Abstract: In property insurance claims triage, insurers often use static information to assess the severity of a claim and identify the subsequent actions. We hypothesize that the pattern of weather conditions throughout the course of the loss event is predictive of insured losses and hence appropriate use of weather dynamics improve the operation of insurer's claim management. To test this hypothesis, we propose a deep learning method to incorporate the dynamic weather data in the predictive modeling of insured losses for reported claims. The proposed method features a hierarchical network architecture to address the challenges introduced into claims triage by weather dynamics.
In the empirical analysis, we examine a portfolio of hail damage property insurance claims obtained from a major U.S. insurance carrier. When supplemented by the dynamic weather information, the deep learning method exhibits substantial improvement in the hold-out predictive performance. Built upon the proposed deep learning method, we design a cost-sensitive decision strategy for triaging claims using the probabilistic forecasts of insurance claim amounts. We show that leveraging weather dynamics in claims triage leads to a substantial reduction in operational cost.
About: Peng Shi is on the faculty of the Risk and Insurance Department at the University of Wisconsin-Madison. He is also the Charles and Laura Albright Professor in Business and Finance. Professor Shi is an Associate of the Casualty Actuarial Society (ACAS) and a Fellow of the Society of Actuaries (FSA). Professor Shi's research interests are at the intersection of insurance and statistics. He has won various research awards in actuarial science, including the Charles A Hachemeister Prize, American Risk and Insurance Association Prize, Ronald Bornhuetter Loss Reserve Prize, and IAA Best Paper etc. Current research focuses on longitudinal data, dependence models, insurance analytics, and actuarial data science