Risk and exam

Shareable reference

Reconstruct the real reasons behind an AI-touched decision

File reconstruction plus method validation so a customer notice matches what was actually scored.

A practical example

What this looks like in the business

An auto lender or claims writer used a high-variable model. Examiners ask for 30 decline files, the inputs actually scored, the principal reasons sent to the customer, and a replay that matches. The desk reconstructs a sample, tests the reason-selection method, and writes what would fail fair-lending or unfair-practices review. Opacity is not a defense. Sample checklists are not enough.

What you leave with. A reconstructable trail and the method changes needed to keep it that way.

Not this. A generic explainability memo or rewriting the notice template overnight. Calendar for this class of work is measured in weeks, not hours.

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