Artificial Intelligence and Machine Learning Approaches for Detecting Complex Money Laundering Patterns in Global Financial Transactions

Author

Victoria Anderson


Abstract

 The increasing globalization of financial markets, expansion of digital banking, and proliferation of cross-border transactions have intensified the complexity of money laundering activities, posing significant challenges to financial integrity, regulatory compliance, and economic stability. Traditional anti-money laundering systems, predominantly reliant on predefined rules and transaction thresholds, frequently struggle to identify sophisticated laundering schemes involving layered transactions, concealed beneficial ownership, and interconnected financial networks. This study examines the transformative potential of artificial intelligence and machine learning in detecting complex money laundering patterns across global financial systems. Particular attention is given to supervised learning, unsupervised anomaly detection, deep learning, and graph-based analytical techniques for identifying suspicious transactional behaviours, hidden relationships, and evolving financial crime typologies. Furthermore, the study explores the integration of predictive analytics, explainable artificial intelligence, and real-time transaction monitoring to strengthen detection accuracy, reduce false positives, and enhance regulatory decision-making. It also critically considers challenges associated with data fragmentation, algorithmic bias, privacy protection, model interpretability, and cross-jurisdictional regulatory differences. Ultimately, the study highlights the importance of adaptive, transparent, and collaborative intelligence-driven frameworks for strengthening global financial surveillance and combating increasingly sophisticated money laundering operations.


Keywords

Artificial Intelligence; Machine Learning; Money Laundering Detection; Financial Transaction Monitoring; Graph Neural Networks; Anti-Money Laundering Compliance.



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References


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