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

45 Pages Posted: 9 Sep 2020

Date Written: July 29, 2020

Abstract

The financial technology revolution is a reality, as the financial world is gradually transforming 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 behaviour shifts towards a pro-technology attitude of financial services offered by market participants, financial institutions and financial technology companies. This new norm is confirming that information technology is driving innovation for financial technology. In this framework, the value of big data, artificial intelligence and machine learning techniques becomes apparent. The aim of this chapter is multi-fold. Firstly, a multidimensional descriptive analysis is shown to familiarise the reader with the extent of penetration of the above in the financial technology road-map. A short non-technical overview of the methods is then presented. Next, the impact of data analytics and relevant techniques on the evolution of financial technology is explained and discussed along with their applications’ landscape. The chapter also presents a glimpse of the shifting paradigm these techniques bring forward for several fintech related professions, while artificial intelligence and machine learning techniques are tied with the future challenges of AI ethics, regulation technology and the smart data utilisation.

Keywords: FinTech, Artificial Intelligence, Machine Learning, Big Data, Digital Finance, RegTech, Smart Data

JEL Classification: C5, C6, G2, O3

Suggested Citation

Stasinakis, Charalampos and Sermpinis, Georgios, Big Data, Artificial Intelligence and Machine Learning: A Transformative Symbiosis in Favour of Financial Technology (July 29, 2020). Available at SSRN: https://ssrn.com/abstract=3663062 or http://dx.doi.org/10.2139/ssrn.3663062

Charalampos Stasinakis (Contact Author)

University of Glasgow ( email )

University Avenue
Adam Smith Business School
Glasgow, Scotland G128QQ
United Kingdom

Georgios Sermpinis

University of Glasgow ( email )

Adam Smith Business School
Glasgow, Scotland G12 8LE
United Kingdom

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