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AI-powered financial crime prevention with cybersecurity, IT, and data science in modern banking
1 Department of Engineering and Computing, School of Architecture, Computing, and Engineering, University of East London, London, United Kingdom.
2 College of Engineering Technology, Grand Canyon University, Phoenix, Arizona, USA.
3 Interswitch Group, Lagos, Nigeria.
4 Joltz Security Nigeria Limited, Lagos, Nigeria.
5 Department of Computer Science, Montclair State University, New Jersey, USA.
6 Independent Research Consultant (Foylan Incorporated), IT Project Manager, Toronto, Canada.
Review
International Journal of Science and Research Archive, 2024, 13(02), 570–579.
Article DOI: 10.30574/ijsra.2024.13.2.2143
Publication history:
Received on 25 September 2024; revised on 05 November 2024; accepted on 07 November 2024
Abstract:
Financial crime in modern banking has evolved significantly with the digital transformation of financial services, presenting unprecedented challenges to traditional prevention methods. This comprehensive review examines the integration of artificial intelligence (AI), cybersecurity frameworks, and data science methodologies in combating financial crime within the banking sector. We analyze the current state of AI-powered solutions, including machine learning models, real-time detection systems, and advanced analytics frameworks that have transformed financial crime prevention. The review synthesizes findings from recent studies and industry implementations, highlighting the synergistic relationship between AI technologies and cybersecurity measures in creating robust defense mechanisms. Our analysis reveals that while AI-powered solutions demonstrate superior detection rates and reduced false positives compared to traditional methods, significant challenges remain in areas of data privacy, regulatory compliance, and system integration. The paper concludes by identifying critical research gaps and proposing future directions for enhancing the effectiveness of AI-based financial crime prevention systems. This review provides valuable insights for researchers, banking professionals, and policymakers working at the intersection of AI, cybersecurity, and financial crime prevention.
Keywords:
Artificial Intelligence; Financial Crime Prevention; Machine Learning; Cybersecurity; Banking Security; Data Analytics
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Copyright information:
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
