ANTI-MONEY LAUNDERING THROUGH ARTIFICIAL INTELLIGENCE

The increasing complexity of financial crimes has made it essential for financial institutions to continuously modernize their anti-money laundering frameworks. This thesis investigates how artificial intelligence can strengthen AML processes by enhancing transaction monitoring, customer due diligence, and suspicious activity detection, while ensuring alignment with regulatory requirements. Grounded in global AML standards including FATF Recommendations, EU Anti-Money Laundering Directives, and national legislation in the Czech Republic and Turkey, the study examines how AI-powered tools integrate with existing compliance obligations. Using a case study of a Turkish commercial bank, the findings reveal that AI functions as a decision-support mechanism operating under human oversight rather than as an autonomous system, and highlight structural challenges including data quality issues, model explainability shortcomings, and the absence of formally defined AI governance standards. Focusing on the intersection of regulatory compliance and technological transformation, this study contributes to the academic and practical understanding of AI as a strategic enabler in modern AML compliance environments.