Model Governance for ML-Driven Underwriting

Machine-learning underwriting can improve loss curves and approval rates simultaneously. It can also produce disparate impact that is difficult to defend without disciplined governance.

Our framework covers model inventory, development standards, challenger review, ongoing monitoring, and adverse action notice generation tied to genuine reason codes.

Examiners — and sponsor banks — increasingly expect this posture as table stakes.