Machine Learning Applications in Sustainable Finance: Enhancing ESG Risk Assessment and Credit Modeling

Abstract

Sustainable finance has emerged as a pivotal force in modern financial markets, integrating environmental, social, and governance (ESG) factors into investment and lending decisions. This article explores the application of machine learning (ML) techniques to enhance ESG risk assessment and credit risk modeling within sustainable finance frameworks. Drawing on recent empirical studies and market data up to 2026, we demonstrate how ML outperforms traditional models in predicting ESG-driven risks and financial outcomes. Using a dataset of corporate bonds and equity returns, we present comparative performance metrics and a table illustrating model accuracies. Our findings reveal that ML models achieve up to 15% higher AUC scores in default prediction while incorporating ESG variables, underscoring their potential to drive transition finance and financial inclusion. This study contributes to the literature by bridging ML innovation with sustainable investing, offering practical implications for policymakers and financial institutions.