AI-Based Pattern Recognition and Characteristic Analysis of Cross-Border Money Laundering Behaviors in Digital Currency Transactions

Digital currency transactions have become increasingly prevalent for cross-border money laundering activities, presenting significant challenges to traditional anti-money laundering (AML) frameworks. This research investigates the application of artificial intelligence techniques for identifying and analyzing patterns in cross-border money laundering behaviors within digital currency ecosystems. Through comprehensive analysis of transaction data and behavioral characteristics, this study develops a systematic approach to pattern recognition using machine learning and deep learning methodologies. The research examines various money laundering schemes, including mixing services, layering techniques, and decentralized finance exploitation. Advanced AI algorithms demonstrate superior performance in detecting suspicious transaction patterns compared to conventional rule-based systems. The findings reveal distinct behavioral signatures associated with illicit cross-border activities, enabling more effective detection and prevention strategies. This work contributes to the advancement of regulatory technology solutions and provides insights for policymakers and financial institutions in combating digital currency-based money laundering.