APAC-Sensitive Anomaly Detection: Culturally-Aware AI Models for Enhanced AML in U.S. Securities Trading

This paper presents a novel culturally-aware artificial intelligence framework for enhancing anti-money laundering (AML) detection in cross-border securities trading, specifically targeting Asia-Pacific (APAC) investors in US markets. Conventional AML systems struggle with high false positive rates when monitoring APAC investors due to legitimate cultural variations in trading behaviors being misclassified as suspicious. Our approach integrates region-specific cultural feature vectors within a hybrid machine learning architecture, enabling accurate distinction between legitimate cultural trading patterns and genuinely suspicious activities. The framework incorporates temporal clustering for time-zone specific behaviors, regional trading preference calibration, and adaptive threshold adjustment through reinforcement learning. Experimental evaluation using 3.7 million trading transactions from 42,856 APAC investors demonstrated significant performance improvements compared to conventional systems. Results show an average 17.4% improvement in F1-scores across APAC regions with false positive reductions of 48.6%, 44.2%, and 41.9% for Hong Kong, Singapore, and Australian investors respectively. The system maintains 98.7% regulatory compliance while reducing average transaction analysis time by 32.5%. This research addresses a critical gap in cross-border financial monitoring capabilities, enhancing detection precision without compromising regulatory requirements. The implementation strategies and cost-benefit analysis provide practical guidance for US financial institutions serving APAC clients.