Applications of Population Sampling to Insurance Ratemaking and Reserving

This paper explores the underutilized application of population sampling in the realm of actuarial science, a field where these statistical methodologies have been traditionally overlooked. Focusing on two distinct applications within insurance ratemaking and reserving, we unveil innovative approaches to address challenges in actuarial contexts and provide valuable insights into advancing methodologies in the field. The first application introduces population sampling as a solution to the computational complexities inherent in credibility premium calculation, particularly under Bayesian regression models. By combining population sampling with surrogate modeling, we present a method to manage computation challenges effectively. The second application delves into incurred but not reported reserves, challenging the conventional Chain–Ladder method and individual reserving models by incorporating population sampling. Proposing a reserve estimator based on inverse probability weighting techniques, we demonstrate a statistically robust, distribution-free method for IBNR reserving, emphasizing the integration of granular policyholder information