Enhancing Financial Risk Management: Utilizing Machine Learning in Climate Risk Model Benchmarking

Abstract

This paper investigates the convergence of machine learning (ML) and climate risk modeling in the financial industry, focusing on applying ML methods to improve the validation of climate risk models. In light of the growing significance of precisely evaluating and controlling climate-related risks within the financial sector, conventional models demonstrate their inadequacies in confronting the intricacies and unpredictability of climate change. By conducting an extensive analysis of bottom-up and top-down modeling methodologies, this research emphasizes using machine learning algorithms to bolster the reliability of financial risk assessments, control non-linearities, and enhance predictive accuracy. The innovative applications of ML in scenario analysis, stress testing, and model performance evaluation on out-of-sample data are explored, along with the difficulties of model validation. This paper enhances the ongoing discussion on improving frameworks for climate risk modeling by highlighting emerging trends and best practices in integrating physical and transition risk factors. The results emphasize the critical importance of machine learning in revolutionizing financial risk management approaches to more effectively navigate the uncertainties associated with climate change.