Analysis of loan defaults based on data mining

Zhaozhao Ma

doi:10.1117/12.2656843

13 January 2023 Analysis of loan defaults based on data mining

Zhaozhao Ma

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125100R (2023) https://doi.org/10.1117/12.2656843
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Due the redundancy of a large number of user data, how to use data mining to efficiently and scientifically establish the loan default model is an important issue for reducing bank loan loss. In this paper, logistic regression is adopted to identify the nine significant influencing factors of loan defaults. Further, differences among multiple groups of different samples are studied with the methods contrast analysis and Analysis of Variance (ANOVA). Through factor analysis, eight common factors are extracted, which could explain 80% all of the original variables. This study can provide some implications for banks to use when reviewing loan applications.

Citation Download Citation

Zhaozhao Ma "Analysis of loan defaults based on data mining", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125100R (13 January 2023); https://doi.org/10.1117/12.2656843

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