报 告 人：Professor X. Sheldon Lin, University of Toronto

报告时间：2023年12月14日（周四）下午15点-16点

报告地点：腾讯会议 321-818-561

 

报告摘要：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. In this presentation, we present a flexible regression model with random effects, called the Mixed LRMoE, which leverages both policyholder information and their claim history to classify 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.

 

报告人简介: Sheldon Lin is a professor of actuarial science at the University of Toronto. His research areas include insurance risk modeling and actuarial statistics. Sheldon Lin is a well-known expert in actuarial science. His papers appeared in all major actuarial science journals, and he has also published in top finance journals, including the Journal of Financial Economics. He was an Associate Editor of Management Science, a co-editor of the North American Actuarial Journal, and is now serving as an editor of Insurance: Mathematics and Economics. He received a number of awards.