Explainable AI based LightGBM prediction model to predict default borrower in social lending platform

Abstract

This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. The dataset used in this study was obtained from Lending Club and consisted of various borrower characteristics and loan features. The proposed model not only provides high accuracy (0.87) in predicting defaulted borrowers, but also offers an explanation of the factors that contribute to the prediction. The model interpretability is facilitated through LIME and SHAP values, where SHAP values provide insights into the feature importance for the prediction. The outcome shows that the proposed model outperforms traditional approaches and delivers valuable insights for lending decision-making. The proposed model can be useful for lenders and regulators in the lending industry to improve decision-making processes and mitigating risk. Moreover, the XAI approach enables transparency and accountability in the decision-making process, making it more understandable and trustworthy for stakeholders.

Keywords

• Social lending
• Credit risk prediction
• Machine learning
• Explainable AI
• Recursive feature elimination