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Original Research Article

Comparing SVR, Random Forest, and XGBoost in ESG-Integrated Credit Risk Modeling: Evidence from Kenyan Banks

Olal Darick, Rangita Apaka

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Olal Darick Corresponding Author

Department of Mathematics, Statistics and Actuarial Sciences, Maseno University, Kenya.

Correspondence: olaldarick@gmail.com

Received
13 Aug 2026
Published
10 Sep 2026

Abstract

This research paper presents a comprehensive comparison of three machine learning algorithms for credit risk assessment when integrated with Environmental, Social, and Governance (ESG) factors. The study uses real-world data from Kenyan banks over a decade (2015-2024) to determine which algorithm provides the most accurate predictions and which ESG factors are most influential in credit risk assessment. Purpose: This study compares the predictive performance of three machine learning algorithms--Support Vector Regression (SVR), Random Forest (RF), and XGBoost--for credit risk assessment incorporating Environmental, Social, and Governance (ESG) factors. Design/Methodology/Approach: Using a balanced panel dataset of 15 Kenyan commercial banks from 2015-2024 (150 observations), we evaluate model performance using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R-squared), and Mean Absolute Percentage Error (MAPE). Findings: XGBoost significantly outperforms both Random Forest and SVR, achieving near-perfect predictive accuracy (R-squared = 0.998, RMSE = 0.0013, MAPE = 1.05%). Random Forest ranks second (R-squared = 0.946), while SVR exhibits the lowest performance (R-squared = 0.912). Environmental factors demonstrate the strongest inverse relationship with default probability (r = -0.647). Practical Implications: Financial institutions should prioritize gradient boosting methods for ESG-integrated credit risk models. Environmental performance metrics warrant greater weight in credit assessment frameworks. Originality/Value: This study provides the first comprehensive comparison of SVR, Random Forest, and XGBoost specifically for ESG-integrated credit risk modeling in an emerging market banking context.

Keywords: ESG, Credit Risk, Machine Learning, XGBoost, Random Forest, Support Vector Regression, Probability of Default, Banking

How to Cite

APA

Darick, O., & Apaka, R. (2026). Comparing SVR, Random Forest, and XGBoost in ESG-Integrated Credit Risk Modeling: Evidence from Kenyan Banks. Journal of Contemporary Academic Research and Methodologies, 1(7). https://doi.org/10.5281/zenodo.22683898

MLA

Darick, Olal, and Rangita Apaka. "Comparing SVR, Random Forest, and XGBoost in ESG-Integrated Credit Risk Modeling: Evidence from Kenyan Banks." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 7, 2026. DOI: https://doi.org/10.5281/zenodo.22683898

Chicago

Darick, Olal, and Rangita Apaka. "Comparing SVR, Random Forest, and XGBoost in ESG-Integrated Credit Risk Modeling: Evidence from Kenyan Banks." Journal of Contemporary Academic Research and Methodologies 1, no. 7 (2026). https://doi.org/10.5281/zenodo.22683898

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Metadata
ISSN 3139-7247
Tracking ID JCARM_AUG_26_034
Article No. 013
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Article Info
Journal JCARM
Volume Vol 1, No 7
Year 2026
Type Original Research Article
Licence CC BY-NC-SA 4.0
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