Abstract
To evaluate the credit risk of small and medium-sized enterprises, banks should consider the risk level of enterprises comprehensively. Accordingly reflect the development of the enterprise, financial situation and loan repayment ability to estimate. Using Navicat and Excel in data screening, 13 quantitative independent variables were obtained based on the principle of science, comprehensiveness and large coverage. In order to avoid unnecessary interference, factor analysis is combined to reduce the dimension of data to improve the simplicity of the model. The comprehensive contribution rate and the inflextion point of gravel chart were used to select the more important influencing factors, and the Logit [1] regression model was built to judge the credit risk of smes (small and medium-sized enterprises). It is hoped that this result can provide some reference value for banks to choose loan enterprises.
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Acknowledgment
The work was supported by Tianjin Ninghe District Science and Technology Development Project Grant Qjxm202006.
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Guo, L., Zhao, W., Chen, J., Liu, Q. (2021). Quantitative Research on Credit Risk Based on PCA and Logit Regression Model. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2021. Advances in Intelligent Systems and Computing, vol 1385. Springer, Cham. https://doi.org/10.1007/978-3-030-74814-2_67
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DOI: https://doi.org/10.1007/978-3-030-74814-2_67
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