Default Prediction of Automobile Credit Based on Support Vector Machine


Ying Chen, Ruirui Zhang, Journal of Information Processing Systems Vol. 17, No. 1, pp. 75-88, Feb. 2021  

https://doi.org/10.3745/JIPS.04.0207
Keywords: Credit Loan, Default prediction, Random Forest, Support Vector Machine
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Abstract

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.


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Cite this article
[APA Style]
Chen, Y. & Zhang, R. (2021). Default Prediction of Automobile Credit Based on Support Vector Machine. Journal of Information Processing Systems, 17(1), 75-88. DOI: 10.3745/JIPS.04.0207.

[IEEE Style]
Y. Chen and R. Zhang, "Default Prediction of Automobile Credit Based on Support Vector Machine," Journal of Information Processing Systems, vol. 17, no. 1, pp. 75-88, 2021. DOI: 10.3745/JIPS.04.0207.

[ACM Style]
Ying Chen and Ruirui Zhang. 2021. Default Prediction of Automobile Credit Based on Support Vector Machine. Journal of Information Processing Systems, 17, 1, (2021), 75-88. DOI: 10.3745/JIPS.04.0207.