Journal of Risk Model Validation
ISSN:
1753-9579 (print)
1753-9587 (online)
Editor-in-chief: Steve Satchell
A model combining Optuna and the light gradient-boosting machine algorithm for credit default forecasting
Xinyong Lu, Yuchong Li, Jiaxin Wang, Xiuwei Liu and Jiahui Wei
Need to know
- The study introduces a new credit default prediction model called Opt_LightGBM to tackle the difficulties associated with analyzing real-world bank loan data.
- The paper employs the Borderline-SMOTE sampling technique to address data imbalance, helping improve the performance of the prediction model.
- The Opt_LightGBM model utilizes the Optuna parameter tuning tool to enhance the efficiency of parameter optimization and improve the predictive accuracy of the LightGBM model.
- Results show that the model achieves an accuracy of 88%, an AUC of 0.9, and both recall rate and F1 score exceeding 0.88, demonstrating its effectiveness in predicting credit default and aiding risk management.
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