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An ESG-Modified Credit Risk Assessment Model Based on Decision Tree Model

In: Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

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  • Haiyue Chen

    (South China University of Technology, School of Economics and Finance)

Abstract

How to accurately assess corporate credit risk is a very important issue for financial institutions such as banks. Especially after the 2008 financial crisis, the discussion of credit ratings has gained more and more attention, and various evaluation models have been proposed to predict credit risk for enterprises. This paper is different from the traditional evaluation system, relying only on financial indicators. In this research, the ESG performance that reflects the sustainable development ability of the enterprise is included in the company's evaluation system for analysis. In addition, considering the inherent differences in ESG performance between different industries, a new indicator—relative ESG scoring is created to eliminate industry impact and obtain a more fair ESG evaluation. Then, this paper collects the data of 51 companies in different industries, establishes three decision tree models for comparison, adds ESG performance and relative ESG scoring in turn, and finally gets the model prediction accuracy rates: 71.43%, 80.95%, and 85.71%, respectively. After analyzing the results, it is proved that the addition of ESG performance and the newly created indicator can significantly improve the prediction accuracy of the credit risk assessment model, which provides a new idea for improving the index system of the credit risk assessment model.

Suggested Citation

  • Haiyue Chen, 2022. "An ESG-Modified Credit Risk Assessment Model Based on Decision Tree Model," Advances in Economics, Business and Management Research, in: Yushi Jiang & Yuriy Shvets & Hrushikesh Mallick (ed.), Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), pages 15-21, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-036-7_4
    DOI: 10.2991/978-94-6463-036-7_4
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