Can a bank keep a credit model in production if it cannot explain a single denial? The answer here is no. SR 11-7, the Federal Reserve's 2011 model risk guidance, was written for logistic regression, and examiners in 2026 are treating unexplainable gradient boosting, neural network and foundation model scoring as unvalidated. Claims from Google's AI team and OpenAI about explainability tooling and reasoning gains are treated as vendor marketing until independently rerun.
You come away knowing why SHAP values describe correlation inside a model rather than a defensible decision, why surrogate models break on the edge cases that carry legal risk, why retrains make revalidation a quarterly cost, and how adverse action notices set the real bar.
0:00 Why unexplainable models fail validation
0:43 From logistic regression to gradient boosting
1:33 Do SHAP values satisfy examiners
2:17 Accuracy lift versus ongoing governance cost
3:19 Why surrogate explainer models fail
4:14 Test ten denials by hand
