Special Data Science Seminar - Lisa Gao - A Marked Spatial Point Process for Insurance Claims Management


Speaker: Lisa Gao - University of Wisconsin
Title: A Marked Spatial Point Process for Insurance Claims Management

Abstract: Technological advances in data collection indicate growing potential for analytics to support efficient claims management. We demonstrate how insurers can incorporate high-resolution weather data to assess hail property damage immediately following a hailstorm. In particular, we propose a marked spatial point process for replicated point patterns to model the frequency and severity of hail damage insurance claims. The point process focuses on the geographical distribution of claims and allows insurers to simultaneously incorporate densely collected weather features and traditional policyholder-level rating characteristics, despite being observed from different locations. The marks concern the financial impact of a hailstorm, particularly the effects of dependence among claims. We employ a spatial factor copula to capture spatial dependence, allowing insurers to decompose sources of dependence when jointly characterizing claim severity. 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.

Location: Virtual Zoom meeting

Meeting ID: 949 5373 0428
Passcode: 126157
Go Back
McMaster University - Faculty of Science | Math & Stats