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Academic Journal of Computing & Information Science, 2022, 5(10); doi: 10.25236/AJCIS.2022.051009.

Research on Personal Credit Risk Assessment Based on Combination Weight and Shap Interpretable Machine Learning


Xuehan Peng

Corresponding Author:
Xuehan Peng

School of Statistics, Capital University of Economics and Business, Beijing, 100000, China


With the continuous promotion of e-commerce platform installment payment and P2P credit platform, personal credit risk assessment is becoming more and more important for e-commerce and users Taking the data of Tianchi competition platform as a sample, this paper constructs an index system for personal credit risk assessment, and calculates the combined weight of each index through the critical method and entropy weight method, and weights it to obtain three factors: basic information, credit information and lending behavior information. Based on the quantified three indicators and the unbalanced sample algorithm, XGBoost and lightGBM are used to predict credit risk, it is found that the performance of these two methods is basically the same. This paper uses the SHAP-values interpretable machine learning method to explain the importance of these three factors. The empirical results show that the accuracy of XGBoost and lightGBM is higher than 80%, and the order of the importance of the three factors is: "basic information" is higher than "lending behavior information" and higher than "credit information". Finally, this paper puts forward relevant suggestions for the stable development of enterprise risk control and credit industry.


credit risk assessment, portfolio weight, SHAP-values, machine learning

Cite This Paper

Xuehan Peng. Research on Personal Credit Risk Assessment Based on Combination Weight and Shap Interpretable Machine Learning. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 10: 54-59. https://doi.org/10.25236/AJCIS.2022.051009.


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