A Deep Learning-Copula Framework for Climate-Related Home Insurance Risk

TL;DR

This paper introduces a novel deep learning and copula-based framework to analyze how precipitation influences home insurance claims amid increasing climate-related weather events.

Contribution

It combines deep neural networks with copula models to better understand and predict weather-related insurance risks, a novel integration for this application.

Findings

Effective modeling of precipitation impact on claims

Improved risk assessment accuracy

Case study validation in Canadian Prairies

Abstract

Extreme weather events are becoming more common, with severe storms, floods, and prolonged precipitation affecting communities worldwide. These shifts in climate patterns pose a direct threat to the insurance industry, which faces growing exposure to weather-related damages. As claims linked to extreme weather rise, insurance companies need reliable tools to assess future risks. This is not only essential for setting premiums and maintaining solvency but also for supporting broader disaster preparedness and resilience efforts. In this study, we propose a two-step method to examine the impact of precipitation on home insurance claims. Our approach combines the predictive power of deep neural networks with the flexibility of copula-based multivariate analysis, enabling a more detailed understanding of how precipitation patterns relate to claim dynamics. We demonstrate this methodology through a case study of the Canadian Prairies, using data from 2002 to 2011.