Explainability of Machine Learning Models in Credit Risk Management

Abstract

The effective management of credit risk is a critical challenge for financial institutions, with accurate assessment of loan default risk playing a central role in maintaining financial stability. Machine Learning (ML) techniques have become increasingly prevalent in credit risk assessment due to their ability to capture complex patterns in borrower behavior and improve predictive accuracy. However, the lack of interpretability of many advanced ML models, such as Random Forest, XGBoost, and Neural Networks, raises concerns regarding transparency, fairness, and accountability in decision-making, particularly in high-stakes environments where regulatory compliance and ethical considerations are paramount. This study seeks to bridge the gap between predictive accuracy and interpretability by applying two post-hoc, model-agnostic explainability techniques Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to evaluate five commonly used ML models Logistic Regression, Multivariate Adaptive Regression Splines (MARS), Neural Networks, Random Forest, and XGBoost. Using an open-access Kaggle dataset, the study examines both the predictive performance and the interpretability of these models, with a particular focus on the trade-offs between high accuracy and model transparency. The results highlight a clear trade-off while ensemble models like XGBoost and Random Forests exhibit superior accuracy, particularly in predicting low-risk borrowers, they struggle with detecting high risk applicants and lack the interpretability required for transparent decision-making. Simpler models, such as Logistic Regression, offer greater transparency and are more effective in identifying high-risk cases but sacrifice predictive accuracy. Neural Networks strike a balance, providing better accuracy than linear models while maintaining moderate sensitivity to high-risk applicants. By leveraging SHAP and LIME, this research enhances model transparency, offering both global insights into risk factors and local instance-level explanations for individual predictions, which can aid stakeholders such as financial institutions, regulators, and applicants in making more informed, fair, and accountable credit decision