Agentic Model Risk Management: Industrializing Risk Ranking, Validation, and Monitoring

About this session

Model risk management today remains highly manual, sequential, and fragmented across development, validation, and monitoring, leading to slow cycle times, inconsistent outputs, and scaling bottlenecks. We are building and operationalizing a suite of AI agents that execute end to end model risk workflows - including risk ranking, documentation generation, validation, and continuous monitoring - while keeping humans firmly in control. These agents codify domain expertise into reusable capabilities, standardize validation practices, and enable scalable, audit ready outputs across the model lifecycle. Early results show significant acceleration in validation timelines (e.g., multi week processes reduced to days or weeks), alongside meaningful efficiency gains across model risk teams.