COMBATING MONEY LAUNDERING: APPLYING ARTIFICIAL INTELLIGENCE TO DETECT SUSPICIOUS MUTUAL FUND TRANSACTIONS

Abstract

This qualitative study examines complex money-laundering patterns in the asset management industry and explores strategic guidelines for applying Artificial Intelligence (AI) to detect suspicious mutual fund transactions. Utilizing in-depth, semi-structured interviews with twenty-one purposively selected key informants—comprising compliance officers at asset management companies and regulatory authorities from the Anti-Money Laundering Office—the research systematically analyzes current financial crime vulnerabilities. The empirical findings reveal that illicit funds are typically laundered through systematic placement, concealment, and economic integration within mutual fund portfolios. While AI offers a transformative solution for identifying anomalous financial behavior and effectively cross-referencing watchlist individuals, significant operational challenges remain. Consequently, the study proposes a comprehensive implementation framework that emphasizes seven critical guidelines: establishing clear corporate policies, managing employee adaptation to new technology, optimizing budget allocations, ensuring strict data privacy, acknowledging inherent algorithmic limitations, evaluating overall cost-effectiveness, and reinforcing rigorous compliance oversight. Ultimately, intelligently integrating AI into anti-money laundering protocols significantly enhances transaction monitoring efficiency, fortifying the financial sector against increasingly sophisticated economic crimes while safeguarding long-term market sustainability and promoting broader economic security.