Credit Risk Analysis using Explainable Artificial Intelligence

The proposed research focuses on enhancing the interpretability of risk evaluation in credit approvals within the banking sector. This work employs LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to provide explanations for individual predictions: LIME approximates the model locally with an interpretable model, while SHAP offers insights into the contribution of each feature to the prediction through both global and local explanations. The research integrates gradient boosting algorithms (XGBoost, LightGBM) and Random Forest with these Explainable Artificial Intelligence (XAI) techniques to present a more comprehensible framework. The results demonstrate how interpretability methods such as LIME and SHAP enhance the transparency and trustworthiness of machine learning models, which is crucial for applications in credit risk evaluation.