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Abstract

This paper offers a systematic review of academic and practitioner-oriented literature on FinTech to determine the literature's existing scope and examine the intersection with work in the Information Systems (IS) field. Findings from our review show that the practitioner-oriented literature foreshadowed the rise of FinTech by extensively reporting on algorithm-based and electronic trading (2009 onwards), followed by reporting on FinTech start-ups and funding successes (2014 onwards). The practitioner literature subsequently reported on alternative finance models, the introduction of cryptocurrencies, and risks and regulatory issues. Academic literature on FinTech began to rise from 2014 onwards, focusing initially on the development of FinTech in the aftermath of the 2007-2008 global financial crisis. Research attention subsequently shifted to FinTech innovations (alternative finance, cryptocurrency and blockchain, machine-based methods for financial analysis and forecasting, including artificial intelligence), as well as risk and regulatory issues. IS work on FinTech started to emerge from 2015 onwards, initially focusing on mobile payment systems and peer-to-peer lending. However, the body of work at the intersection of FinTech and IS is still small. Our review sheds light on several opportunities for future research, including financial inclusion, the impacts arising from COVID-19, and the emergence of new business models, such as Banking as a Service (BaaS).
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Trends in FinTech Research and Practice: Examining the Intersection with the
Information Systems Field
Cynthia Cai
Macquarie Business School
Macquarie University, Sydney, Australia
cynthia.cai@mq.edu.au
Mauricio Marrone
Macquarie Business School
Macquarie University, Sydney, Australia
mauricio.marrone@mq.edu.au
Martina Linnenluecke
Centre for Corporate Sustainability and Environmental
Finance, Macquarie Business School
Macquarie University, Sydney, Australia
martina.linnenluecke@mq.edu.au
Please cite this article as: Cai, Cynthia; Marrone, Mauricio; Linnenluecke, Martina: Trends in FinTech Research
and Practice: Examining the Intersection with the Information Systems Field, Communications of the
Association for Information Systems (forthcoming), In Press.
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ommunications of the
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Research Paper ISSN: 1529-3181
Accepted Manuscript
Trends in FinTech Research and Practice: Examining
the Intersection with the Information Systems Field
Cynthia Cai
Macquarie Business School
Macquarie University, Sydney, Australia
cynthia.cai@mq.edu.au
Mauricio Marrone
Macquarie Business School
Macquarie University, Sydney, Australia
mauricio.marrone@mq.edu.au
Martina Linnenluecke
Centre for Corporate Sustainability and Environmental
Finance, Macquarie Business School
Macquarie University, Sydney, Australia
martina.linnenluecke@mq.edu.au
Abstract:
This paper offers a systematic review of academic and practitioner-oriented literature on FinTech to determine the
literature's existing scope and examine the intersection with work in the Information Systems (IS) field. Findings from
our review show that the practitioner-oriented literature foreshadowed the rise of FinTech by extensively reporting on
algorithm-based and electronic trading (2009 onwards), followed by reporting on FinTech start-ups and funding
successes (2014 onwards). The practitioner literature subsequently reported on alternative finance models, the
introduction of cryptocurrencies, and risks and regulatory issues. Academic literature on FinTech began to rise from
2014 onwards, focusing initially on the development of FinTech in the aftermath of the 2007-2008 global financial crisis.
Research attention subsequently shifted to FinTech innovations (alternative finance, cryptocurrency and blockchain,
machine-based methods for financial analysis and forecasting, including artificial intelligence), as well as risk and
regulatory issues. IS work on FinTech started to emerge from 2015 onwards, initially focusing on mobile payment
systems and peer-to-peer lending. However, the body of work at the intersection of FinTech and IS is still small. Our
review sheds light on several opportunities for future research, including financial inclusion, the impacts arising from
COVID-19, and the emergence of new business models, such as Banking as a Service (BaaS).
Keywords: AlgoTrading, cryptocurrency, literature review, machine-based learning, research trends, risk and
regulation, financial inclusion, COVID-19, Banking as a Service (BaaS), RegTech.
[Department statements, if appropriate, will be added by the editors. Teaching cases and panel reports will have a
statement, which is also added by the editors.]
[Note: this page has no footnotes.]
This manuscript underwent [editorial/peer] review. It was received xx/xx/20xx and was with the authors for XX months for XX revisions.
[firstname lastname] served as Associate Editor.] or The Associate Editor chose to remain anonymous.]
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“Bright young things based in San Francisco, New York, London and Stockholm are raising billions of dollars
in venture capital to “disrupt” financial services. With much brashness, these t-shirt-wearing whizz-kids are
confident they will do to banks what digital photography did to Kodak.”
- The Economist, June 17th of 2015
1 Introduction
Many industry sectors have experienced significant disruption in recent years through the introduction of
new financial technology (or FinTech), including process automation in financial services and the adoption
of cryptocurrencies. FinTech first emerged in the early 1990s, even though more traditional financial
technologies have a much longer legacy than the term itself (Cai, 2018). From the first telegraph cable in
1866 to blockchain in 2009, the evolution of financial technologies has always been aligned with innovations
in information systems (IS). A move to ‘digitization’ commenced in the late 1960s and has led to an increased
use of IS infrastructure in financial communications and transactions, but the competitive landscape of the
financial industry remained relatively stable until the early 2000s (e.g., Alt et al., 2017; Arner et al., 2016).
In recent years, however, the FinTech movement has substantially transformed the provision of financial
services, mainly due to start-ups and technology firms entering the market to provide niche services to
customers, businesses, and banks themselves (Arner et al., 2016). Such a transformation is reshaping the
market infrastructure and the entire financial system.
In parallel with the rapid progress in the uptake of FinTech, there has been a substantial rise of publications
on the topic in a range of outlets. The purpose of this paper is to conduct a systematic review of key research
trends in FinTech to determine the existing scope of the literature and to examine the intersection with work
in the field of IS, using a systematic review technique (Linnenluecke, et al., 2020; Marrone & Hammerle,
2017). We broaden the scope of the literature review by including both academic publications, as well as
“grey literature” (i.e., outlets such as CIO Magazine, Computerworld, and Forbes), as proposed by Schryen
(2015). The addition of practitioner literature is beneficial for the context of the paper because applications
of FinTech have been extensively discussed in this literature ahead of academic research (see also Rossi,
et al., 2019). Specifically, our paper maps bursting themes, or “hot topics”, in both the grey and academic
literature to demonstrate how attention to various aspects of FinTech has shifted. While there has been a
notable increase in IS publications on the topic in recent years (Lagna & Ravishankar, 2021), the IS field
has only recently started to provide more substantial contributions to FinTech research. We identify new
and emerging topics that have attracted recent attention from practitioners and researchers in other fields
and might be of future interest to IS researchers.
Our paper responds to calls for more scoping reviews in the field of IS (Schryen et al., 2017) and emerging
research connecting FinTech with IS research (Lagna & Ravishankar, 2021). Our findings are based on an
analysis of academic publications and practitioner articles, respectively. Specifically, we analyzed 1,261
academic conference papers and journal articles (gathered from Scopus) and a further 6,816 practitioner-
oriented articles (collected from Factiva). Findings from our review show that the practitioner-oriented
literature foreshadowed the rise of FinTech by extensively reporting on algorithm-based and electronic
trading (2009 onwards), followed by reporting on FinTech start-ups and funding successes (2014 onwards).
The practitioner literature subsequently reported on alternative finance models, the introduction of
cryptocurrencies, as well as risks and regulatory issues. Academic literature on FinTech began to rise from
2014 onwards, focusing initially on the development of FinTech in the aftermath of the 2007-2008 global
financial crisis. Research attention subsequently shifted to FinTech innovations (alternative finance,
cryptocurrency and blockchain, machine-based methods for financial analysis and forecasting, including
AI), as well as risk and regulatory issues. Publications in IS have remained few in number, even though IS
played a vital role across nearly all components of FinTech applications and business models (Mamonov,
2020). With boundaries between finance, FinTech and IS becoming more blurred, our review sheds light on
several opportunities for future research, including financial inclusion, the impacts arising from COVID-19,
and the emergence of new business models, such as Banking as a Service (BaaS) (i.e., the integration of
digital banking services into products and services offered by non-bank businesses).
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2 Background
The introduction of IS, starting from the 1960s onwards, enabled financial communications and transactions
to become largely digitalized and offered a gestational environment for the FinTech movement. For instance,
digital technologies started to spread along the banking value chain and demonstrated that products and
services of the entire financial industry could be supported by IS. Inefficiencies in the financial intermediary
system (such as high transaction cost and high inherent systematic risks) led to fertile ground for the FinTech
revolution in the aftermath of the 2007-2008 global financial crisis (e.g., Alt et al., 2018; Arner et al., 2016;
Philippon, 2016). In 2009, the first Bitcoin was mined (Nakamoto, 2008), which substantially accelerated
FinTech development. Other innovations followed for instance, Ripple was launched in 2012, aiming to
build a cross-border, real-time settlement system based on blockchain technology. Ethereum, an open-
source blockchain project, was proposed in late 2013 and went live in 2015. Tractable, an artificial
intelligence (AI) image estimating startup, was founded in 2014 and used deep learning algorithms to predict
repair costs of car accidents in real time, disrupting the traditional insurance claim process. These
technological advancements became catalysts of new innovative products and services in the financial
market that were subsequently adopted across many industries.
Figure 1 shows the increase in publications over time, as well as a summary of the main recent trends in
both the practitioner and academic literature that we derive from our analysis (detailed further below). As
evident from Figure 1, research on FinTech at the time was initially scarce, and available information mainly
consisted of publications in practitioner outlets recognizing the disruptive potential of FinTech and focusing
on disruptive products/services and the trending applied technologies on which they were based. In addition,
new financial products and services arrived that were not necessarily a direct outcome of new technology,
but of new business models they enabled. For instance, crowdfunding (including peer-to-peer loan and
equity crowdfunding), which provides an alternative financing channel for borrowers and start-ups, is based
on well-established information technology: the Internet. M-Pesa (a mobile payment solution in Kenya) is
another such example and combines SMS technology with a Hawala System
1
. As our review will show, the
practitioner literature extensively reported on these and other new products and services, contributing to the
substantial surge in practitioner articles.
Figure 1. Practitioner and Academic Literature on FinTech
Research attention towards FinTech slowly increased from around 2014 onwards, when some of the early
FinTech start-ups began to achieve significant success: customers started to switch their bank accounts to
1
The Hawala system is an informal method of transferring money without moving any currency physically. It is an alternative remittance
channel outside the traditional banking systems.
0
200
400
600
800
1000
1200
1400
1600
1800
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Academic Practitioner
2009
Start of
Bitcoin
2014
FinTech
starts to
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widespread
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disruptors, and a large amount of venture capital flowed into technology-led companies, making incumbents
nervous. While the academic literature on FinTech began to rise at that time, it retained a dominant focus
on the area of finance, focusing initially on the development of FinTech in the aftermath of the 2007-2008
global financial crisis. Publications in IS journals on FinTech (with a focus on mobile payment systems and
peer-to-peer lending) started to emerge mainly from 2015 onwards (e.g., Hedman & Hennigsson, 2015; Liu,
Brass et al., 2015; Liu, Kauffman et al., 2015; Malekipirbazari & Aksakalli, 2015) but the body of work at the
intersection of FinTech and IS has remained small. In the next section, we further detail our method of
determining the existing scope of the literature, as well as key research trends and future research
directions, using a systematic review technique.
3 Methodology
This section summarizes the step-by-step procedure we followed for our review. Papers for potential
inclusion were first identified by conducting a systematic literature search on FinTech. We then cleaned this
dataset and subjected it to a burst detection algorithm. This technique was initially developed for studies in
natural language processing (Kleinberg, 2003; Small et al., 2014) and subsequently implemented to identify
key research trends (Cai et al., 2019; Linnenluecke et al., 2020). It involves two steps: entity linking and the
use of a burst algorithm (further detailed below). In combination, these two steps enabled us to identify
research topics in the academic and practitioner literature on FinTech. We analyzed each body of textual
data separately due to the larger number of contributions from the practitioner literature, which would
otherwise dominate findings.
3.1 Data collection, retrieval, and categorization
First, we conducted a systematic search for publications on FinTech for further analysis. Relevant academic
publications were identified by searching the Scopus database (for academic articles) and Factiva database
(for practitioner articles) for the keywords ‘FinTech’, ‘Financial Technologies’ or ‘Financial Technology’. Our
search in June 2020 for those keywords returned 1,261 academic publications from Scopus and 6,816
publications by practitioners from Factiva
2
. We extracted the data in the form of comma-separated values
(CSV) files recording the title, year, and abstract of each article. We then merged each title with the
corresponding abstract (omitting any keywords in the academic articles since TAGME operates on
unstructured but coherent text, not on text fragments like keywords). To gain a deeper understanding of the
academic publications retrieved, we used the Academic Journal Guide (2018) list, produced by the
Chartered Association of Business Schools (ABS) in the UK, to identify the fields and quality of the
publications
3
. Out of the 1,261 publications retrieved, 235 publications appear in outlets that were listed in
the Academic Journal Guide (2018). Of these 235, 64 appeared in Finance journals, 42 in Information
Management journals and 30 in Economics journals. Results are presented in Appendix (Table A3).
3.2 Entity linking
We analyzed the practitioner and academic data sets separately because of the different nature of the
textual data. The first step in the analysis of each dataset is entity linking, which serves to identify topics
within the articles included in each dataset. Entity linking simplifies the disambiguation of words and
phrases, enabling literature to be analyzed despite varying terminology for the same concept. Entity linking
is a technique in natural language processing to allow the automatic identification of topics, rather than
obliging researchers to manually group words into categories. In entity linking, meaningful items in a text
are linked to a catalogue, or a comprehensive knowledge base, like Wikipedia (Piccinno & Ferragina, 2014)
to disambiguate expressions that could have more than one referent. Attributing each meaningful mention
to a specific entry in a larger catalogue (Ferragina & Scaiella, 2010) also allows merging different strings
that refer to the same item, e.g., to identify U.S., USA, and the United States of America as synonyms.
Following Cai et al. (2019) and Linnenluecke et al. (2020), we use TAGME to identify topics in each abstract
and title pair for academic articles and full text and title for the practitioner articles. TAGME uses Wikipedia
articles as its database to annotate strings of terms as ‘topics’ (Ferragina & Scaiella, 2010). This database
ultimately enables topics to be disambiguated. TAGME is seen as an effective means of annotating and
2
The Factiva setting ‘Duplicates: similar’ was selected to remove duplicate articles.
3
We note the limitations with journal lists, see Fitzgerald et al. (2019) and George (2019).
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disambiguating texts (Cornolti et al., 2013; Ferragina & Scaiella, 2010). It is especially effective in annotating
short texts, like articles in newspapers or journals, or abstracts from them (Ferragina & Scaiella, 2010). Prior
work (see Cai et al. 2019) has established the validity of this approach when comparing findings from the
automated approach using TAGME with results from traditional analysis of the literature (via manual coding
of the articles), based on an analysis of 3,967 articles.
To achieve optimal performance, we based the parameters for TAGME on those used in previous research
by Cuzzola et al. (2015), with the area-under-the-curve F measure being the stochastic setting of tunable
parameters (long_text 10, epsilon 0.427, q = 0.1613). The parameter, ‘long_text’ allows the analyst to
specify how many topics can be used in the parsing stage to annotate a particular mention; epsilon assists
in fine-tuning the disambiguation process, with a higher value preferable for more common topics, but lower
values better suited to the context in the disambiguation stage; and q is the threshold of the confidence
score in the pruning stage. Table 1 shows examples of topics extracted with TAGME. After completing the
disambiguation step, we deleted any false positives; that is, topics that had little meaning in the context
where they occurred in the document. Some false positives found in our sample were ‘The power (snap!
song)’, ‘The T.O. show, and ‘Voices of Animals and Men’. We also deleted expressions that related to the
research process in general, instead of a specific method or research topic derived from an academic field;
that is, terms such as ‘evidence’, ‘concept’, ‘case study’, ‘theory’, ‘research’ or ‘qualitative methods’. We
also deleted copyright information included in the abstracts for purposes of the analysis.
Table 1. Example of Format for Tables (Source)
Example Title and Abstract
Topics extracted in TAGME
Nurturing a FinTech ecosystem: The case of a youth microloan startup in
China
Financial technology, or FinTech, involves the design and delivery of
financial products and services through technology. It impacts financial
institutions, regulators, customers, and merchants across a wide range of
industries. Pervasive digital technologies are challenging the fundamentals
of the highly regulated financial sector, leading to the emergence of non-
traditional payment systems, peer-to-peer money exchanges and
increased turbulence in currency markets. This case study explores the
development of a FinTech company in China that offers microloans to
college students. Five lessons learned are presented for organizations to
better manage the challenges and to leverage the opportunities amidst the
disruption of financial sector. Our findings also shed light on how digital
technology 1) offers the strategic capability for a firm to occupy a market
niche in financial sector, 2) enables the generation of alternative credit
scores based on non-traditional data, and 3) improves the financial
inclusion of previously excluded market segments.
Information management
Financial technology
Design
Financial services
Technology
Financial institution
Payment system
Peer-to-peer
Money Exchange (organized market)
Foreign exchange market
Economic development
China
Microcredit
Organization
Management Leverage (finance)
Business
Financial inclusion
Note: Our analysis proceeds with the most extracted topics as many topics receives only very few tags and can thus be considered
not to be among important or trending topics.
To further establish the validity of the TAGME analysis for purposes of this paper, we gave the results of
the automated analysis for further review to two independent reviewers. The reviewers reported overall
agreement with the tagged topics in 97.54% of cases. We reran the analysis by removing tags in doubt, and
results remained unchanged.
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3.3 Application of the burst algorithm
After completing the entity linking process, the next step was to extract topics that showed significant
increases in attention to chart topic development. For this purpose, we used Kleinberg’s (2003) burst
detection algorithm, which is increasingly widely used to identify emerging topics from increases in their
occurrence in a literature (e.g., Mane & Börner, 2004). Burst detection is a technique for describing the way
topics develop over time. It detects increased activity during a series of specific events at a known time
through an infinite Markov model. In this method, a document stream is created, which represents
documents over a given period. The model represents the document stream as a continuous state space;
the bursts appear within this state space as state transitions (Cai et al., 2019). Thus, a topic burst can occur
in conjunction with new developments in the topic or with a sudden surge of articles related to a topic area,
such as in a special issue.
Kleinberg’s (2003) basis for the burst detection algorithm was the co-occurrence of a topic’s appearance in
a document stream with a ‘burst of activity’. It aims to identify the global structure of a document stream; the
algorithm requires topics to have a certain intensity to be classified as ‘bursting’. It also recognizes that the
appearance of topics in document streams is frequently non-uniform over time. Research fields, for
example, feature topics or themes that appear, become more frequent for a while, and then steadily
diminish. Identifying “hot” or bursting topics enables researchers to develop a greater understanding of the
way the field has developed and areas where different researchers are concentrating their efforts. Python
provides a library called burst_detection (version 0.1.3) which is also used in our study, with the resolution
of state jumps (the distance between states) set to 2 and the gamma (i.e., the probability of transition) set
to 0.5 (see Cai et al., 2019 for further explanation of these parameters) and a minimum frequency of topic
appearance of 10. The procedure calculated the number of unique appearances of a topic within each
abstract, with ‘burstiness’ considered to take place when topics appeared five times or more.
In the last step, and to facilitate the presentation and discussion of the results, we grouped the topics
extracted from the burstiness analysis according to overarching themes through researcher triangulation
(Braun & Clarke, 2016). To do so, we examined commonalities and differences amongst the topics that
burst (and publications belonging to the respective topic), decided on an overall heading that would best
represent the topics, and then allocated the topics accordingly (see subheadings in Figure 2 below). Below,
we provide a detailed discussion of these topic bursts and provide supplementary tables in our appendix
(Table A1 & A2).
4 Results
Results from our analysis are shown in Figure 2. The figure shows a timeline of topics and topic bursts.
Topic bursts in the academic literature are presented in red, and topic bursts in the practitioner literature are
presented in blue. When examining the results, it is important to note that some topics burst in the
practitioner literature because relevant articles mention companies that have the term “financial technology”
as part of their company name. The topic ‘China’ was bursting in the practitioner literature in 2011/2012 due
to a financial report scandal of a Chinese company called Longtop Financial Technologies. This scandal led
to bursts in the topics ‘U.S. Securities and Exchange Commission’ and ‘Audit’, but these topics were more
closely related to the company itself, rather than financial technology more generally. A similar burst for the
topic ‘India’ occurred when Spot Contracts on the National Spot Exchange of India’s commodities exchange
market were suspended for some time. This exchange market was owned by a company called Financial
Technologies (India) Ltd. This led to associated bursts in the topics ‘Commodity’, ‘Multi-Commodity
Exchange’, ‘National Spot Exchange’, and ‘Trade’. Lastly, the topic ‘Equity (Finance)’ relates to two
companies called Fintech and Financial Technologies (India) Ltd. While these bursts represent “legitimate”
bursts related to the phrase “financial technology”, we, therefore, do not further examine these topics in our
discussion below.
We use the figure to review research trends and augment our discussion by providing additional contextual
explanations and examining the intersection with work in the field of IS. Our scope review suggests that
research on FinTech was primarily driven by developments in practice (i.e., phenomenon-driven), except
for more technical research focusing on the role of machine-based methods for financial analysis and
forecasting, which was advanced by academic research. As we will show below, the IS community has
started to take an interest in the topic of FinTech, but much of the IS literature has remained peripheral
when compared to the main body of literature developing in the field of finance. We discuss these themes
in the following sections and cite relevant papers from our sample, where appropriate. Since we have a
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substantial sample of papers, we were unable to include all relevant papers in our discussion; therefore, we
have drawn upon examples from highly cited papers or that illustrate a certain point particularly well.
4.1 Rising Awareness of FinTech
Substantial growth of technological applications and innovations in the financial industry became noticeable
from about 2009 onwards. Initially, even though some of these FinTech innovations had the potential to
disrupt the financial system, they were neither prominent enough to result in significant uptake of the new
technology or a significant shift towards new business models, nor were they attractive enough to draw wide
attention from practitioners and researchers.
Algorithm-Based Trading: The topic of ‘Derivative (finance)’ has the longest burst amongst all topics in
the practitioner literature, which can be attributed to the substantial interest in algorithm trading/electronic
trading in the derivative markets. Practitioner-oriented articles associated with this topic burst have
discussed how the financial system was greatly affected by automation and the use of algorithms in areas
such as banking, payment, asset management, quantitative trading and “quant” investing (e.g., Gara,
2016a; Gara, 2016b). The topic first burst in 2009 when G20 leaders committed to reform over-the-counter
(OTC) derivatives trading (central clearing, and the exchange or electronic trading of standardized OTC
derivatives, where appropriate) to address weaknesses in the OTC derivatives market that were exposed
during the 2007-2008 global financial crisis. The topic is closely related to the theme “Risk and Regulatory
Issues” (detailed below): In the U.S., subsequent reforms were carried out under the Dodd-Frank Wall Street
Reform and Consumer Protection Act (the Dodd-Frank Act) and rulemakings by U.S. agencies, including
the Commodity Futures Trading Commission, Securities and Exchange Commission (SEC), as well as
prudential regulators including the Federal Reserve (Federal Reserve Bank of New York, 2020). These
reforms and resulting changes were widely reported in the practitioner literature, resulting in the continuous
burst of the ‘Derivative (finance)’ topic.
The dearth of IS research on OTC derivatives and algorithm-based trading is perhaps not surprising, given
that algorithm-based trading strategies have been primarily used by traders, analysts, and investors.
However, a separate stream of publications in IS journals has recently emerged, focusing on algorithm-
based trading in the cryptocurrency market. For instance, researchers have examined the design,
performance, and optimization of cryptocurrency trading information systems (see Fang et al., 2020). Others
(e.g., Vo and Yost-Bremm, 2020) discuss algorithm-based trading strategies for cryptocurrencies and
suggest a need to further investigate the creation, efficacy, and utilization of trading algorithms in IS
research. Some studies have expanded on these recommendations and have examined the use of deep
learning and neural networks for trend predictions of cryptocurrency exchange rates (e.g., Alonso-Monsalve
et al., 2020). This work has only recently emerged and has therefore not resulted in a new topic burst; it is
also overall more closely connected to the application of Artificial Intelligence (AI) methods, further
discussed below.
FinTech Start-Ups and Growing Academic Interest: A distinct shift of attention to FinTech started in 2014
when, after a few years of accumulation, some FinTech start-ups achieved huge success. Successful
FinTech funding cases were extensively reported in the business news and practitioner outlets, leading to
the subsequent substantial spike in practitioner publications on the topic, represented in Figure 1 above.
One example is the Australian FinTech invoicing app, Invoice2go, which obtained $35 million in funding
from Silicon Valley Venture (Lim, 2014). Another example is TransferWise (a London-based online money
transfer service), which obtained $50 million in start-up funding from Sequoia, another Silicon Valley Venture
Capital fund (Davies, 2014). Accordingly, topics ‘Silicon Valley’, ‘Start-up company’ and ‘London’ burst in
the practitioner literature, reporting on the start-up successes of these and other companies at the time.
However, while the market for mobile phone- and online-based financial services was on the rise, the
practitioner-oriented literature provided few insights into the types of IS resources or capabilities behind
FinTech innovations or startups, likely because of the sensitive nature of the information and associated
difficulties with identifying and disclosing relevant information to the market.
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Figure 2: Timeline of ‘Bursting’ Topics in the Practitioner and Academic Literature
Note: This figure shows topic bursts across the academic literature (red) and the practitioner literature (blue). Topics have been
grouped under themes, or subheadings, to facilitate the subsequent discussion.
Rising Awareness of FinTech
Algorithm-Based Trading
FinTech Start-Ups and Growing Academic Interest
FinTech Innovations
Alternative Finance
Cryptocurrency (Blockchain)
Machine-Based Learning Methods for Financial Analysis and Forecasting
FinTech Risk and Regulatory Issues
Emerging Research Themes
Financial Inclusion
Impact of COVID-19
New business models
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In contrast to the significant increase in news about successful FinTech start-ups reported in practitioner
journals at the time, early FinTech academic studies were scarce and mostly offered conceptual discussions
or theoretical models of the impact of FinTech on financial structures and regulatory developments (e.g.,
Betz, 2014; Ostrovska, 2014; Tapiero, 2014), rather than empirical data. The emerging literature almost
exclusively focused on the field of finance, with academic researchers initially exploring how financial
technologies might change and/or improve the financial system following the 2007-2008 global financial
crisis. For instance, Tapiero (2014) investigated how the growth of FinTech amplified opportunities for profit,
as well as challenging regulators and regulation after the 2007-2008 global financial crisis. Others (e.g.,
Agrawal et al., 2014; Betz, 2014) offered conceptual models and frameworks to explore how FinTech would
alter the financial stability of a layered financial system and attempted to explain the successes and
outcomes of equity-based crowdfunding. Some researchers focused on legal aspects; for instance,
Kaplanov (2012) argued it would be ineffective to introduce any regulation of Bitcoin use.
While research interest in FinTech began to slowly increase, research with a specific focus on FinTech was
largely absent in IS research. Kauffman and Ma (2015) eventually curated a Virtual Special Issue on
“Contemporary research on payments and cards in the global fintech revolution”. The editors noted that
much of the research in the Special Issue was grounded in existing (rather than revolutionary) IS research
on mobile and Internet banking, as well as on consumer behavior towards cash, credit and debit cards, but
already pointed to the future importance of multi-sided business technology and market platforms that
support payment services, economic changes and stakeholder relationships in the era of FinTech.
Subsequently, market platforms beyond traditional e-commerce models (e.g., peer-to-peer lending) have
indeed received significant attention in recent years in both practice and research (see section on Alternative
Finance below). Going forward, this area provides significant opportunities for IS research to broaden
existing research on digital platforms (Constantinides et al., 2018; Rai et al., 2019) and contribute valuable
insights into issues such as the emergence, adoption, and formalization of these new FinTech platforms
and platform ecosystems. Areas that are also still under-researched include issues such as privacy and
security concerns associated with platform use and operation.
4.2 FinTech Innovations
The second wave of literature emerged shortly after the initial shift of attention towards FinTech, discussing
digital innovations and technology-enabled business model innovations in the financial sector. Among
various innovations, the practitioner-oriented literature showed particular interest in alternative finance
(including peer-to-peer loans). In contrast, the academic literature showed a strong focus on the role of
equity crowdfunding and machine-based methods for financial analysis and forecasting. During this time,
some initial papers on crowdfunding also appeared in IS journals (e.g., Gleasure, 2015; Kang et al., 2017;
Lukkarinen et al., 2016), but these mainly discussed the drivers and barriers behind the adoption of
crowdfunding campaigns. The area of blockchain and cryptocurrencies received strong interest from both
practitioners and academics. In the following, we illustrate the coverage of these areas in the practitioner
and academic literature.
Alternative Finance: The rise of peer-to-peer (P2P) lending is a topic that was initially mostly covered in
the practitioner literature. Given that P2P operates as an intermediary between lender and borrower, the
practitioner literature largely focused on lending- and investment-related topics. P2P platforms first emerged
in Europe (see Zopa, 2020) and the U.S. (see LendingClub, 2020) before a rapid rise in P2P took place in
Southeast Asia and China. Starting in 2015, the rapid growth of LendingClub and its successful IPO in the
U.S., together with the announcement that Zopa planned to apply for banking licenses, led to a burst of the
topics ‘LendingClub’, ‘Peer-to-Peer’, and ‘Loan’ in the practitioner literature. (e.g., Wack, 2015; Column,
2016; Colchester, 2016). Practitioner articles also provided early signs that the attitude of the financial
profession towards FinTech had changed, as fund managers started to debut online lending platforms (e.g.,
Dunkley, 2016; Rudegeair, 2016). Academic publications on alternative finance emerged from 2010
onwards, attempting to define crowdfunding and P2P phenomena (e.g., Lambert & Schwienbacher, 2010;
Schwienbacher & Larralde, 2012; Mollick, 2014). The number of these publications was initially not
substantial enough to lead to any topic bursts. However, the wide discussion of peer-to-peer lending
platforms in the practitioner literature eventually prompted researchers to examine alternative finance
models further.
Researchers empirically investigated crowdfunding phenomena from about 2015 onwards (and leading up
to the topic burst of ‘Crowdfunding’ in 2018). While the practitioner literature focused on successful, but at
times controversial, crowdfunding start-ups such as ‘LendingClub’, academic articles (often with a focus on
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entrepreneurship) aimed to explain (1) how to ensure the success of online funding campaigns (e.g., Ahlers
et al., 2015; Colombo et al., 2015; Ge et al., 2016) and (2) the behaviors of funders (e.g., Burtch et al., 2016;
Jiang et al., 2018; Xu & Chau, 2018). While there are few studies at the intersection of FinTech and IS, a
prior review of the IS literature identified a total of 49 studies on crowdfunding up to 2020, making
crowdfunding one of the larger topics of IS research on FinTech (Lagna & Ravishankar, 2021). However,
the focus of most studies remained on identifying the drivers and barriers behind the adoption of
crowdfunding campaigns. For example, we find research on the success factors of crowdfunding platforms
in the United States (Mamonov & Malaga, 2018) and the effects that the usage of provision-point
mechanisms have on website visitor conversion and contribution decisions (Burtch et al., 2018).
Given the ongoing evolution of alternative finance models and the emergence of new models such as
Banking as a Service (BaaS) (i.e., the integration of digital banking services into products and services
offered by non-bank businesses), there are opportunities for IS research to contribute further insights
beyond P2P lending and crowdfunding. The accessibility of alternative forms of finance also has implications
for financial inclusion. We discuss these points in the section on Emerging Research (below).
Cryptocurrency and Blockchain: After the emergence of Bitcoin (Nakamoto, 2008), blockchain
applications saw a rapid uptake and substantial discussion in the literature: both the industry and the
academic community began to explore blockchain applications in three main finance areas:
cryptocurrencies (e.g., Berentsen & Schaer, 2018; Luther, 2016; Massey et al., 2017); payment systems
(clearing and settlement) (e.g., Brainard, 2016; Higgins, 2016; Chanjaroen & Roman, 2016; Ripple, 2017);
and asset-backed securities (e.g., Deloitte, 2017; Tanaya, 2016). Due to Bitcoin’s open-source code, other
cryptocurrencies started to emerge in 2011. In 2014, another blockchain platform (Ethereum) was launched,
bringing the concept of ‘smart contracts’ to public attention (Buterin, 2014). Smart contracts further
enhanced the power of blockchain and, from 2014 onwards, applications on blockchain started to be widely
explored across different industries.
Between 2016-2017, the practitioner literature heavily discussed the announcement that the U.S. Office of
the Comptroller of the Currency (OCC)
4
would consider issuing licenses to FinTech companies as banks,
leading to a corresponding topic burst (e.g., Clozel, 2017; Witkowski et al., 2016). The cryptocurrency
market was subject to much speculative trading behavior at the time, which resulted in a dramatic surge
and subsequent collapse of the market in 2018. The collapse of the “crypto bubble” led to a burst of the
topic ‘cryptocurrencies’ among the practitioner articles at that time. Practitioners heavily discussed the price
volatilities of cryptocurrencies, associated legal and regulatory issues like investors’ identities, and whether
cryptocurrency transactions should be treated as ‘illegal’ (e.g., Cornish & Murphy, 2018; Hadi, 2018; Jeong
& Russolillo, 2018; Reiners, 2018; Thoms, 2018; Vigna, 2018). Despite the crash (Petersson, 2018), interest
in specific cryptocurrencies received a further boost in 2019 when the social network giant Facebook
introduced Libra, unveiling an ambitious vision of a decentralized, autonomous organization to oversee the
creation of a borderless, easy-to-transfer means of exchange for its nearly 2.5 billion users (one-third of the
world’s population). Facebook’s project was widely reported and discussed in the practitioner literature,
leading to the topic burst, ‘Facebook’ among articles published by practitioners (e.g., Adams, 2019; Cook &
Field, 2019; Isaac & Popper, 2019; Shalal, 2019; Werschkul, 2021).
5
The substantial discussion of Libra revolved around the feasibility of the idea of introducing a global payment
system beyond the control of central banks, prompting central banks to contemplate the introduction of a
central bank digital currency (CBDC). The U.S. Federal Government set up an enquiry into whether to have
a CBDC, with the concern that a decentralized digital currency may ‘compete’ with fiat currencies. Several
central banks started to explore the possibility of introducing a CBDC to improve financial inclusion and
replace physical cash (e.g., BIS, 2020), with the Bahamas recently introducing the “Sand Dollar” as the first
nationwide CBDC (Bharathan, 2020). Most of the existing literature in the field of CBDC consists of
discussion papers initiated by central banks, financial institutions, and government agencies. The focuses
of these papers are: (i) the motivations and possible benefits of a CBDC (e.g., Engert and Fung, 2017;
Meaning et al., 2018); (ii) the risks that the adoption of a CBDC brings to the financial system (e.g., BIS,
2018); (iii) the design and implementation issues (e.g., Barontini and Holden, 2019; Cukierman, 2019). The
academic community has not yet paid adequate attention to the adoption of CBDCs (and relevant
4
The OCC is an independent bureau within the United States Department of the Treasury.
5
Facebook initially considered a cryptocurrency backed by a basket of national currencies but has subsequently considered the
introduction of various stablecoins (i.e., a cryptocurrency pegged to a national currency). The company now wants to introduce the
still-pending stablecoin “Diem” alongside a digital wallet “Novi”.
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implications for IS research). For example, it is unclear if CBDCs and intended solutions such as social
assistance payments and government-to-person payments to mobile wallets will really improve financial
inclusion (CGAP, 2021). As payment technologies continue to pave the path towards a cashless society,
there are significant opportunities for IS research to investigate this cashless transformation in relation to
impacts on consumers, the acceptance of new payment methods and the development of business models,
but also in relation to financial inclusion (further discussed below), governance, changes in regulations, and
impacts on the banking sector (e.g., Bjerg, 2017; Andolfatto, 2021).
In parallel to the practitioner literature, academic research on blockchain finance emerged around 2016 and
then burst in 2017, corresponding with the topic bursts, ‘Bitcoin’ and ‘Authentication’ as an associated topic.
The early focus of blockchain research was to expound the main principles of this technology, to
conceptualize possible blockchain innovations (including blockchain-enabled cryptocurrencies such as
Bitcoin), and to explore how it might reshape the financial industry (e.g., Cai, 2018; Philippon, 2016; Fanning
& Centers, 2016; Scott et al., 2017, Li & Wang, 2017), along with online authentication issues (e.g., Kang &
Lee, 2016; Park & Park, 2017). IS researchers have commented on the lack of rigorous empirical and theory-
driven IS blockchain research (see Rossi et al., 2019, for a review) and suggested that IS research needs
a more comprehensive and multi-paradigmatic research agenda, “that underscores the need for behavioral
(individual, group, and organizational), design science, and IS economics research on blockchain” (Rossi
et al., 2019, pg. 1390).
Going forward, blockchain has significant potential beyond its application in blockchain finance, for instance,
in terms of improving the efficiency of information systems (e.g., to ensure the authenticity of all parties
before a transaction is made), potentially improve fault tolerance (e.g., use of decentralization to avoid loss
of records) and help manage data validation and transaction integrity, especially across supply chains
(Berdik et al., 2021). These areas offer opportunities for IS research to address many unresolved
challenges, especially on the application level. For instance, there is yet limited research that answers
questions such as how the adoption of blockchain impacts the cost of doing business and leads to any
economic changes (Rossi et al., 2019). While there are many claims that blockchain eliminates the need
for intermediaries and thus improves the cost of doing business, these assumptions have not yet been
tested empirically across different settings (Rossi et al., 2019). Issues arise from the implementation and
integration of blockchain within existing systems, likely requiring new skills and capabilities. There is also
limited research on the advantages and drawbacks of the application of blockchain, as well as the integration
with existing management and accounting information systems. In addition, little IS research has examined
other types of digital assets such as tokens (e.g., utility tokens that can be exchanged to access products
or resources; security tokens that grant rights like ownership, or asset-backed tokens that are pegged to
assets such as gold or commodities) and implications for IS-related areas such as their adoption and
acceptance (EY, 2021). We identify opportunities for future research in Table 2.
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Table 2: Possible avenues for future research on Blockchain and IS
Area
Possible Research Questions
Reference of Further Reading
Value creation
How can organizations capitalize on blockchain and create
value for their customers?
Zheng & Boh, 2021
Does blockchain help build distinctive value to organizations
and improve their performance and competitive advantage?
Zheng & Boh, 2021
How can blockchain solutions be best implemented to contribute
to business outcomes (e.g., value creation, efficiency gains)?
Berdik et al., 2021
What metrics or approaches can be used to measure the
effectiveness of blockchain applications?
Berdik et al., 2021
Wu et al., 2017
Blockchain
integration
What are the barriers to adoption that organizations face with
regards to the adoption and use of blockchain?
Berdik et al., 2021
How can database-driven architecture be replaced by
blockchain, and what are the implications for customers,
businesses, and third-party agents?
Berdik et al., 2021
How can the interoperability of currently dispersed systems and
databases be improved through the implementation of
blockchain?
Berdik et al., 2021
How can blockchain-based scalability issues be best
addressed?
Gopalan et al., 2020
What is the role of IT departments in the integration of
blockchain?
Queiroz et al., 2019
How can blockchain be made more accessible to the layman,
as opposed to an expert?
Berdik et al., 2021
Validation of
data and
transactions
How can blockchain be used to ensure the integrity and validity
of transactions and data?
Secinaro et al., 2021
How can blockchain be implemented across supply chain
systems to help trace the provenance of goods and enforce
contract integrity?
Berdik et al., 2021
How can IS support applications such as the use of blockchain
in national elections, voting or securing access to cloud-based
systems?
Berdik et al., 2021
Machine-Based Learning Methods for Financial Analysis and Forecasting: A topic that received
substantial attention in the academic literature but not in the practitioner-oriented literature is the role of
machine-based learning methods for financial analysis and forecasting. This is perhaps not surprising, given
that variations of machine learning algorithms have a long history in the finance field, with well-established
research on modelling financial system mechanisms, financial market analysis and forecasting and
investment optimization. Some well-known examples are sentiment analysis of news, trend analysis,
customer segmentation via unsupervised learning algorithms, risk modelling and portfolio optimization (e.g.,
Henry & Pearson, 1995; Khandani et al., 2010; Mousa, 2016; Ponsich et al., 2013). Together, the increasing
computing power, the automation of traditional tasks and services, and the availability of a vast amount of
structured and unstructured data led to a rise in machine-based analytical methods. Besides typical
statistical modelling (Remillard, 2013), various machine learning and knowledge discovery methods, such
as network modelling and graph theory, have also emerged (e.g., Athey, 2018; Hexmoor, 2015).
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Meanwhile, the rapid adoption of programming languages such as R and Python and machine-learning-
focused frameworks, such as TensorFlow, further pushed the broader adoption of Artificial Intelligence (AI)
methods. Although the term ‘AI’ does not appear directly in our results, the topic is represented by the
themes ‘financial innovation’, ‘institution’, ‘computer’, ‘financialization’, ‘information system’ and ‘informatics’.
Cao (2020) conducted a comprehensive review of the application of AI, concluding that: “the communities
of economics and finance are increasingly interested in applying AI in Finance and FinTech” (Cao, 2020:
30). Advanced AI techniques have increasingly been used to research financial markets; for instance, in the
areas of market complexities and dynamics (e.g., Cao, 2015; Cao, 2018; Hexmoor, 2015); market analysis
(e.g., Albashrawi, 2016; Nassirtoussi et al., 2014; Namini et al., 2019; Xu & Cohen, 2018); trading
optimization (e.g., Hu et al., 2015; García-Galicia et al., 2019; Ponsich et al. 2013;); and market anomalies
(e.g., Chen & Tsang, 2018; Da et al., 2014; Harvey et al., 2016; Neely et al., 2014). The development of
deep learning has enabled finance researchers to examine various topics, including news sentiment
analysis (Day & Lee, 2016); investment portfolio management (e.g., Heaton et al., 2017; García-Galicia et
al., 2019); or financial market movements (e.g., Cao et al., 2015; Cao et al., 2019).
While much of the literature has focused on applications in finance, AI applications are now transforming
business models across many sectors, including insurance and real estate. This provides opportunities for
IS research to contribute to understanding the organizational and inter-organizational implications of the
digital transformation of existing business models. For instance, insurance technology (InsurTech) has
emerged as an area of research, corresponding to the bursting topic of ‘insurance’ in the academic literature
in 2018. IS research can build on many emerging research questions, including the question of how AI
techniques such as classification, interaction learning, and behavioral analysis can contribute to broad
aspects of insurance: better pricing models (e.g., Kazutoshi et al., 2019); more customized insurance
policies (e.g., Creighton, 2016); and improved fraud detection and claims processes (e.g., Albashrawi, 2016;
Son et al., 2016). Alongside InsurTech, research on RegTech (see next section) and PropTech (i.e.,
business models using technology to disrupt the purchasing, construction and management of residential
and commercial properties) is also growing (e.g., Baldominos et al., 2018; Paefgen et al., 2014; Viriato,
2019; Zhang & Zhang, 2019). We provide a detailed research agenda at the intersection of Alternative
Finance and AI in the section on “New Business Models” (below).
4.3 Risks and Regulatory Issues
Between 2016-2017, regulatory issues and customer protection of FinTech innovations became the focus
for both academic and practitioner articles, corresponding to the bursting topics of ‘regulation’ (in the
academic literature), as well as topics such as ‘financial regulation’ (in the practitioner literature). The
heightened attention to regulation resulted from the Federal Bureau of Investigation (FBI) investigation into
the dark web and the online marketplace, Silk Road. The marketplace enabled the purchase of illicit goods
and services via Bitcoin transactions, allowing buyers and sellers to conduct a secure and anonymous
money transfer that bypassed traditional financial markets (Adler, 2018). In addition, FinTech companies
started to increasingly (and legally) offer niche services by disintermediation that circumvented existing
banks (Nicoletti, 2017). Many banks acknowledged threats arising from the growth of FinTech start-ups (see
reports by KPMG, 2017, 2018), but also raised concerns that much less rigorous standards are applied to
disruptors (e.g., Bunea et al., 2016; Demos, 2016). Consequently, the possible introduction of FinTech
regulations has been widely debated amongst practitioners (e.g., Ackerman, 2016; Popper, 2016). The
practitioner literature started to issue warnings about initial coin offerings (ICOs) (Don, 2017); and reported
on enforcement action by the Consumer Financial Protection Bureau to rectify data security issues
experienced by FinTech start-ups (Hayashi, 2016), as well as issues around illegal fees charged by online
lenders (Koren, 2016).
Other bursting topics in the practitioner literature were the ‘Dodd-Frank Wall Street Reform and Consumer
Protection Act’ alongside ‘U.S. Securities and Exchange Commission’. The Act was initially introduced in
the aftermath of the 2007-2008 global financial crisis as a legislative reform package by the Obama
administration (see above). Under the Act, FinTech companies, such as P2P lending platforms must adhere
to the same legal framework as large, established firms. However, this is a debatable topic for FinTech start-
ups, as the Dodd-Frank Wall Street legislative framework is primarily built on the “too big to fail” mindset: to
prevent giant traditional financial institutions from systematically abusing their powers in the financial market.
Therefore, this Act does not fit with small FinTech start-ups that focus on narrow segments of the financial
market (Magnuson, 2018). The topic burst in our sample after Donald Trump was elected as U.S. President
in 2016 and attempted to dismantle the Dodd-Frank Act, arguing that this would lift burdens unnecessarily
put on small and medium-sized lenders. Consequently, attempts to roll back Dodd-Frank were of high
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interest to FinTech practitioners in the hopes that FinTech compliance would be exempt from the act and
possibly other financial services regulations (e.g., Flitter & Holland, 2016; Thrush, 2017).
Around 2016, driven by the urgency of regulations, finance and legal researchers began to discuss FinTech
regulatory issues and the risks associated with a FinTech revolution (e.g., Arner et al., 2016; Buchak et al.,
2018; Magnuson, 2018; Treleaven, 2015). The IS literature has started to acknowledge the importance of
examining regulatory issues; for instance, a literature review carried out by Milian et al. (2019) identified
regulation as the main topic related to FinTech. Examples of IS research on regulation include work from
Mamonov & Malaga (2018), who explore regulation surrounding equity crowdfunding. However, while there
is recognition of the importance of regulation, recent literature has suggested that protocol issues (in
particular, privacy, security, and scalability) are perhaps of more immediate concern for future research
(Rossi et al. 2019). Nonetheless, reports of data security issues at Apple lnc. and the collapse of Wirecard,
one of Europe’s fastest-growing FinTech payment companies (both bursting in the practitioner literature),
highlight the need to further research risks brought about by FinTech innovations (e.g., Tan, 2020; CNBC,
2020). Different jurisdictions have started to implement different regulatory measures that will directly impact
the future of FinTech, including China’s ban on cryptocurrency (Sutherlin, 2021), the European
Commission's Regulation of Markets in Crypto-assets (MiCA) (Cenguiz, 2021) as well as a growing amount
of regulatory measures in the U.S. (Sorkin et al., 2021). Different regulatory regimes provide the opportunity
to investigate their impact on the intersection of FinTech and IS over time.
Beyond the focus on regulatory changes, there is also an opportunity for IS research to further research the
implications of Regulatory Technologies (RegTech). RegTech focuses on adopting new technologies to
facilitate the delivery of regulatory requirements (FSB, 2017). The cost of regulatory compliance has been
a primary driver for RegTech adoptions among organizations and financial institutions (Butler & O’Brien,
2018); for instance, RegTech can help with translating government rules into public APIs (Nott, 2017). So
far, the academic literature has mainly examined RegTech at a conceptual level (e.g., Arner et al., 2017;
2016) with a few articles starting to investigate challenges related to the update of RegTech: the possibility
that RegTech might lead to unethical business practices and hinder good judgment and human input in risk
and governance decision processes (e.g., Packin, 2018). Some interesting work in IS has started to emerge
that examines the deployment of investment management systems in the context of RegTech in the UK
financial industry (see Currie et al., 2018), suggesting that substantial challenges with the deployment of
such systems relate to transparency, surveillance, and accountability. We summarize avenues for future
research in Table 3 below.
Table 3: Possible avenues for future research on Risks, Regulations and IS
Area
Possible Research Questions
Reference of Further Reading
Risks and
Regulations
How can information systems help with regulatory approaches
to privacy?
Lowry et al., 2017
How can Fintech systems be integrated into accounting and
auditing activities to reduce business risks and improve
compliance with regulations?
Cai, 2021; Secinaro et al., 2021
What impact do Fintech Systems have on information security
management, risk management, and governance?
Lowry et al., 2017
RegTech
What is the role of information systems in reconfiguring
compliance with federal, state and local government rules and
regulations?
Butler & O’Brien, 2018
What are the impacts of RegTech on transparency, surveillance,
and accountability?
Currie et al., 2018
4.4 Other Emerging Research Areas
Our review has shown that there has certainly been an overall increase of FinTech-related IS publications
in recent years; however, IS research has overall had somewhat limited engagement with the topic of
FinTech, which provides substantial opportunities for future research. Here, we reflect on recent
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contributions in the literature that offer additional new avenues for research, including: (1) academic work
on financial inclusion and the broader social impacts of FinTech (see also Lagna & Ravishankar, 2021); (2)
practitioner-led work observing the impacts of COVID-19 on digitization and the adoption of FinTech, and
(3) academic work studying the impacts of new business models for managing debt and investments. We
summarize a range of possible research questions below, stimulating further research in these emerging
areas.
Financial Inclusion: Successful cases of mobile payment systems in developing countries, such as M-
Pesa in Kenya, Fino PayTech in India, as well as WeChat and AliPay Payments in China (leading to the
topic burst of ‘Ant Financial’ which owns AliPay), have prompted researchers to explore how FinTech might
help to solve broader societal issues, such as poverty in developing regions. Accordingly, a new research
area emerged in academic literature, corresponding to bursting topics like ‘China’, ‘Poverty’ and ‘Financial
Inclusion’. Financial inclusion refers to households and businesses having availability and equality of
opportunities to access financial services (Demirgüç-Kunt, Klapper, Singer, Ansar, & Hess, 2018). Recent
publications on this topic have examined:
I. the contribution of FinTech and initiatives such as the implementation of CBDCs to increasing
financial inclusion (e.g., DNDJ, 2018; Gabor & Brooks, 2016; Salampasis & Mention, 2018; BIS,
2020; Mardiana et al., 2020);
II. how FinTech innovations, especially mobile money, might help to address the Sustainable
Development Goals (SDGs), including poverty reduction (e.g., Fall et al., 2020; Ozili, 2020; Schuetz
& Ventkatesh, 2020), gender inequality (e.g., Gupta & Arya, 2020; Tavneet & Jace, 2020), and
healthcare utilization (e.g., Egami & Matsumoto, 2020);
III. how FinTech can help with financing entrepreneurs and small and medium-sized enterprises
(SMEs) (e.g., Lam & Liu, 2020);
IV. how FinTech can be aligned with alternative financial systems, such as Islamic Finance, to improve
financial inclusion, especially among the underbanked and unbanked population (e.g., Baber, 2019;
Mohamed & Ali, 2021).
A recent publication by Lagna & Ravishankar (2021) points out the limited engagement of IS research with
FinTech’s promise of fostering financial inclusion and offers a substantial research agenda to further IS
research on this topic. Prior research in IS on financial inclusion has been scarce, with only few publications
on the topic (see, e.g., the study by Leong et al. (2017), who investigated the positive impacts of a microloan
start-up in China on financial inclusion). Lagna & Ravishankar (2021) propose that further IS research is
needed to provide answers to several important questions; for instance, if FinTech initiatives have positive
impacts on poverty reduction in developing countries over time, and if firms can foster the individual and
collective capabilities of poor people and improve their social equality. To date, little is known regarding how
vulnerable individuals or minorities in developed countries might be reached via FinTech innovations
(Caplan et al., 2020). For example, how can IS contribute to designing or implementing access options that
make it more conducive to use FinTech applications in developing countries? So far, only few countries
such as China, Norway or Sweden have achieved a level of digitization that would allow a widespread
adoption of FinTech and digital payment solutions (CGAP, 2021). Furthermore, what levels of digital and
financial literacy are required to access FinTech, and how can digital and/or financial literacy be improved
in culturally appropriate ways?
Moreover, there is a considerable gap in investigating the barriers to the adoption of FinTech for vulnerable
people, with more research needed that provides theoretical and empirical insights into how adoption can
be fostered. However, researchers have cautioned against the perhaps too optimistic view that FinTech can
offer a quick solution for financial inclusion or poverty reduction and have argued that more critical research
is needed that also considers potential adverse outcomes or even a “dark side” of FinTech adoption. For
example, there are concerns that the adoption of FinTech might “intrude [on] the lives of poor people” (Lagna
& Ravishankar, 2021: 13), and that FinTech mechanisms, such as the application of algorithms to assess
creditworthiness, might have adverse impacts on empowerment and agency. These concerns are also
reflected in the trending topic ‘information asymmetry’, which essentially addresses whether FinTech can
truly match the needs of borrowers and lenders. We believe that these are important questions for future
research and can see an opportunity for IS research to also consider ethical questions around the societal
impacts of FinTech and the role of IS in this context.
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Table 4: Possible avenues for future research on financial inclusion
Area
Research Question
Source of Further Reading
Financial
Inclusion
To what extent do Fintech initiatives have positive or negative
impacts on financial inclusion?
Schuetz and Venkatesh., 2020
Can FinTech foster social equality?
Lagna & Ravishankar, 2021
What are positive and negative societal impacts associated with
the implementation of FinTech?
Lagna & Ravishankar, 2021
Can the adoption of a CBDC improve financial inclusion?
BIS, 2018
Sustainable
Development
Goals (SDGs)
To what extent do Fintech initiatives positively or negatively
impact poverty reduction, gender equality and/or healthcare
utilization in developing countries over time?
Lagna & Ravishankar. 2021
SME financing
How can FinTech support the financing of microenterprises,
entrepreneurial projects, or SMEs?
Lam & Liu, 2020
Alternative
financial
systems
How can FinTech be aligned with alternative financial systems,
such as Islamic finance?
Leidner & Kayworth, 2006
What cultural concepts must be considered when introducing
FinTech across different countries or regions?
Leidner & Kayworth, 2006
Ethics and
information
asymmetry
What are ethical considerations associated with the
implementation of FinTech across different parts of society?
Lagna & Ravishankar 2021
How can fintech help manage information asymmetry between
decision-makers and users’ preferences for privacy and
anonymity?
Liu et al., 2017
Which policies or controls can reduce information asymmetry
and increase transparency? How can we evaluate the
effectiveness of such measures?
Liu et al., 2017
New Business Models: A stream of recent research is further extending the areas of Alternative Finance
and AI, examining new applications in FinTech but also InsurTech, and PropTech (see above), ‘proactive’
credit scoring (Carta et al., 2020), FinTech financing (Mutamimah, 2020), the provision of automated
financial and investment advice (Brenner & Meyell, 2020), and the automated pricing of financial products
(Gan et al., 2020). The new research stream leads to a burst of the topics ‘debt’ and ‘investment’. There are
multiple avenues for IS research to contribute to this emerging research field: For instance, IS research can
provide valuable insights into the role of information systems in reconfiguring BaaS value chains, and in
enabling third-party distributors (e.g., fintech companies, non-banking businesses) to offer alternative
banking products and services via application programming interfaces (APIs) (Deloitte Digital, 2021).
6
In
addition, IS research can also generate insights into how the integration of banking and non-banking
information systems into new BaaS ecosystems can be successfully managed. The integration of these
systems will not be without challenges, as banking systems are typically regularized, structured and rigid,
while FinTech systems are likely characterized by flexibility and rapid innovation (Nicoletti, 2021).
There are substantial ethical considerations associated with the increasing digitization of services such as
banking, including issues around privacy and the misuse of information, the profiling of individuals based
on online data and AI applications (e.g., in the context of robo-advisors), as well as cybersecurity breaches
and cyber theft. The use of automated decision-support systems has been particularly controversial. For
6
An example is Lending Club the firm decided to acquire Radius Bank and restructure from a P2P lending platform to a neobank
(internet-only bank). It allows small business owners to open accounts within minutes together with debit card services as well as
payment acceptance capabilities (Deloitte Digital, 2021).
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instance, several recent articles (e.g., Brenner & Meyll, 2020) find advantages associated with the use of
robo-advisors, such as immediate availability, lower fees, and a lower potential for conflicts of interest. Still,
many ethical questions remain: What is the extent of customers’ knowledge and comprehension of the
service offered? What are the implications of automatically profiling customers? Are the services provided
consistent with the risk preferences of the customer? (Shanmuganathan, 2020) The use of automated
systems for purposes of automated payments and debt assessment is not just pursued by private entities
but also by governments and evidence is pointing to unfavourable outcomes. A case in point is Australia’s
Robodebt scheme, an automated data assessment and matching process to issue debt recovery notices to
people who might have received welfare overpayments. The system relied on averaged data (i.e., not taking
into consideration that recipient’s income might have been highly variable) which led to a substantial number
of errors and caused substantial distress amongst already vulnerable people who received debt-recovery
notices (Meers et al., 2017; Huggins, 2019). We provide related research questions in Table 5 below.
Table 5: Possible avenues for future research on business models
Area
Research Question
Source of Further Reading
New systems
for value
generation
What is the role of information systems in reconfiguring BaaS
value chains?
Lanteri et al., 2021
How can the integration of traditional and FinTech-enabled
systems be facilitated?
Lee et al., 2018
Privacy and
security
How can issues around privacy and the misuse of information,
the profiling of individuals based on online data and AI
applications (e.g., in the context of robo-advisors), as well as
cybersecurity breaches and cyber theft be managed?
Awad & Krishnan, 2006
Ethics
Which ethical principles should be enacted when implementing
new FinTech business models?
Omarova, 2020
What ethical considerations are associated with automated
decision-support systems, and how can possible negative
impacts be mitigated?
Cummings, 2006
How can identity management be improved with the usage of
FinTech?
Omarova, 2020
Impact of COVID-19: Concerns about the impacts of COVID-19 have seen a substantial discussion in the
practitioner literature, highlighting both the substantial industry impacts caused by the pandemic, but also
the need for many individuals, businesses and governments to shift towards a rapid ‘digitalization’ and
adoption of new technologies. The introduction of social distancing measures and stay-at-home orders
meant a shift towards remote work, limited in-person interactions, and a substantial increase in online
transactions (e.g., online shopping, online services such as telehealth). COVID-19 has made ‘digital-only’
the new norm for many transactions. However, COVID-19-related impacts have led to additional questions
around the implementation of digital solutions; for instance, traditional banks faced pressures to move to
digital platforms (e.g., Burton, 2020; Alix, 2020), but these were not available to or accessible by all
customers, especially the elderly and those without digital access. While academic research at the
intersection of IS, FinTech and the impacts of COVID-19 is still sparse, we expect to see an increase in
studies over the next few years of research that is currently in progress. We also offer some possible
avenues for research in Table 6 below.
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Table 6: Possible avenues for future research on the impacts of COVID-19
Area
Research Question
Source or Further Reading
COVID-19
impacts
What impacts has COVID-19 had on the adoption of FinTech
and what are implications for IS-related areas (e.g.,
information-related behavior, cybersecurity, data privacy)?
Davison, 2020
How have FinTech applications helped support customers
and businesses during COVID-19, for instance, financial
management, lending and borrowing, or accessing
government payments? How has this impacted digital
behaviors?
Ågerfalk et al., 2020
How did COVID-19 impact issues such as the inability to carry
out in-person due diligence, and how can FinTech
applications help overcome these challenges?
Accenture, 2021
What were notable FinTech innovations because of COVID-
19, and how did they impact customers, firms, or other parts
of society?
Akpan et al., 2020
Ethical
considerations
What impact did the wider utilization of contactless
commerce” FinTech tools have on marginalized sections of
the population?
Pan & Zhang, 2020
What are the ethical implications of the digital transformation
in areas such as digital ID, data ownership and data privacy?
Accenture, 2021
5 Conclusion
This paper has offered a systematic review on FinTech to determine the existing scope of the literature, as
well as key research trends and future research directions. The review included all available academic
publications and practitioner-oriented (or “grey”) literature (i.e., outlets such as CIO Magazine,
Computerworld, and Forbes) on the topic of FinTech. Findings from our review show that the practitioner-
oriented literature foreshadowed the rise of FinTech by extensively reporting on algorithm-based and
electronic trading (2009 onwards), followed by reporting on FinTech start-ups and funding successes (2014
onwards). The practitioner literature subsequently reported on alternative finance models, the introduction
of cryptocurrencies, as well as risks and regulatory issues. Academic literature on FinTech began to rise
from 2014 onwards, focusing initially on the development of FinTech in the aftermath of the 2007-2008
global financial crisis. Research attention subsequently shifted to FinTech innovations (alternative finance,
cryptocurrency and blockchain, machine-based methods for financial analysis and forecasting, including
AI), as well as risk and regulatory issues. IS work on FinTech (initially focusing on mobile payment systems
and peer-to-peer lending) started to emerge from 2015 onwards, but the body of work at the intersection of
FinTech and IS is still small. Our review sheds light on several opportunities for future research, including
financial inclusion, the impacts arising from COVID-19, and the emergence of new business models, such
as Banking as a Service (BaaS).
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Appendix A
Table A1: Top 30 ‘Bursting’ Topics in the Practitioner Literature
Topic
Burst Start
Burst Finished
Weight
U.S. Securities and Exchange Commission1)
2011
2012
10.36
China1)
2011
2012
11.54
Audit1)
2011
2013
9.98
India2)
2008
2013
33.19
Commodity2)
2008
2013
16.02
Trade2)
2010
2013
11.02
Multi-Commodity Exchange2)
2008
2013
10.90
Fraud
2011
2014
9.97
Derivative (finance)
2009
2014
11.30
National Spot Exchange2)
2013
2014
10.55
Equity (finance)3)
2010
2014
10.25
London
2014
2015
19.00
Silicon Valley
2014
2015
11.29
Start-up company
2015
2015
13.90
Peer-to-peer
2015
2016
10.91
Online banking
2016
2016
10.99
LendingClub
2016
2016
16.74
Loan
2016
2016
10.41
Dodd-Frank Wall Street Reform and Consumer Protection Act
2016
2017
10.14
U.S. Securities and Exchange Commission
2017
2017
12.72
Federal Reserve System
2017
2017
12.04
Office of the Comptroller of the Currency
2016
2017
11.44
Consumer Financial Protection Bureau
2017
2017
10.49
Financial regulation
2016
2017
63.06
Cryptocurrency
2018
2018
16.82
Ant Financial Services Group
2018
2018
11.13
Facebook
2019
2019
21.50
Apple Inc.
2019
2019
10.65
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Wirecard
2019
2020
38.56
Coronavirus
2020
2020
55.49
Pandemic
2020
2020
26.53
1) When examining the results, note that some topics burst because relevant articles mention companies that have the term “financial
technology” as part of their company name. The topic ‘China’ relates to a financial report scandal of a Chinese company. The
company’s name is Longtop Financial Technologies. This led to bursts in the topics ‘U.S. Securities and Exchange Commission’,
‘China’ and ‘Audit’.
2) Spot Contracts on the National Spot Exchange of India’s commodities exchange market were suspended for some time. This
exchange market was owned by a company called Financial Technologies (India) Ltd. This led to associated bursts in the topics ‘India’,
‘commodity’, ‘Multi-Commodity Exchange’, ‘National Spot Exchange’, ‘Trade’.
3) The topic ‘Equity_(Finance)’ relates to two companies called Fintech and Financial Technologies (India) Ltd.
Table A2: Top 30 ‘Bursting’ Topics in the Academic Literature
Topic
Burst Start
Burst Finished
Weight
Investment
2013
2013
1.81
Financial crisis of 2007-08
2014
2014
1.95
Security
2015
2015
1.80
Economy
2013
2015
4.37
China
2016
2016
2.50
Regulation
2016
2016
2.23
Bitcoin
2017
2017
1.81
Cloud computing
2017
2017
2.40
Financial innovation
2016
2017
2.31
Information system
2017
2017
2.13
Authentication
2016
2017
3.54
Institution
2017
2017
2.31
Computer
2017
2017
1.80
Crowdfunding
2018
2018
1.79
Start-up company
2018
2018
3.21
Taxonomy (general)1)
2018
2018
2.85
Digital electronics1)
2018
2018
2.15
Financialization
2018
2018
2.15
Informatics
2018
2018
2.08
Europe1)
2018
2018
1.70
Insurance
2018
2018
1.95
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Islamic banking and finance
2019
2019
3.10
Economic stability
2019
2019
2.53
Performance1)
2019
2019
2.19
Poverty
2020
2020
2.23
China
2020
2020
2.05
Information asymmetry
2020
2020
1.78
Investor
2020
2020
3.03
Debt
2020
2020
1.81
Financial inclusion
2020
2020
2.21
1) These topics were used across papers to refer to a wide variety of generic issues and we have therefore excluded them from further
discussion in this paper.
Table A3: Top ‘Bursting’ Topics in ABS-Ranked Journals
Topic
Burst Start
Burst Finished
Weight
Accounting
2017
2017
1.57
Institution *
2017
2017
1.85
Trade
2018
2018
1.91
Financial institution
2018
2018
1.66
Crowdfunding *
2018
2018
1.41
Disruptive innovation
2019
2019
1.42
Information system *
2017
2017
1.93
Informatics *
2018
2018
1.39
Business model
2020
2020
1.79
Saving
2020
2020
1.59
Debt *
2020
2020
1.59
Decision-making
2020
2020
1.90
* These topics are also bursting topics in our analysis of the entire sample. Overall, there is strong overlap between topics bursting
in ABS-ranked journals and our entire academic literature sample. Some deviations, such as the bursting topic of ‘Accounting’ can
be explained by publications that have appeared in accounting-related outlets, e.g., Rivas et al. (2017).
Note: Given that the number of papers was reduced by 80%, we also reduced the minimum frequency required for a topic to be
considered for the analysis. In our initial analysis, a topic had to appear a minimum of 10 times for it to be considered. Therefore,
we lowered this by 80%, and included topics that appeared a minimum of 2 times.
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About the Authors
Cynthia Cai is a Senior Lecturer in Applied Finance and the Director of Finance Decision Lab at Macquarie
Business School in Sydney, Australia. She has over 12 years of diversified academic and professional work
experience in Australia, mainland China and Hong Kong SAR. Cynthia’s research focuses on FinTech,
including Machine Learning Algorithms and Blockchain analytics, and has attracted significant academic,
industry and media attention. She is the recipient of two best papers awards, and her article on blockchain
was the most downloaded article on Wiley Online in 2019. Starting in 2020, Macquarie University began to
offer the very first Master’s degree with a FinTech specialization in Australia, with Cynthia being responsible
for designing and delivering the two core subjects: (1) FinTech and Innovations and (2) Payments,
Cryptocurrencies and Blockchain.
Mauricio Marrone is an Associate Professor of Business Information Systems at Macquarie University in
Sydney, Australia. His research uses text mining to analyze the emergence of scientific discoveries and
progress in business research. His work has been published in several journals, including Scientometrics,
European Journal of Information Systems, Technological Forecasting and Social Change and International
Journal of Information Management. Dr Mauricio Marrone has received the Vice-Chancellor’s award for
Learning Innovation and designed the Coursera MOOC Innovation and emerging technology: Be
disruptive.
Martina Linnenluecke is Professor of Environmental Finance and leads the Center for Corporate
Sustainability and Environmental Finance at Macquarie University, Sydney, Australia. The Centre brings
together an interdisciplinary team of experts in the areas of corporate sustainability and environmental
finance. The work undertaken by the Centre focuses on demonstrating a financial case for action on
environmental and social change, and also explores the intersection of FinTech and Sustainability.
Professor Linnenluecke has been a chief investigator on multiple projects funded by the Australian Research
Council, including a current project on “Creating Sustainability-Oriented Fintech Lending Platforms”
(LP200301118). Professor Linnenluecke has published over 100 academic articles, book chapters and
conference papers and has been the recipient of numerous awards for her work. Professor Linnenluecke is
a contributing author to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report
(AR6 WGII) and is a member of the College of Experts of the Australian Research Council (ARC).
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The recent outbreak of the COVID-19 pandemic has posed a significant threat to the healthy lives and well-being of billions of people worldwide. As the world begins to open up from lockdowns and enters an unprecedented state of vulnerability, or what many have called “the new normal”, it makes sense to reflect on what we have learned, revisit our fundamental assumptions, and start charting the way forward to contribute to building a sustainable world. In this essay, we argue that despite its significant damage to human lives and livelihoods, the coronavirus pandemic presents an excellent opportunity for the human family to act in solidarity and turn this crisis into an impetus to achieve the United Nation’s (UN) Sustainable Development Goals (SDG). In this article, we will highlight the six relevant themes that have evolved during the pandemic and the corresponding topics that future researchers could focus on. We conclude by issuing a call for more research attention on tackling SDG through developing the concept and practice of digital sustainability.