Seminar by Spark Tseung

Insurance Risk Classification and Ratemaking with Random Effects in the Mixture of Experts Model

In the underwriting and pricing of non-life insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. Motivated by a real life problem of automobile insurance risk selection, we introduce a flexible regression model with random effects, called the Mixed LRMoE, which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness-of-fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history.