The Future of Foundation Models in Predicting Climate-Related Risks in the Insurance Sector: A Case Study in Louisiana
Keywords: Supervised learning, insurance, climate change, foundation models
TL;DR: This research proposes incorporating foundation models into climate risk assessment and insurance modeling to gain deeper insights into the impact of climate change on insurance premiums and the disparities in equal access to affordable insurance.
Abstract: Climate change continues to pose a significant threat to the world, contributing to the increase in frequency and severity of natural disasters. This escalating risk directly impacts the U.S. homeowners' insurance industry, leading to higher premiums and reduced coverage availability in vulnerable areas of the country. Traditional methods of modeling insurance premiums often fail to fully account for the multifaceted effects of climate risk, insurer risk assessments, and the spatial-temporal variance of climate impacts. This study focuses on Louisiana, a state particularly vulnerable to climate change, to illustrate these gaps. We propose incorporating foundation models into climate risk assessment and insurance modeling to gain deeper insights into the impact of climate change on insurance premiums and the disparities in equal access to affordable insurance. This approach supports greater climate resilience aligning with the UN Sustainable Development Goal 13 to strengthen adaptive capacity to climate hazards.
Submission Number: 2
Loading