Leveraging Machine Learning for Enhanced Climate Risk Compliance in Financial Institutions

Abstract

The present study investigates the incorporation of machine learning (ML) technologies in financial institutions to tackle the increasing requirements of climate risk regulatory compliance. With the rising scrutiny from regulatory bodies regarding financial institutions’ climate risk management practices, there is a growing need for sophisticated analytical capabilities to evaluate, forecast, and track climate-related risks effectively. This paper outlines the function of machine learning (ML) in improving the accuracy of climate risk scenario analysis, stress testing, and compliance monitoring. As a result, it enables more knowledgeable decision-making and strategic planning. Combining technical analysis and industry-specific knowledge demonstrates how machine learning applications can go beyond conventional analytical limits. This gives financial institutions a solid framework to navigate the challenges of the changing regulatory environment related to climate risks. The results highlight machine learning (ML) ‘s capacity to optimize compliance procedures and facilitate proactive risk management approaches that align with worldwide sustainability goals.