Dynamic AML risk scoring: Adapting to evolving financial crime threats with AI
Between $800 billion and $2 trillion is laundered through the global financial system every year. This amounts to roughly 2 to 5% of global GDP, according to the United Nations Office on Drugs and Crime. Of that, law enforcement seizes less than 1%. The infrastructure detecting and mitigating the remainder is, in most institutions, a patchwork of static rules, periodic reviews, and manual alerts. To counter these threats, AML risk scoring must evolve from a static perimeter check into a dynamic, real-time assessment tool. This article examines what a genuinely dynamic AML risk scoring model looks like, why the risk-based approach demands it, and how AI is beginning to close the gap between compliance volume and compliance quality.
