This study investigates whether gradient boosting models (XGBoost and LightGBM), combined with SHAP and LIME explainability, offer meaningful advantages over Logistic Regression in credit scoring. Using the German Credit dataset (n = 1,000), three models were compared through stratified five-fold cross-validation, held-out test evaluation, and pairwise McNemar’s tests. None of the three models achieved a statistically significant improvement over any other (all p > 0.05). Logistic Regression achieved the highest recall on the test set (0.800), followed by XGBoost (0.717) and LightGBM (0.567). SHAP analysis revealed consistent nonlinear risk patterns across both ensemble models, including duration threshold effects and compensatory attribute dynamics inaccessible through linear coefficients. A comparative analysis of SHAP and LIME showed broad agreement on top predictors but meaningful differences in stability and explanation structure. The findings suggest that on small, structured datasets, Logistic Regression remains the most practical production choice, while gradient boosting with SHAP serves a complementary analytical role for credit policy design.
This study compared Logistic Regression, XGBoost, and LightGBM within an explainable credit scoring framework using both SHAP and LIME. No model achieved a statistically significant predictive advantage. Logistic Regression achieved the highest recall (0.800) and remains the most practical production choice for small structured portfolios due to its native interpretability and regulatory compatibility.
SHAP analysis retains analytical value independently of model superiority, revealing consistent nonlinear risk patterns across both ensemble models. The SHAP-LIME comparison demonstrates that explanation methods offer complementary rather than redundant perspectives, with SHAP better suited for audit documentation and LIME for interpretable customer communication. Future research should extend this comparison to larger real-world datasets, incorporate macroeconomic variables, evaluate fairness, and examine interpretable ensemble architectures such as Explainable Boosting Machines.
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How to Cite This Paper
Durrusadaf Imanova, Hakan Kutucu (2026). Explainable Credit Risk Assessment: Comparing Logistic Regression, XGBoost, and LightGBM with SHAP and LIME Analysis. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
