A Structured Evaluation Framework for AI Tool Adoption in Regulated Financial Decisioning: Quantifying Model Risk Before Production Use

Abstract

Financial institutions increasingly place artificial intelligence tools into decisions carrying regulatory consequence, including collateral valuation, credit assessment, and tenant selection. Model risk guidance addresses tools already in use, and the April 2026 revised interagency guidance expressly excludes generative and agentic systems while narrowing its stated relevance to the largest banking organisations. A gap therefore exists in time and in coverage: legal consequence attaches at first use, while governance engages afterwards, and institutions outside supervisory scope remain fully subject to the substantive consumer protection statutes. This paper proposes a pre-adoption framework that characterises the decision a tool will influence, scores inherent risk across seven weighted dimensions, applies capped control credit to yield a residual exposure score, and maps that score to a documented adoption gate. Two veto rules and one condition rule override the arithmetic where a legal obligation is categorical rather than tradeable, which is the framework's principal design contribution: weighted additive scoring is compensatory by construction, and some obligations are not compensable. The framework is proposed, not validated. It has never been applied to a real tool, no institution has run a tool through it, no adoption decision anywhere has been informed by it, and no claim is made that using it produces better decisions than the practice it would replace. That is a hypothesis the paper sets out to be tested. The two worked examples are synthetic constructions with invented inputs, and the paper reports no results.