Big Data, Artificial Intelligence, and Machine Learning: A Transformative Symbiosis in Favour of Financial Technology

Aug 4, 2026

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This paper uses a multidimensional descriptive analysis to map the penetration of big data, artificial intelligence (AI), and machine learning (ML) techniques in the financial technology roadmap. A framework is proposed to explain the symbiotic relationship among these data-science themes and how it can empower fintech. The framework is assessed through their effects on fintech, financial-services professions, and the evolving data-scientist role, while also addressing the dark side via AI ethics, regulation technology, and smart data utilization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages57

Reading Time2 h 24 min

AccessFree to download

Introduction

Fintech evolution and digital transformation

Motivation for the role of new technologies

Abstract and contribution overview

Nguyen, D. K., Sermpinis, G. and Stasinakis, C. (2023) Big data, artificial intelligence, and machine learning: a transformative symbiosis in favour of financial technology. European Financial Management, 29(2), pp. 517-548. There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it. This is the peer reviewed version of the following article: Nguyen, D. K., Sermpinis, G. and Stasinakis, C. (2023) Big data, artificial intelligence, and machine learning: a transformative symbiosis in favour of financial technology. European Financial Management, 29(2), pp. 517-548 , which has been published in final form at [https://doi.org/10.1111/eufm.12365](https://doi.org/10.1111/eufm.12365) This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. [http://eprints.gla.ac.uk/268977/](http://eprints.gla.ac.uk/268977/) [Deposited on: 11 April 2022](Deposited on: 11 April 2022) Enlighten – Research publications by members of the University of Glasgow [http://eprints.gla.ac.uk](http://eprints.gla.ac.uk) Big Data, Artificial Intelligence, and Machine Learning: A Transformative Symbiosis in Favour of Financial Technology Duc Khuong Nguyen1,2 􀁾 Georgios Sermpinis3 􀁾 Charalampos Stasinakis3 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, United Kingdom Correspondence Duc Khuong Nguyen, IPAG Lab, IPAG Business School, 184, Boulevard Saint‐Germain, Paris 75006, France. Email: D.K. Nguyen ([d.nguyen@ipag.fr](d.nguyen@ipag.fr)), G. Sermpinis (Georgios.Sermpinis@glas[gow.ac.uk](gow.ac.uk) , C. Stasinakis (Chara[lampos.Stasinakis@glasgow.ac.uk](lampos.Stasinakis@glasgow.ac.uk)) Abstract This paper uses a multidimensional descriptive analysis 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 in 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 technology and the smart data utilization. KEYWORDS FinTech, artificial intelligence, machine learning, big data, digital finance JEL CLASSIFICATION G10, G20, L51 The authors would like to thank two anonymous referee 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 in 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 1970’s. 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 70’s mark the era ofFintech 2.0 (1967 – 2008), which mainstreams the use of digital banking and online customer services. Within these decades, the world came across electronic trading floor

FAQs

What is the main purpose of this paper?

To analyze how big data, AI, and machine learning penetrate the fintech roadmap and to propose a framework describing their symbiotic relationship for fintech empowerment.

How does the paper validate its framework?

By examining the impact of these data-science themes on fintech, financial-services professions, and the shifting paradigm of the data scientist role.

What risks and challenges does the paper discuss?

It addresses the dark side of the symbiosis, linking AI and ML to future challenges such as AI ethics, regulation technology, and smart data utilization.

Aug 4, 2026

Upload by Skyler