Enhancing ML Models Interpretability for Credit Scoring

Transparency and performance don't have to be mutually exclusive in credit decisions. The paper demonstrates that a simpler, interpretable model using only 10 features achieved comparable predictive performance to a complex black-box model while reducing features by 88.5%, suggesting banks can meet regulatory requirements without sacrificing accuracy in default prediction.

AI explanation tools can guide the creation of more trustworthy models. By using SHAP (a technique that explains which factors drive individual predictions) to identify the most important features first, then building transparent models from those features, the researchers show a practical pathway for converting opaque AI systems into ones regulators and customers can understand.

Regulatory compliance in banking requires rethinking how we build AI models. The paper highlights that post-hoc explanations of black-box models—the most common approach in AI research—don't fully satisfy banking regulations like Internal Ratings-Based (IRB) requirements, which demand models that are inherently transparent and auditable from the ground up.

Human expertise remains essential for responsible AI in high-stakes decisions. The research shows that combining automated feature selection with human expert input and domain knowledge checks (like correlation analysis and interaction testing) produces more robust and interpretable models, suggesting AI should augment rather than replace human judgment in credit decisions.