A0562
Title: A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims
Authors: Peng Shi - University of Wisconsin-Madison (United States)
Shimeng Huang - University of Wisconsin-Madison (United States)
Lu Yang - University of Minnesota (United States) [presenting]
Abstract: Accurate prediction of an insurer's outstanding liabilities is crucial for maintaining the financial health of the insurance sector. The aim is to develop a statistical model for insurers to dynamically forecast unpaid losses by leveraging the granular transaction data on individual claims. The liability cash flow from a single insurance claim is determined by an event process that describes the recurrences of payments, a payment process that generates a sequence of payment amounts, and a settlement process that terminates both the event and payment processes. More importantly, the three components are dependent on one another, which enables the dynamic prediction of an insurer's outstanding liability. A copula-based point process framework is introduced to model the recurrent events of payment transactions from an insurance claim, where the longitudinal payment amounts and the time-to-settlement outcome are formulated as the marks and the terminal event of the counting process, respectively. The dependencies among the three components are characterized using the method of pair copula constructions. A stage-wise strategy is further developed for parameter estimation, and its desirable properties are illustrated with numerical experiments.
