In the finance sector, a company is implementing an AI system for credit risk assessment.

For an AI credit risk assessment system, PMI-style AI governance and lifecycle guidance consistently emphasizes thatdomain and data expertise must be combinedto ensure model accuracy, relevance, and reliability. In the finance context, this means involving: (1)data analysts / data scientistswho understand data structures, data quality, feature engineering, and model behavior, and (2)financial / credit risk expertswho understand regulatory constraints, lending policies, risk appetite, and real-world meaning of variables and outputs. Together, they validate that input data correctly represents customer risk profiles, that derived features reflect sound credit risk logic, and that model outputs are interpretable and aligned with institutional policies.

Options B, C, and D conflict with good AI practice described in PMI-style guidance. Focusing on SMEs “with experience in noncognitive solutions” is irrelevant to credit risk modeling. Relying on general IT staff ignores the need for specialized financial and data expertise. Selecting SMEs based on availability rather than expertise directly undermines model quality and risk control. Therefore, theeffective and expectedmethod in an AI credit risk initiative is toengage internal data analysts and financial expertsas data SMEs to support model design, validation, and ongoing monitoring.