Explainable AI Frameworks for Credit Risk Assessment in Financial Technology Applications

Abstract

Machine learning models for credit scoring achieve superior predictive accuracy over traditional logistic regression, but their black-box nature creates regulatory barriers under GDPR's right to explanation, fair lending laws, and Basel III model risk management requirements. We develop XAI-Credit, an explainable AI framework that combines gradient boosting with Shapley additive explanations (SHAP), counterfactual reasoning, and monotonicity constraints to produce credit risk assessments that are accurate, interpretable, and fair. Evaluated on three real-world datasets from a European fintech lender (n=420,000), a US community bank (n=185,000), and a Southeast Asian digital bank (n=310,000), XAI-Credit achieves AUC of 0.847-0.862 while providing per-applicant explanations that satisfy regulatory audit requirements. Disparate impact analysis shows XAI-Credit reduces approval rate disparities across protected groups by 42% compared to unconstrained XGBoost, with less than 1.5% AUC degradation.