Statistics Seminar

Abstract: Accurate prediction of an insurer’s outstanding liabilities is crucial for maintaining the financial health of the insurance sector. This study is driven by the imperative for insurers to dynamically forecast unpaid losses using the granular transaction data on individual claims. We introduce a copula-based point process framework 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. We further develop a stage-wise strategy for parameter estimation and illustrate its desirable properties with numerical experiments. Real data applications further illustrate that our proposed joint model enhances the insurer's decision making in claims management and risk financing operations.