Model Risk Management for AI Agents in AML
A practical framework for classifying, validating and governing AI agents in AML workflows.
This guide helps risk, compliance and technology teams define the right governance approach based on how an agent is used, the decisions it influences and how much the institution relies on its outputs.
What’s inside the guide:
The state of AI governance in 2026: Get a closer look at how AI adoption, regulatory expectations and governance requirements are shifting in 2026
A four-factor classification test: Determine whether an AI agent should be governed as a model or a tool based on its autonomy and role in the control environment
A map of model risk across the workflow: Understand how risk enters through input data, AI analysis, policy decisioning and system changes
Five areas of AI agent validation: Learn how to validate conceptual soundness, data lineage, outcomes, change controls and ongoing performance of AI workflows
A vendor evaluation checklist: Ask the right governance questions during initial screening, pre-contract diligence and ongoing oversight
Common governance pitfalls: Identify governance gaps that can make AI systems harder to validate, monitor and defend later
The demand for AI in AML is growing stronger. 43% of risk and compliance executives see AML alert investigation as the use case with the greatest potential for agentic AI, while 75% rank AI for detecting financial crime among their top three tools for preventing financial crime.
Build a defensible approach to AI agent governance.
