In an era where the digital transformation of the financial services sector has accelerated at an unprecedented pace, generative artificial intelligence (AI) for anti-money laundering (AML) compliance has been hailed as a cure-all.

Generative AI providers often promote their technologies as revolutionary tools that can enhance AML processes. The technology is touted as a solution to automate and streamline the labor-intensive and time-consuming tasks in AML operations—i.e., transaction monitoring, customer due diligence, and the generation of intelligence and suspicious activity reports—that traditionally require significant human intervention and are prone to errors.

A major challenge in transaction monitoring and screening, for example, is the high number of false positive alerts generated by existing systems. Generative AI providers assert their solutions can drastically reduce these false positives by more accurately distinguishing between legitimate and suspicious transactions, thereby saving time and resources.