A Bayesian deep learning method based on loan default rate detection

ShaSha Liu; MingXi Guan; JinKun Ji; Yan Li; MengLu Wang; HuiMin Zhu

doi:10.1117/12.2678879

8 May 2023 A Bayesian deep learning method based on loan default rate detection

ShaSha Liu, MingXi Guan, JinKun Ji, Yan Li, MengLu Wang, HuiMin Zhu

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 1263516 (2023) https://doi.org/10.1117/12.2678879
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

The probability of loan default is one of the most important activities in the financial sector. In this context, lenders issue loans to borrowers in exchange for a promise to repay the principal and interest. In this paper, we use a Bayesian deep learning model to build a predictive model for high performance loan default probability. In the practical case of loan default modeling, we cannot use clean and complete data. Some of the potential problems we inevitably encounter are missing values, incomplete categorical data and irrelevant features, thus requiring data pre-processing. In this paper, we train our model by analyzing the Kaggle Lending Club loan dataset from 2007 to the third quarter of 2017. The results show that our model has more than 96% accuracy. Compared with popular classification models, our model has higher performance.

Citation Download Citation

ShaSha Liu, MingXi Guan, JinKun Ji, Yan Li, MengLu Wang, and HuiMin Zhu "A Bayesian deep learning method based on loan default rate detection", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 1263516 (8 May 2023); https://doi.org/10.1117/12.2678879

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