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

Haiyue Chen ()
Additional contact information
Haiyue Chen: South China University of Technology, School of Economics and Finance

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 15-21 from Springer

Abstract: 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.

Keywords: Credit risk assessment; ESG scoring; Relative ESG scoring; Decision tree model (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_4

Ordering information: This item can be ordered from
http://www.springer.com/9789464630367

DOI: 10.2991/978-94-6463-036-7_4

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().