A Study on Application of Explainable AI for Credit Risk Management of an Individual

This study investigates the potential of Explainable AI (XAI) to enhance credit risk assessment. By employing machine learning models like Logistic Regression and Decision Tree, coupled with XAI techniques LIME and SHAP, the study aim to identify key factors influencing loan default risk. Our analysis, based on both primary and secondary datasets, reveals that XAI can provide valuable insights into model predictions, leading to more transparent and equitable decision-making in the credit lending process. The study's findings highlight the effectiveness of XAI in improving the reliability and interpretability of credit risk assessments