A Framework for Integrating ESG Factors into Bank Credit Risk Models

Thesis Abstract

The increasing integration of environmental, social, and governance (ESG) considerations into banking activities demands a systematic approach to incorporating these factors into credit risk assessment models to enhance predictive accuracy and promote sustainable finance. This study aims to develop a comprehensive framework for integrating ESG factors into bank credit risk models, with specific objectives to identify key ESG indicators influencing credit risk, evaluate existing credit risk modeling techniques, and propose methodological enhancements for embedding ESG considerations into predictive models. Employing a mixed-methods research design, the study combines quantitative analysis of historical credit data with qualitative insights from industry experts, ensuring a holistic understanding of the subject. The quantitative component involves analyzing a sample of 50 commercial banks across Europe, utilizing datasets comprising 10,000 individual borrower observations collected from bank reports and credit bureaus over a five-year period (2017–2021). Data collection instruments include structured questionnaires targeting risk managers and semi-structured interviews for validation of quantitative findings. The primary analytical technique involves multiple linear regression analysis and machine learning algorithms such as random forest and support vector machines to assess the impact of ESG factors on credit default prediction accuracy. The qualitative data are analyzed through thematic analysis to identify industry-driven nuances and practical considerations for integrating ESG metrics into existing credit risk frameworks. Expected findings suggest that specific ESG indicators—such as carbon emissions, labor practices, and board diversity—are significant predictors of credit default risk beyond traditional financial metrics. Incorporating these variables into predictive models is anticipated to improve model accuracy by up to 15% as measured by Area Under the Curve (AUC) scores in Receiver Operating Characteristic (ROC) analysis. Additionally, the study hypothesizes that the application of the Stakeholder Theory and the Resource-Based View (RBV) provides a theoretical foundation for understanding how ESG factors influence credit risk, with empirical evidence supporting their relevance in a banking context. The research will culminate in a novel conceptual framework that delineates the methodological steps required for integrating ESG metrics into credit scoring systems, along with a set of standardized ESG indicators suitable for routine credit risk assessment. This study makes a significant contribution to the body of knowledge by addressing the gap concerning operational integration of ESG factors within credit risk models, which has predominantly been discussed in theoretical or macroeconomic contexts. It expands the empirical basis for banks and regulators to adopt ESG-integrated credit scoring, facilitating more sustainable lending practices and risk mitigation strategies. The findings are expected to inform policymakers on developing guidelines for ESG disclosures and assessments tailored to credit risk management, thus fostering greater transparency and accountability within financial institutions. The main conclusion underscores the importance of systematically embedding ESG factors into credit risk modeling to elevate predictive performance and promote sustainability in banking. It recommends that banks incorporate standardized ESG datasets, invest in capacity-building for risk analysts on ESG issues, and adopt machine learning techniques tailored to handle complex and high-dimensional ESG data. The study further advocates for regulatory frameworks to incentivize ESG disclosure by borrowers, and calls for future research to explore longitudinal effects and regional variations in ESG-influenced credit risk. Overall, this research provides a pioneering, empirically validated framework that advances both academic inquiry and practical application, contributing to more responsible and resilient banking practices amidst growing sustainability challenges.