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

Asim K. Dey

TL;DR

The paper addresses climate-induced variability in home insurance risk driven by precipitation. It combines a deep neural network that maps precipitation covariates to weekly claim counts with a copula-based ensemble to integrate six climate-model projections, enabling multivariate tail-risk assessment. Marginals are modeled as Negative Binomial type I and dependence is captured by a six-variate Gumbel copula, yielding tail probabilities for extreme claim events. A Canadian Prairies case study shows City B exhibits heavier tails and higher tail risk than City A under 2021–2030 projections, illustrating the method's utility for risk quantification and pricing under climate-model uncertainty. The work highlights practical insurer applications and outlines future work to include additional drivers and broader spatial analysis.

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.

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

TL;DR

The paper addresses climate-induced variability in home insurance risk driven by precipitation. It combines a deep neural network that maps precipitation covariates to weekly claim counts with a copula-based ensemble to integrate six climate-model projections, enabling multivariate tail-risk assessment. Marginals are modeled as Negative Binomial type I and dependence is captured by a six-variate Gumbel copula, yielding tail probabilities for extreme claim events. A Canadian Prairies case study shows City B exhibits heavier tails and higher tail risk than City A under 2021–2030 projections, illustrating the method's utility for risk quantification and pricing under climate-model uncertainty. The work highlights practical insurer applications and outlines future work to include additional drivers and broader spatial analysis.

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.
Paper Structure (7 sections, 5 equations, 5 figures, 2 tables)

This paper contains 7 sections, 5 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Data
  3. A two-step procedure for quantifying insurance risk
  4. Deep neural network models for weekly insurance claims
  5. Ensemble predicted insurance risks
  6. Results
  7. Conclusion

Figures (5)

  • Figure 1: Observed weekly precipitation and number of claims (2002-2011) in City A and City B.
  • Figure 2: City A: Density curves of the predicted weekly average claim frequency in 2021–2030 under different climate models.
  • Figure 3: City B: Density curves of the predicted weekly average claim frequency in 2021–2030 under different climate models.
  • Figure 4: Joint probabilities of high weekly average number of claims in 2021--2030 in City A and City B using lognormal marginals and Gumbel copula.
  • Figure 5: A comparison of the risk of the high weekly average number of claims in City A and City B.