False positives in AML compliance generally refer to instances when a transaction or customer record is flagged as potentially matching a name on a sanctions list, a watchlist, or a politically exposed person (PEP) list, but upon further review, the match is found to be incorrect. These are instances where legitimate transactions or behaviours are flagged by automated monitoring systems as potentially illicit. While such systems are essential for detecting and preventing money laundering, fraud, and other financial crimes, they can sometimes produce false positives due to overly broad or sensitive detection criteria.  

Managing false positives is crucial as they can lead to unnecessary investigations, wasted resources, and customer dissatisfaction. Financial institutions employ various strategies to minimise false positives, including refining detection algorithms, implementing machine learning models, and enhancing risk assessment frameworks. Accurate data and continuous system updates are essential for improving these monitoring systems' precision. By effectively managing false positives, organisations can focus their efforts on genuine risks, ensuring more efficient and effective compliance processes while maintaining customer trust and operational efficiency.