International Journal on Science and Technology

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A Machine Learning Paradigm for Cross-Sector Financial Crime Prevention

Author(s) Hariprasad Sivaraman
Country United States
Abstract In today’s fast paced world, financial crimes such as corporate tax evasion, financial legerdemain, money laundering and terrorist financing are constantly evolving with sophistication and scale, and bad actors have the increasing ability to take advantage of the shortcomings of traditional siloed solutions designed for detection. These crimes may sometimes mirror methodology and impact, while threatening the financial order on a global scale. The current detection techniques are over reliant on manual reviews, rule-based systems, and domain-specific methods, leaving complex, inter-twined fraud schemes undetected.

This paper presents a Machine Learning (ML) based framework for cross-sector data integration for monitoring and prevention of financial crimes. The suggested system integrates several complex ML techniques, ranging from anomaly detection to Graph Neural Networks (GNNs), Reinforcement Learning (RL), and online risk scoring. The real-time framework allows for scalable and effective detection through dynamic thresholds with risk prioritization. The transformative power of this system in tackling complex methods of modern financial crime is demonstrated through real-life applications including Anti Money Laundering (AML), corporate tax fraud, audit manipulation, and many more.
Keywords Cross-Sector Financial Crime Prevention, Machine Learning, Corporate Tax Fraud, CPA Audits, AML, Terrorist Financing, Graph Neural Networks, Reinforcement Learning, Real-Time Risk Scoring
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 14, Issue 2, April-June 2023
Published On 2023-04-04
Cite This A Machine Learning Paradigm for Cross-Sector Financial Crime Prevention - Hariprasad Sivaraman - IJSAT Volume 14, Issue 2, April-June 2023.
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