Enhancing Financial Liquidity Risk Management through Machine Learning - Navigating ClimateChange Impacts

Abstract

Climate change's escalating severity presents substantial threats to worldwide financial stability, explicitly managing liquidity risk within the financial industry. This study investigates the incorporation of Machine Learning (ML) methodologies to enhance the resilience of financial institutions in mitigating liquidity risks arising from market disruptions caused by climate change. This study showcases the utilization of machine learning's predictive solid abilities to analyze extensive collections of climate, financial, and socio-economic data to anticipate and address forthcoming liquidity difficulties. This methodology not only improves conventional risk management tactics but also establishes a foundation for a more robust financial system that can effectively navigate the intricacies of climate change. This paper emphasizes the crucial role of technology in constructing flexible and resilient financial systems in the presence of environmental uncertainties by examining the technical aspects of machine learning applications and conducting a detailed analysis of liquidity risk management.