Governed AI in Credit: Five Questions Executives Should Ask
A practical framework for evaluating AI-assisted credit decisions without surrendering policy control, accountability, or evidence.
AI can accelerate document review, research, spreading, exception detection, and memo preparation. None of those benefits removes the institution’s responsibility for the decision.
The right executive evaluation therefore begins with control design: where AI is allowed to act, what it must show, when a human must intervene, and how performance will be monitored.
1. What is the system allowed to do?
Distinguish extraction, calculation, recommendation, workflow routing, and final approval. Each has a different risk profile and should have a separately defined authority boundary.
2. Can a reviewer trace every material claim?
A useful recommendation links facts to source evidence, calculations to formulas, and judgments to policy or stated reasoning. A fluent explanation without source-level traceability is not sufficient.
3. How are uncertainty and missing evidence handled?
The system should surface gaps, conflicts, stale data, and low-confidence conclusions. It should not convert uncertainty into artificial precision simply to complete a workflow.
4. Who can override—and what happens next?
Overrides need authority limits, rationale, visibility, and follow-up. The goal is not to eliminate expert judgment; it is to ensure that judgment becomes part of the governed record.
5. How will management know the system is improving decisions?
Measure review time, rework, exception patterns, approval consistency, override performance, and portfolio outcomes. Speed alone is an incomplete executive metric.
Governed AI is not a model with an explanation attached. It is a complete operating design that preserves evidence, policy, human authority, and management visibility.