Evaluating an AI vendor requires more than understanding what a tool can do. It requires clear accountability, appropriate human oversight, and enough documentation to determine whether the technology can be governed within the institution’s risk management processes. This checklist gives financial institution leaders a consistent starting point for evaluating AI vendors and specific tools before they automate.
The questionnaire translates defensible AI principles into practical questions for vendor evaluation. It covers how to define the business use case, identify responsibilities, assess data and model risks, evaluate explainability and human review, monitor performance after implementation, and document a decision that can support internal review, audits, and examinations.
You will learn:
- Questions to ask about the business use case, vendor accountability, explainability, human oversight, and data governance
- How to assess model updates, hallucinations, unreliable outputs, risk controls, monitoring, and model drift
- What to document about vendor and institution responsibilities, human review, implementation, and ongoing oversight to support a defensible purchase decision
