MCFAM Seminar - Dynamic Prediction of Outstanding Insurance Claims Using Joint Models for Longitudinal and Survival Outcomes

Speaker: Lu Yang

Abstract: To ensure the solvency and financial health of the insurance sector, it is vital to accurately predict the outstanding liabilities of insurance companies. We aim to develop a dynamic statistical model that allows insurers to leverage granular transaction data on individual claims into the prediction of outstanding claim payments. However, the dynamic prediction of an insurer's outstanding liability is challenging due to the complex data structure. The liability cash flow from a claim is generated by multiple stochastic processes: a recurrent event process describing the timing of the cash flow, a payment process generating the sequence of payment amounts, and a settlement process terminating both the recurrence and payment processes. We propose to use a copula-based marked point process to jointly model the three processes. Specifically, a counting process is employed to specify the recurrent event of payment transactions; the time-to-settlement outcome is treated as a terminal event for the counting process; and the longitudinal payment amounts are formulated as the marks associated with the counting process. The dependencies among the three components are induced using the method of pair copula constructions. Compared with existing joint models for longitudinal and time-to-event data such as random effect models, the proposed approach enjoys the benefits of flexibility, computational efficiency, and straightforward prediction.