Application of explainable AI and uncertainity quantification in credit risk assessment

Abstract

Credit risk modelling is essential for assessing the likelihood of borrower default and supporting informed lending decisions. Despite advances in predictive algorithms, challenges remain in ensuring model transparency, reliability, and robustness to uncertain inputs. This study investigates the integration of explainable AI (XAI) and uncertainty quantification (UQ) to enhance both interpretability and confidence in credit risk predictions. Three modelling approaches—Logistic Regression, Random Forest, and XGBoost—were evaluated using the Home Equity (HMEQ) dataset, with performance assessed on predictive accuracy, probability calibration, interpretability, and uncertainty handling. Ensemble methods achieved superior predictive performance, exceeding 98% accuracy and yielding near-perfect AUC scores above 0.999, whereas Logistic Regression exhibited substantially lower performance. Calibration analysis revealed a discrepancy between accuracy and probabilistic reliability: Random Forest, despite high accuracy, produced less well-calibrated predictions (ECE = 0.0475), while XGBoost achieved both strong predictive performance and reliable confidence estimates (ECE = 0.0117). Entropy-based uncertainty quantification identified instances where the model’s predictions carried high doubt, effectively highlighting challenging cases. SHAP and LIME consistently identified DELINQ, DEROG, and DEBTINC as primary drivers of default risk, aligning with established financial risk logic. By combining SHAP, LIME, and entropy-based UQ, this study proposes a unified framework that enhances interpretability, supports regulatory compliance, and increases trust in automated lending systems, emphasising the importance of reliable confidence alongside predictive accuracy.