Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology

• Published date: 01 March 2023
• Author: Duc Khuong Nguyen,Georgios Sermpinis,Charalampos Stasinakis
• Date: 01 March 2023
• DOI: http://doi.org/10.1111/eufm.12365

DOI: 10.1111/eufm.12365

ORIGINAL ARTICLE

Big data, artificial intelligence and machine

learning: A transformative symbiosis in favour

of financial technology

Duc Khuong Nguyen

1,2

|Georgios Sermpinis

3

|

Charalampos Stasinakis

3

1

IPAG Lab, IPAG Business School, Paris,

France

2

International School, Vietnam National

University, Hanoi, Vietnam

3

Adam Smith Business School,

University of Glasgow, Glasgow, UK

Correspondence

Duc Khuong Nguyen, IPAG Lab, IPAG

Business School, 184 Blvd, St. Germain,

Paris 75006, France.

Email: d.nguyen@ipag.fr

Georgios Sermpinis and Charalampos

Stasinakis, Adam Smith Business School,

University of Glasgow, Glasgow, UK.

Email: Georgios.Sermpinis@glasgow.ac.

uk and Charalampos.Stasinakis@

glasgow.ac.uk

Abstract

This paper uses a multidimensional descriptive analy-

sis to familiarize the reader with the extent of

penetration of big data, artificial intelligence (AI) and

machine learning (ML) techniques in the financial

technology roadmap. We propose a clear framework

for the symbiotic nature of these data science themes

towards fintech empowerment. The framework is

validated through their impact on fintech, financial

services' profession and the shifting paradigm of the

data scientist role. We also discuss the dark side of this

symbiosis, while AI and ML techniques are tied with

the future challenges of AI ethics, regulation technol-

ogy and the smart data utilization.

KEYWORDS

artificial intelligence, big data, digital finance, fintech, machine

learning

JEL CLASSIFICATION

G10, G20, L51

Eur Financ Manag. 2023;29:517–548. wileyonlinelibrary.com/journal/eufm © 2022 John Wiley & Sons Ltd.

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We would like to thank two anonymous referees and the Editor‐in‐Chief, Professor John A. Doukas, for their

constructive comments that helped us significantly improve our paper.

1|INTRODUCTION

Financial Technology (Fintech) is a contemporary topic that has been at the centre of recent

developments in the finance industry and a subject of an emerging research strand. This abbreviation

is frequently cited in the same context of terms such as innovation, disruption, revolution, big data

analytics, blockchain inter alia. However, navigating through its landscape is not easy. To do so,

researchers such as Arner et al. (2015) follow a fintech timeline that starts before the 1970s. In

particular, the authors explain that the transition from analogue to digital services (Fintech 1.0:

1886–1967) is the first manifestation of fintech principles. This period is the prelude for developing

and expanding the required infrastructure for enabling globalized financial services. The installation

of the first ATM by Barclays in 1967 and the establishment of NASDAQ and SWIFT institute in the

'70s mark the era of Fintech 2.0 (1967–2008), which mainstreams the use of digital banking and

online customer services. Within these decades, the world came across electronic trading floors,

personal finance applications and high‐frequency trading. These advances have been revolutionary at

thetimeandhaveshapedfinancialmarketoperations till now. The above shows that it is a

misconception when the public associates fintech with the cryptocurrency hype after 2009. We thus

observe a transition to Fintech 3.0. The post‐financial crisis infused the lack of trust in banks and

brought the democratization of financial services to the forefront of the debate. This debate was

originally shaped around regulatory changes and technological advances (e.g., smartphone adoption,

Banking as a Service (BaaS) platforms, Distributed Ledger Technology, inter alia).

The fintech hype is evident over the last decade, but recently even more. Why now? Goldstein

et al. (2019) answer this question by suggesting that the fusion between technology and financial

services is more enabled now due to the high pace of adoption of new technologies. This fusion

originates mostly from outside the financial industry, through startups and big tech companies

disrupting banks' business models and their traditional competitive advantage in financial services

products. The financial world is gradually evolving into a digital domain of high‐volume information

and high‐speed data transformation and processing. The more this transformation takes place, the

more consumer and investor behaviours shift towards a protechnology attitude of financial services

offered by market participants, financial institutions and financial technology companies. Fintech

solutions nowadays take the form of a service, software, or business decision‐making tool that allows

financial processes to be more efficient in terms of cost and speed, while consumers are further

empowered by selecting from a variety of options better tailored to their needs and financial

circumstances. The effect of this is that traditional financial institutions become gradually unpopular

and their operations get disrupted and disintermediated. We take the arguments of Arner et al. (2015)

and Goldstein et al. (2019) one step further and we postulate that fintech is more ‘exotic’now than ever,

because of the gradual acceptance of Artificial Intelligence (AI) and Machine Learning (ML) techniques

in finance along with the availability of big data after the Internet‐of‐Things and Web 2.0 revolution of

the last decades. These three elements are ‘breadcrumbs’within Fintech 3.0. However, their symbiosis

canleadtoafintechtransformationintheyearstocome,namely,Fintech 4.0.

This symbiosis is tethered because digital technologies enable entrepreneurial activity and

expand its scope (Chalmers et al., 2021; Nambisan, 2017) and boost fintech empowerment (Siau

et al., 2019). AI automation and ML efficiency are the main technological tools to harness and

leverage the information power from big data pools concentrated in servers of financial

institutions and firms. This confirms that fintech innovation and disruption of traditional

financial services are steered through information technology advances (Hauswald &

Marquez, 2003). These two pillars of the data analytics discipline are found to be the main

technology of choice for key participants in the financial markets aiming at improving security,

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reducing operating costs and smoothing business processes and more importantly, providing

robust financial analysis results for decision making. Academic findings such as the above are

also supported by practitioner reports. For, example Accenture (2017) states that fintech is

more about ‘tech’than ‘fin’, implying that improvements in financial services would not be

possible without the emergence of new technologies such as AI and ML. McKinsey Global

Institute (2018) also identifies a fintech company as a start‐up that applies technological

advances to impact the way consumers behave in terms of their basic financial operations (e.g.,

savings, borrowing, investing, payments and protection).

To motivate our study, we showcase the transformative fusion between big data, AI and ML

through some examples from the fintech sector. Battling financial fraud is an example of significant

success from the side of AI. AI‐based agents are very efficient, particularly in identifying malicious

transactions related to e‐commerce and credit card online transactions (Ryman‐Tubb et al., 2018).

The success is based on the systematic and automated analysis of location, transaction history and

shopping habit data of credit card users through ML algorithms. For example, open‐banking

operators such as Plaid use this as a major selling point for their digital financial services. AI and ML

solutions are also highly successful in credit decision‐making. Their main advantage over traditional

credit scoring systems and underwriting processes is that they are cost‐and time‐efficient and, at the

same time, less restrictive and more transparent. ZestFinance and Ocrulus are typical platforms

offering such credit scoring and lending management solutions. Finally, AI and ML techniques offer

better customer targeting and retention platforms. One major success of the fintech industry is

attracting and displacing GenZ customers from traditional financial institutions. The implementation

of robo‐advisors through ML agents is the key driver of this success. Moneybox and Mylo APIs are

representative cases of how millennials can be persuaded towards saving and investment and

eventually become more financial literate. Big data analytics and gaining strategic value from the

extrapolated information are common denominators for all case studies mentioned above.

This paper contributes to the related literature by providing a comprehensive review of how

AI, big data and ML have been shaping the future of fintech. Other studies, such as Lee and

Shin (2018), discuss the historical evolution of fintech and assess that fintech creates disruptive

innovation capable of changing business models and investment types in financial markets.

Recently, Liu et al. (2020) review the 629 research papers on fintech business models and find

that mobile payment, microfinance, peer‐to‐peer lending platform and crowdfunding are the

essential research topics over the period 1995–2017, while future studies will look at blockchain

and crowdfunding. We take a distinct approach that differs from such studies in that we

combine insights and findings from academic research, professional surveys and regulatory

perspectives. To date, several attempts have been made to review and synthetize the state of the

art of the fintech ecosystem and research (e.g., Lee & Shin, 2018; Liu et al., 2020; Milian

et al., 2019), but little is known about the effects of AI and ML in conjunction with big data on

the fintech evolution and applications. Another important strand of literature focuses more on

the impacts of fintech on financial markets, instruments and services (e.g., Boot et al., 2021;

Chang et al., 2020; X. Chen, Yu, et al., 2021). Although our work ties with that literature too, to

the best of our knowledge, this paper is the only study that attempts to shed light on how the

fusion of big data, AI and ML occurs in the fintech domain. In that effort, a novel conceptual

framework is provided to highlight how these three data science pillars manifest symbiotic

characteristics that have a transformative effect on the fintech sector.

The remainder of the paper is organised as follows. Section 2presents some interesting descriptive

statistics on AI, big data and ML techniques and illustrates their symbiotic characteristics towards

fintech. Section 3presents a nontechnical summary of big data, AI and ML in relation to the

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