups, as the market becomes
more crowded and the “low-hanging fruit” of technology innovation are harvested in
industry? Will knowledge from the development and marketing of fintech innovation-based
products and services spill over into other areas of business involving technology?
(2) Will the fintech sector be characterized by centripetal forces that lead to its agglomeration
(market linkages, local knowledge externalities, thick labor markets, and national
regulations)? Or will we instead observe the domination of centrifugal forces, leading to the
globalization of the industry with punctuated equilibria of local innovations that are spun off
for increasingly international R&D-driven service transformation? What theoretical basis is
appropriate to apply or develop to understand the geographical locations of the core fintech
innovations?
(3) Can we expect to see a fintech value paradox, analogous to but different than the IT
productivity paradox reported on by IS and Economics scholars? Will a continuing lack of
standards, incomplete systems integration, and remaining “islands of data” within industry
firms and across the industry overall be as harmful as before? What can IS scholars do in
order identify the tactics that firms can employ to appropriate the greatest amount of value
from the fintech-based products, services, infrastructures, and innovations they deploy? Will
it be possible to characterize different but unique value appropriation paths that banking firms
can follow to maximize their success with fintech value appropriation? If so, what theoretical
perspectives will support such an effort?
(4) Will the issue of ownership of systems re-emerge in the Fintech Revolution, making it so that
not all of the incentives of stakeholders are properly aligned? Can we expect to see individual
firm value maximization gradually transition to shared views of valuation harmonization in
fintech application areas that require more infrastructure than what any firm can provide?
Will such fintech innovation activities as faster payments infrastructure, multi-firm
cybersecurity investments, and social investing platforms create the impetus for greater
cooperation among firms?
(5) How will the business models, operations and microstructure of leading financial markets be
affected by the developments that are occurring in the fintech sector, specifically as
blockchain technology might challenge their very foundations? Will exchanges implement the
highest- tech approaches, involving ML, AI and data stream sensing analytics, beyond what
we already observe is being done by firms to understand the role that social sentiment, the
sudden injection of new information, and the other quantitative indicators play in guiding
market trading? How will market operators respond to the threats due to ICOs that might
disintermediate the concept of traditional IPOs and how will regulators be able to secure
investor protection in this context?
(6) How will regtech innovations come into the industry? Will there be a need for new models of
technology adoption that are specific to such concerns as cybersecurity and regtech
investments? This models will aim at diminishing the rising risks of high-tech and machine-
based fraud.
These are among the numerous and unique research questions that are awaiting further study in this
area of IS research. It is clear that there is room for developing threads in new fintech research that
incorporate existing knowledge from a range of interdisciplinary sources. They include: IS, Finance
and Economics, Strategy and Organizations, Marketing, Statistics and Data Science, Operations
27
Management and Management Science, and Computer Science. Studying the issues associated with
the Fintech Revolution in future years will be more effective when IS researchers employ
interdisciplinary research designs, theory and thinking, and methodologies.
ACKNOWLEDGMENTS. The authors would like to acknowledge the following people and organizations who have
sponsored and supported research in this area for the past several years that some of us have done: Peter and
Nancy Murphy at the SWIFT Institute in London; Mats Wallén and Johan Weijne at Bankgirot, Sweden for
research project-related discussions on the volume and content of this payment settlement intermediary’s intra-
day retail payment transactions; colleagues at Citibank in Singapore, and the E-Finance Lab and the Research
Center SAFE at Goethe University Frankfurt; and Steve Miller, Emmy Hoang, Zhiling Guo, Dan Ma, Mei Lin,
Paul Griffin, and Dan Geng at Singapore Management University. We also wish to thank the authors of the
“Fintech Revolution” Special Issue articles, for contributing to our: Gordon Burtch, Jan Damsgaard, Daniel
Gozman, Chad Ho, Yili Hong, Yang Jiang, Erol Kazan, Jonathan Liebenau, Eric Lim, De Liu, Jonathan
Mangan, Feng Mai, Carsten Sørensen, Chee Wee Tan, Yong Tan, Jennifer Xu, and Xiangbin Yan, Jonas
Hedman, Kalina Staykova, and the anonymous reviewers. All errors and omissions are the sole responsibility of
the authors.
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