Reinforcement Learning for Pattern Recognition in Cross-Border Financial Transaction Anomalies: A Behavioral Economics Approach to AML

This paper presents a novel approach to anti-money laundering (AML) in cross-border financial transactions by integrating reinforcement learning (RL) with behavioral economics principles. The research addresses critical limitations in traditional AML systems by conceptualizing money laundering detection as a sequential decision-making problem where detection policies adapt to evolving criminal strategies. We develop a specialized methodology that incorporates multi-level data representations, behavioral feature extraction algorithms, and a composite reward function designed to balance detection accuracy with investigation efficiency. The framework leverages behavioral economics principles to distinguish between legitimate financial anomalies and suspicious patterns indicative of money laundering activities. Experimental evaluation across three datasets demonstrates that the proposed approach achieves a 27.4% improvement in money laundering detection rate while reducing false alerts by 18.6% compared to state-of-the-art methods. Behavioral pattern recognition components prove particularly effective for identifying sophisticated laundering schemes characterized by strategic transaction structuring and temporal spacing designed to evade traditional detection systems. Case studies of cross-border money laundering operations validate the approach's effectiveness in operational environments. The research contributes a unified theoretical framework that enhances AML capabilities while providing practical implementation guidance for financial institutions and regulatory bodies engaged in combating cross-border financial crime.