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Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022, Zhengzhou, China

Research Article

Loan Default Prediction Based on Machine Learning Methods

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  • @INPROCEEDINGS{10.4108/eai.2-12-2022.2328740,
        author={Yuran  Zhou},
        title={Loan Default Prediction Based on Machine Learning Methods},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={BDEIM},
        year={2023},
        month={6},
        keywords={loan default prediction; machine learning; extreme gradient boosting tree},
        doi={10.4108/eai.2-12-2022.2328740}
    }
    
  • Yuran Zhou
    Year: 2023
    Loan Default Prediction Based on Machine Learning Methods
    BDEIM
    EAI
    DOI: 10.4108/eai.2-12-2022.2328740
Yuran Zhou1,*
  • 1: Beijing Normal University
*Contact email: 201911011130@mail.bnu.edu.cn

Abstract

Loan default prediction helps institutions predict whether a borrower will default on a loan and decide whether to lend, thereby reducing losses. We investigate the performance of different machine learning models in predicting customers' loan defaults. Four machine learning models: Logistic Regression, Decision Tree, Random Forest and XGBoost, are used to predict the loan default, considering dependent variables such as the value of all the assets, living status and yearly income. Our results show that XGBoost is the best model with the highest Recall of 0.35 and AUC of 0.832. This study is expected to help lending institutions identify potential default users, and then decide on who to accept or refuse for a loan.

Keywords
loan default prediction; machine learning; extreme gradient boosting tree
Published
2023-06-14
Publisher
EAI
http://dx.doi.org/10.4108/eai.2-12-2022.2328740
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