Evaluation of Customer Default Risk in Tianchi Financial risk

Authors

  • Chuhan Su

DOI:

https://doi.org/10.61173/ry2b9753

Keywords:

default risk, PCA, logistic regression model

Abstract

Based on the PCA(principal component analysis) and logistic regression model, this essay evaluates the default Risk of the borrowers’ information in the Tianchi Financial Risk dataset. The research finds that the default rate is the main factor affecting Tianchi Financial Risk. Combining borrowers’ credit grades with factors influencing the default rate, the logistic regression analysis is conducted. It is concluded that individuals with a step above D have a high risk of default, whereas those with a grade below D have low-risk defaults.

References

[1] Zhang Ruizhi, Yang Guowei and Xu Quan, a classification audit model of bank loan risk based on self-coding clustering algorithm. Audit Observation, 2022(03): pp. 77-81.

[2] Gu Huiying and Yao Zheng, A study on the Influencing Factors of Borrower Default Risk in P2P online lending Platform -- A case study of WDW. Shanghai Economic Research, 2015(11): 37-46.

[3] Li Xianhang, Machine Learning-based Explainable Credit Risk Scoring and Default Prediction, 2022, Southwestern University of Finance and Economics. Page 61.

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Published

2023-10-21

Issue

Vol. 1 No. 3 (2023): Volume 1, Issue 3

Section

Articles

License

Copyright (c) 2023 by the authors.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.