Loan Default Prediction Based on XGBoost

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Year:
2023
Type of Publication:
Article
Keywords:
Loan Default Prediction, IV Information, Model Comparison, XGboost
Authors:
Xiaoqian Peng; Jingyi Zhang; Xu Cheng; Yifan Huang; Zhouqiang Zheng
Journal:
IJISM
Volume:
11
Number:
1
Pages:
20-29
Month:
January
ISSN:
2347-9051
Abstract:
Aiming at the credit risk loss of commercial banks caused by loan default, this paper establishes an integrated learning model to predict customer default based on the loan record data of credit platform to reduce credit risk. According to the characteristics of unbalanced loan data categories and high feature dimensions, this paper cleans the data and uses IV information to screen the factors, so as to select the factors with strong predictive ability. Then, on the basis of comparing various models, this paper chooses the best XGboost model to construct the loan default prediction model and evaluate the model. The analysis found that the indicators of XGboost are high, indicating that the performance of the model is good and can be used to accurately predict loan defaults. This provides a reference for commercial banks and other loan platforms to provide credit products to borrowers.

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