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.

翻译：极端天气事件正日益频繁，强风暴、洪水及持续降水正影响着全球社区。这些气候模式的变化对保险业构成直接威胁，使其面临日益增长的天气相关损害风险。随着与极端天气相关的索赔增加，保险公司需要可靠工具来评估未来风险。这不仅对设定保费和维持偿付能力至关重要，也对支持更广泛的防灾准备和韧性建设工作具有重要意义。本研究提出一种两步法来考察降水对家庭保险索赔的影响。该方法将深度神经网络的预测能力与基于连接函数的多元分析灵活性相结合，从而能够更细致地理解降水模式与索赔动态之间的关联。我们通过2002年至2011年加拿大草原地区的案例研究，验证了该方法的有效性。