Harnessing artificial intelligence for combating money laundering and fraud in the U.S. financial industry: A comprehensive analysis
1 Department of Electrical Engineering, University of Ibadan, Ibadan Nigeria.
2 College of Business, University of Louisville, Kentucky USA.
3 Department of Computer Science, University of Lagos, Akoka, Lagos Nigeria.
4 Department of Information Science, Cornell University, New York USA.
5 Data Science and Analytics Institute, University of Oklahoma, Norman USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 17(02), 940-953
Article DOI: 10.30574/wjarr.2023.17.2.0227
Publication history:
Received on 22 November 2022; revised on 25 February 2023; accepted on 27 February 2023
Abstract:
The increasing sophistication of financial crimes, particularly money laundering and fraud, has necessitated the adoption of advanced technological solutions in the U.S. financial industry. Artificial Intelligence (AI) has emerged as a critical tool in combating these illicit activities by enhancing detection, prevention, and compliance mechanisms. This research explores and analyses the role of AI in countering money laundering and fraud, assessing its effectiveness, challenges, and regulatory implications. A qualitative research methodology was employed, utilizing secondary data sources, including reports from regulatory bodies, financial institutions, and academic literature. This approach provided a comprehensive analysis of AI-driven anti-money laundering (AML) solutions, emphasizing their impact on transaction monitoring, anomaly detection, and risk assessment. The research also examined key challenges, such as data privacy concerns, algorithmic biases, and regulatory compliance, which hinder AI’s full potential in financial crime prevention. Findings reveal that while AI significantly improves fraud detection capabilities, its implementation remains constrained by regulatory gaps and concerns regarding transparency and fairness. Public-private partnerships, secure data-sharing frameworks, and robust AI governance structures are essential to ensuring ethical and effective AI deployment in the financial sector. The research concludes that AI holds immense promise in strengthening AML and fraud prevention strategies but requires continuous innovation and regulatory alignment to maximize its effectiveness.
Keywords:
Artificial Intelligence (Ai); Money Laundering; Machine Learning; Algorithm; Fraud; Anti-Money Laundering (Aml)
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0