XAI - Credit Risk Analysis

This paper delves into the integration of Explainable AI (XAI) techniques with machine learning models for credit risk classification, addressing the critical issue of model transparency in financial services. We experimented with various models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Artificial Neural Networks (ANN), on real-world credit datasets to predict borrower risk levels. Our results show that while ANN achieved the highest accuracy at 95.3%, Random Forest followed closely with 95.23%. Logistic Regression also performed strongly with an accuracy of 94.68%, while XGBoost and LightGBM delivered slightly lower accuracies of 94.4% and 94.37%, respectively. However, the superior accuracy of these complex models, particularly ANN, comes with a trade-off: reduced transparency, making it difficult for stakeholders to understand the decision-making process. To address this, we applied XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Modelagnostic Explanations) to provide clear and understandable explanations for the predictions made by these models. This integration not only enhanced model interpretability but also built trust among stakeholders and ensured compliance with regulatory standards. This study illustrates how XAI serves as an effective mediator between the precision of sophisticated machine learning algorithms and the demand for clarity in evaluating credit risk. XAI offers a well-balanced method for managing risk in finance, harmonizing the need for both accuracy and interpretability.