Explainable AI Framework for Credit Risk Prediction Using Ensemble Learning

Abstract

Credit risk prediction plays a crucial role in modern financial institutions by supporting lending decisions and minimizing potential losses arising from borrower defaults. Traditional credit scoring methods often struggle to capture complex patterns within large and dynamic financial datasets, leading to reduced predictive performance. To address these limitations, this study proposes an Explainable Artificial Intelligence (XAI) framework integrated with ensemble learning techniques for accurate and transparent credit risk prediction. The proposed framework combines multiple machines learning models, including Random Forest, XGBoost, LightGBM, and CatBoost, within a stacking ensemble architecture to improve classification performance. To enhance model interpretability, SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are incorporated to provide both global and local explanations of credit decisions. Additionally, the framework includes a fairness assessment module to identify and mitigate potential biases in lending outcomes. Experimental evaluation demonstrates that the proposed approach achieves superior predictive accuracy, robustness, and transparency compared with traditional machine learning and statistical methods. The integration of explain ability and ensemble learning enables financial institutions to make trustworthy, accountable, and regulatory-compliant credit decisions while maintaining high predictive performance. The proposed framework contributes to the development of responsible AI solutions for next-generation financial risk management systems.