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Artificial Intelligence in Financial Technology
CSWIM (Short Paper)
Keng Leng Siau
City University of Hong Kong
klsiau@cityu.edu.hk
Fiona Fui Hoon Nah
City University of Hong Kong
fuihnah@cityu.edu.hk
Yuzhou Qian
City University of Hong Kong
yuzhou.qian.20@alumni.uci.ac.uk
Brenda L. Eschenbrenner
University of Nebraska at Kearney
eschenbrenbl@unk.edu
Langtao Chen
Missouri University of Science & Technology
chenla@mst.edu
Abstract
AI applications in health care, communications, and arts have brought about rapid and dramatic
advances in these fields. Nevertheless, the rapidly expanding potential of AI in the economy and
society has raised a set of challenging issues. The fields of AI and financial technology are not
spared. How can artificial intelligence (AI) be utilized in financial technology (fintech)? What
will be the impact? What actionable objectives are needed to realize value from AI? This
research uses a systematic qualitative research methodology, Value-Focused Thinking, to
identify the actionable objectives for deriving value from AI in the fintech industry. The results of
this study will provide a theoretical framework for pursuing future research as more AI
applications are developed in the fintech industry. The results of this research provide guidance
to practitioners for achieving value in their AI ventures.
Keywords: artificial intelligence, fintech, value-focused thinking
1. Introduction
Financial technology (Fintech) is defined as "a new financial industry that applies technology to
improve financial activities" (Schueffel, 2016, p.45). Nowadays, the concept is used to illustrate
any innovative methods that enhance and automate financial services (Mention, 2019). The rapid
development of fintech is driven by innovative technologies, such as artificial intelligence and
blockchain, and it has gained attention from innovators, academics, and regulators (Mention,
2019). Startup firms promote more user-friendly products, scholars concentrate on the nature and
the effect of the new technology, and policy-makers determine the expected usage of fintech
(Hornuf et al., 2021; Mention, 2019). Although the scale of fintech is already large (with more
than 1,400 fintech firms reported by Ernst & Young), it is still expanding (Das, 2019). This paper
discusses the impact of one emerging technology - artificial intelligence - on the growth and
development of fintech.
Artificial intelligence (AI) aims at "making intelligent machines" (McCarthy, 2004, p.2). The
concept of "AI-empowered" is gaining increasing popularity. Currently, key participants in
modern finance are not entirely humans; instead, machines constitute a large proportion. They
take over routine and structured tasks such as standardized analysis. Since AI can help business
leaders automate time-consuming and labor-intensive operations, and it enables businesses to
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offer innovative services to customers, the application of AI in the fields of finance has attracted
much attention and interest (Siau et al., 2018; Lin, 2019). The industry is evolving as
organizations that were customarily financial institutions are mutating into information
technology enterprises, and vice versa (Hendershott et al., 2021). With these transformations and
the potential of AI, it will be important for organizations to identify objectives that need to be
accomplished in order for the full value of AI to be realized.
This research addresses the following question: What objectives need to be achieved to realize
the greatest value from AI in the fintech industry? This study aims at discovering the future
development of the application of AI in the fintech industry. More specifically, to ensure that AI
reaches its potential contribution, it will be important to understand what fundamental objectives
need to be met for its value to be fully realized and what are the means objectives to achieve the
fundamental objectives. Understanding the means-end objective network will provide valuable
guidance for researchers and practitioners to derive value from AI in the fintech industry.
2. Literature review
Although definitions of AI are varied and multifaceted, AI can be conceptualized with five
attributes to differentiate it from other technologies. These five attributes are (Hamm and Klesel,
2021):
1. Ability to resolve complex problems – AI solves issues that were once not conceivably
possible.
2. Processing that imitates humans – AI mirrors humans' cognitive functioning.
3. Associations with intelligence – Some aspects of AI functions are considered
intelligence.
4. Technology-based – Technology is prominent in AI.
5. Leveraging external data – AI normally utilizes external data sources for learning.
Because of its distinctiveness and potential, AI has attracted much research attention over the
decades (Hyder et al., 2019). The rapid advancement in machine learning has spurred the interest
in AI (Wang and Siau, 2019). AI technologies and applications span from the use of deep
learning in self-driving cars to natural language processing to analyze text. AI has the potential to
automate tasks, engage with individuals (e.g., customers), generate insights and make decisions,
and support innovation. With AI's uniqueness in comparison to other technologies, it will be
essential to identify the actionable objectives needed to realize its value. Previous studies have
identified success factors associated with AI adoption, such as top management support and
appropriate resources (e.g., data) (Hamm and Klesel, 2021), as well as employees' perceptions
and attitudes prior to AI adoption (Chiu et al., 2021). What has not been identified, however, are
the actionable objectives needed to derive value from AI.
Financial technology (fintech) "encompasses innovative financial solutions enabled by IT"
(Puschmann, 2017). Artificial intelligence and data science are the promoters of the new
generation of fintech, as they have the potential to discover previously hidden relationships
among variables (Wall, 2018). Concepts and tasks in the fintech field are redefined as AI affects
the operation of financial organizations, transfers the way participants interact, and raises new
financial mechanisms. AI-empowered finance has promoted a new era of smart digital currencies,
risk management, and lending (Cao, 2022). Considering the substantial quantity and diversity of
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data in the financial services industry, AI is of significant importance (Veloso et al., 2021). For
example, AI in fintech includes assessing loan applications with neural networks and approving
credit with rule-based expert systems. The development of AI helps improve the efficiency of
financial firms and the quality of financial services and products by reducing cost, enhancing
productivity, and promoting more tailored products (OECD, 2021). Based on AI technology,
security companies build an "intelligent control application system" which can analyze both
internal and external big data and identify and warn of hidden risks (Guo and Polak 2021, p.175).
Fintech lenders use complex AI algorithms to make credit decisions quickly (Jagtiani and John,
2018). Ant Financial, a leading Chinese fintech company, promoted the "Smile-to-Pay" service
based on computer vision technology. Customers can complete payment by "smiling" at a
vending machine without using phones or cash (Qi and Xiao, 2018). Further, investors analyze
big data to find customers' demand information with the help of AI and identify customer
investment preferences (Guo and Polak 2021, p.175).
As an emerging technology, AI also brings new risks and challenges to the financial industry.
Wall (2018) claimed that the AI algorithm could identify relationships that are not causal. This
could lead to biases against some protected classes (i.e., gender, race). Since the decision
processes are complex and invisible, it is hard for humans to regulate and intervene (Jagtiani and
John, 2018). Some studies found that it is hard to convince people to trust financial services and
advice provided solely by automated systems (Fenwick and Vermeulen, 2017). Researchers also
found that Fintech lenders may undermine existing financial regulations (Braggion et al., 2019).
Therefore, regulations that promote policies to protect consumers while encouraging the
innovation and development of new technologies need to be further improved (Jagtiani and John,
2018). For AI's potential contribution to be realized, it will be important to identify the value that
AI can provide and objectives that can be achieved to realize this value. Previous studies have
focused on other aspects of AI in fintech, such as the effectiveness of using machine learning in
P2P lending and methods of removing bias (Fu, Huang, and Singh, 2021), algorithmic trading
systems integrating investors' dispositions (Martínez, Román, and Casado, 2019), and investor
reliance on humanized robo-advisors (Hodge, Mendoza, and Sinha, 2021). However, research is
lacking in discerning the value that can be derived from AI in fintech and the objectives needed
to realize this value.
3. Research Question, Methodology, and Procedure
The application of AI in the fintech industry is relatively new, and much is unknown about the
domain. In this research, we employ a systematic, qualitative research methodology,
Value-Focused Thinking (VFT), to identify the values of AI in the fintech industry. VFT has
been utilized in many IS studies (e.g., Nah et al., 2005; Sheng et al., 2010; Rzepka, 2019; Smith
and Dillon, 2019) and is considered to be an effective methodology in the contexts of emerging
IT (Sheng et al., 2005, 2007).
In the context of decision-making, values should be the ultimate guide (Keeney, 1996). Although
alternatives are utilized in making decisions, they are utilized to achieve what one values. Values
are what we care about, and values are principles used for evaluation (Keeney, 1996). In this
case, values are used to evaluate the actual or potential consequences of action and inaction of
proposed AI alternatives and decisions. To derive values from AI in fintech, one should start
with identifying what one values. Values can encompass "ethics, desired traits, characteristics of
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consequences that matter, guidelines for action, priorities, value tradeoffs, and attitudes toward
risk" (Keeney, 1992, p. 7). VFT provides specific procedures for articulating values by
identifying and structuring objectives qualitatively. These objectives include fundamental and
means objectives. Fundamental objectives are "the essential reasons for interest in the situation"
(Keeney, 1992, p. 34). Means objectives are important to achieving fundamental objectives or
other means objectives. The means objectives are differentiated from fundamental objectives by
using the "Why is it important?" test. If an objective is important because it helps to achieve
another objective, it is a means objective. Otherwise, it is a fundamental objective. The
means-ends objective network is derived from the objectives identified and relationships that
emerge through this process. Values are represented as objectives in the means-ends objective
network. The VFT process is depicted in Figure 1.
Figure 1 Process of Value-Focused Thinking Approach
The interviewees (i.e., subjects) for this research will be fintech professionals and business
executives in these fields. We will interview each of them individually and ask questions to
solicit the values that he or she believes are important in utilizing AI in the fintech industry.
When the interviewees do not generate any further new concepts (i.e., the point of saturation is
reached), we will remove duplicate objectives and combine similar objectives. The consolidated
list of objectives and their relationships describe the values of AI in fintech. Prompting questions
used in the interviews include:
• What would you like to achieve with AI in the fintech industry?
• What potential benefits can be derived from AI applications in the fintech industry?
• If there were no limitations to AI in fintech, what are your expectations?
• What issues do you see in AI applications in fintech?
This research will use snowball sampling, which is a type of convenience sampling, to recruit
subjects for this study. Fintech professionals that are known to the authors will be the initial
recruits for participation. These professionals will be requested to nominate other potential
participants who are also fintech professionals or are associated with the fintech industry
(chain-referral sampling method). The participants will need to have at least three years of
experience in the fintech industry as well as an understanding of AI to be eligible to participate.
The size of the sample will be determined by the point of saturation. The saturation point is
attained when further data collection does not contribute to the research. Saturation is a common
standard and a methodological principle in qualitative research.
4. Pilot Study and Expected Contributions
The pilot study for this research is ongoing. As one of the first studies in this stream of research
to identify the values of AI for fintech professionals using a systematic and well-accepted
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qualitative research methodology, Value-Focused Thinking (VFT), the results of the VFT study
in the form of a means-ends objective network will provide a theoretical framework for
advancing this area of research. For practitioners, the means-ends objective network can help
them to understand the means to achieving the value of AI, and assist business executives in
planning for AI applications in the domain of fintech.
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