AI-driven fraud detection in banking: A systematic review of data science approaches to enhancing cybersecurity
1 Interswitch Group, Lagos, Nigeria.
2 Joltz Security Nigeria Limited, Lagos, Nigeria.
3 Department of Engineering and Computing, School of Architecture, Computing, and Engineering, University of East London, London, United Kingdom.
4 College of Engineering Technology, Grand Canyon University, Phoenix, Arizona, USA.
5 School of Technology, Western Governors University, UT, USA.
6 Independent Research Consultant (Foylan Incorporated), IT Project Manager, Toronto, Canada.
7 Department of Computer Science, Montclair State University, New Jersey, USA.
Review Article
GSC Advanced Research and Reviews, 2024, 21(02), 227–237.
Article DOI: 10.30574/gscarr.2024.21.2.0418
Publication history:
Received on 30 September 2024; revised on 05 November 2024; accepted on 08 November 2024
Abstract:
The proliferation of sophisticated financial fraud and cybersecurity threats in the banking sector necessitates advanced detection and prevention strategies. This comprehensive review examines the current state of artificial intelligence and data science techniques in fraud detection systems within banking institutions, with particular emphasis on enhancing cybersecurity measures. Through systematic analysis of peer-reviewed literature, industry reports, and empirical studies from the past decade, we evaluate the effectiveness of various machine learning algorithms, deep learning architectures, and real-time monitoring systems in fraud detection. Meta-analysis of 47 studies indicates that contemporary AI-powered fraud detection systems achieve detection rates of 87-94% while reducing false positives by 40-60% compared to traditional rule-based methods. Furthermore, integrated AI approaches combining supervised and unsupervised learning techniques consistently demonstrate superior performance in detecting novel fraud patterns and adapting to emerging threats. This review synthesizes current research findings, identifies gaps in existing literature, and provides a comprehensive framework for implementing robust fraud detection systems in banking institutions.
Keywords:
Artificial Intelligence (AI); Banking Fraud Detection; Machine Learning; Cybersecurity; Real-time Analytics; Deep Learning; Systematic Review
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
