Automated Anti-Money Laundering (AML) systems are vital for financial institutions, but managing false positives—legitimate transactions incorrectly flagged as suspicious—remains a significant challenge. Factors contributing to false positives include rigid detection rules, outdated customer data, and similar names. These misidentifications can disrupt legitimate transactions and damage customer trust. To address this, institutions are encouraged to improve data governance, update detection algorithms dynamically, and utilize AI for better decision-making. By adopting predictive monitoring and aligning with industry standards, financial organizations can enhance transaction monitoring, balance compliance with customer experience, and effectively counter fraud.