How to provide auditable regulator friendly explanations for credit risk models?

[Victoria62]

I'm a data scientist at a financial services firm, and we're deploying a new machine learning model for credit risk assessment. Our compliance team is insisting we implement robust explainable AI techniques to justify individual decisions, not just overall model performance. I'm familiar with SHAP and LIME, but I'm struggling with how to translate their outputs into clear, actionable reasons for a denial that would satisfy both regulators and customers. For others in regulated industries, what frameworks or tools have you used to generate compliant, auditable explanations for complex ensemble models? How do you balance the need for transparency with protecting proprietary model details, and have you found that using inherently interpretable models like decision trees is a necessary trade-off for gaining regulatory approval?

[JeffreyW]

Here are several practical replies you could post. They mix concise guidance with concrete tools and examples, and they keep a realistic, non-legalistic tone for a forum setting.

Reply 1 (practical pipeline): In regulated credit decisions, build an explanation pipeline that surfaces both local (per decision) and global (model behavior) explanations. For the local part, use SHAP/TreeSHAP or DiCE for counterfactuals to answer “why was this denial?” in plain language, e.g., “your debt-to-income ratio and recent delinquencies were the main drivers.” For global explanations, provide a high-level feature importance and risk-factor categories (not exact equations). Maintain an auditable trail with model/version IDs, data provenance, and the exact explanation surface shown to the user. Use a model-card-like document (and a data-sheet) so regulators can inspect inputs, training data assumptions, and intended use. Common tools: SHAP, LIME, DALEX, WhatIf Tool, IBM AI 360 for governance. Keep the text customer-facing and non-technical.

Reply 2 (regulatory-friendly vs IP): Be explicit about what you reveal. You can provide transparent reasoning (which features contributed most) while tightly controlling internal model details (weights, architecture). A “surrogate explanation” approach works well: expose the explanation surface (feature contributions) and a high-level description of the model logic, but keep code and exact data pipelines private. Maintain an auditable schema so a regulator can retrace decisions, but avoid disclosing proprietary data or competitive specifics.

Reply 3 (interpretable models): You dont have to abandon performance. Consider inherently interpretable models like Explainable Boosting Machines (EBM), GLMs with monotonic constraints, or small decision-tree ensembles that are designed for interpretability. They can meet explainability requirements without sacrificing too much accuracy. You can still use black-box models but pair them with robust explanations and governance.

Reply 4 (frameworks and governance): Adopt a lightweight explainability framework: two layers (local + global) + governance artifacts (model card, data sheet, risk rationale). Use NIST AI RMF as a structure for risk management, plus regulatory guidelines under ECOA/regulatory regimes. Implement an Explainability/Model Risk Committee, keep audit logs, version control for models, and regular internal/external audits.

Reply 5 (practical starter steps): Quick-start plan: 1) catalog features and prepare SHAP values for a representative sample; 2) implement a counterfactual generator (DiCE) for per-case explanations; 3) draft customer-facing templates for denial reasons that align with ECOA-like requirements; 4) build a simple model-card and data-sheet; 5) pilot with a subset of decisions and measure how regulators respond. If you want, I can share a one-page checklist and a starter template for a model card.