Harnessing Machine Learning for Comparative Analysis of Bottom-Up and Top-Down Approaches in Climate Credit Risk Modeling

Abstract

This research paper examines the utilization of machine learning methodologies to conduct a comparative analysis between bottom-up and top-down approaches regarding climate credit risk modeling. The significance of evaluating climate-related credit risks is increasingly acknowledged by financial institutions, leading to a crucial decision between granular, asset-level models and aggregate, portfolio-level models. Our proposed framework utilizes a combination of supervised and unsupervised learning algorithms to methodically examine and assign attributes to distinctions between these two modeling paradigms. By using predictive models and implementing clustering and dimensionality reduction techniques, this study showcases the potential of machine learning in augmenting comprehension of model disparities and bolstering the resilience of climate credit risk evaluations. The potential of this approach for model validation and benchmarking is exemplified by a case study conducted on the oil and gas industry. In conclusion, we will address optimal strategies, potential avenues for future research, and the significance of integrating bottom-up and top-down
approaches to achieve comprehensive risk management.