Research on credit default prediction method based on LightGBM and SHAP
Machine learning methods have shown promising results in the credit domain;however,their application is constrained by a lack of interpretability.To enhance credibility and transparency,and overcome the opacity inherent in"black box"models,a credit default prediction model based on the LightGBMalgorithm is established.Additionally,the SHAP algorithm is employed to elucidate the model's outcomes.The findings indicate superior performance of the proposed model,achieving an impressive prediction accuracy of 88.61％.Furthermore,SHAP algorithm interpretations reveal the significance of factors,such as"Credit-Mix""Outstanding_Debt"and"Total_EMI_per_month"in influencing credit decisions.
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