[論文レビュー] A Deep Learning-Copula Framework for Climate-Related Home Insurance Risk

Asim Kumer Dey|arXiv (Cornell University)|Jan 17, 2026

Agricultural risk and resilience被引用数 0

ひとこと要約

The paper combines a deep neural network to predict weekly home insurance claims from precipitation data with a copula-based ensemble over six climate-model projections to assess multivariate tail risk of future claims in two Canadian Prairie cities.

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.

研究の動機と目的

• Motivate the insurance industry’s need to quantify climate-driven residential claim risk amid changing precipitation patterns.
• Develop a two-step framework combining deep learning with copula-based multivariate analysis to assess future claim dynamics.
• Incorporate uncertainty from multiple climate model projections through a probabilistic ensemble.
• Inform risk management, pricing, and resilience planning under climate scenarios.

提案手法

• Train a three-hidden-layer deep neural network (64 units per layer, ReLU) to model weekly claim counts as a function of precipitation predictors and their lags.
• Regularize the network with L2 penalties and dropout (rate 0.2) to mitigate overfitting.
• Predict weekly claims for 2021–2030 using six climate-model projections and obtain six forecasted claim series.
• Model dependencies among the six projections with a six-variate distribution via a Gumbel copula to form a multivariate tail risk measure.
• Marginal claim counts are modeled with a negative binomial type I distribution; joint distribution is constructed with the Gumbel copula; tail probability Φ(z) = P(Y1>z,...,Y6>z) serves as risk metric.
• Compare city-specific risk and assess which city exhibits higher tail risk under future projections.

(a) City A

実験結果

リサーチクエスチョン

• RQ1 How can precipitation-driven patterns be used to predict weekly home insurance claims with a deep learning model?
• RQ2 How can multiple climate-model projections be integrated to form a unified assessment of climate-induced home insurance risk?
• RQ3 What is the impact of copula-based multivariate modeling on evaluating tail risk of future claim outbreaks?
• RQ4 How do predicted claim distributions differ between two Canadian Prairie cities under 2021–2030 climate scenarios?

主な発見

Model	Predictors	RMSE
Model 1	X_t, X_{t-1}, X_{t-2}	0.454
Model 2	X_t, X_{t-1}, D_t	0.463
Model 3	X_t, X_{t-1}, X_{t-2}, D_t	0.453
Model 4	X_t, X_{t-1}, X_{t-2}, D_t, D_{t-1}	0.456
Model 1	X_t, X_{t-1}, X_{t-2}	0.470
Model 2	X_t, X_{t-1}, D_t	0.471
Model 3	X_t, X_{t-1}, X_{t-2}, D_t	0.467
Model 4	X_t, X_{t-1}, X_{t-2}, D_t, D_{t-1}	0.461

• The best DNN configurations achieve RMSE values of 0.453–0.463 across cities and predictor sets, indicating competitive predictive performance.
• Predicted weekly claims for 2021–2030 show distributions reflecting six climate-model projections, with City B displaying heavier tails than City A.
• The six marginals are modeled by a negative binomial type I distribution and combined with a Gumbel copula, yielding a joint tail probability Φ(z) for extreme claim risk.
• Estimated copula parameter θ is 1.327 (SE 0.024) for City A and 1.101 (SE 0.012) for City B, indicating different dependency structures among model projections.
• Future tail probabilities increase relative to the control period, with City B at higher risk of large weekly claim numbers.
• The framework suggests City B is more vulnerable to climate-related insurance risk and may require enhanced planning and mitigation strategies.

(b) City B