Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns

Technological advances in weather data collection allow insurers to incorporate high-resolution data to manage hail risk more effectively, but challenges arise when the response variable and predictors are collected from different locations. To address this issue, we adopt a spatial point pattern viewpoint for modeling hail insurance claims. In particular, we propose a spatial mixed-effects framework for replicated point patterns to model the frequency and geographical distribution of hail damage claims following a hailstorm. Our model simultaneously incorporates traditional property rating characteristics collected from policyholders, as well as densely collected weather features, even when observed at different sets of locations across a region. We discuss likelihood-based inference and demonstrate parameter estimation with simulation studies. Using hail damage insurance claims data from a U.S. insurer, supplemented with hail radar maps and other spatially varying weather features, we show that incorporating granular data to model the development of claim reporting patterns helps insurers anticipate and manage claims more efficiently.