Credit rating prediction with ESG data using data mining methods

Murat Doğan (), Muhammed Aslam Chelery Komath () and Özlem Sayilir ()
Additional contact information
Murat Doğan: Manisa Celal Bayar University, IIBF
Muhammed Aslam Chelery Komath: Anadolu University
Özlem Sayilir: Anadolu University

Future Business Journal, 2025, vol. 11, issue 1, 1-14

Abstract: Abstract The development of an adequate credit rating model and credit risk prediction represents a difficult challenge for researchers. Considering the theoretical importance of corporate sustainability performance in the credit rating process, we aimed to develop a data mining model to predict the credit rating of companies, by incorporating ESG data to traditional accounting and financial measures with a sample of 6622 firms. For this purpose, four different classification and regression methods were utilized, i.e., regression analysis (RA), generalized linear model (GLM), CHAID tree analysis (CTA), and artificial neural networks (ANNs). The comparison of the fitting methods shows that CTA is the most appropriate method for modeling SmartRatios credit rating predictions, since it has the highest correlation coefficient (R) and the lowest relative error. Leverage (LEV) and profitability (ROA) emerged as the most important variables across all methods to predict the credit rating. The ESG pillars, environmental (E), social (S), governance (G), and ESG controversies (C) present a mixed landscape of importance values across the models. Our findings revealed that each component of ESG, environmental (E), social (S), and governance (G), contributes uniquely to predicting credit risk, which emphasizes the significant potential of integrating ESG data to the traditional financial performance indicators to enhance the accuracy and comprehensiveness of credit rating predictions.

Keywords: Credit rating; Credit risk; Corporate sustainability; ESG; ESG controversies (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1186/s43093-025-00490-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00490-1

Ordering information: This journal article can be ordered from
https://fbj.springeropen.com/

DOI: 10.1186/s43093-025-00490-1

Access Statistics for this article

Future Business Journal is currently edited by Soad Kamel Rizk and Hayam Wahba

More articles in Future Business Journal from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().