教育部人文社科重点研究基地中央财经大学中国精算研究院学术活动
精算论坛讲座第208期
(2022年11月4日)
讲座题目：A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model
讲座摘要：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. While the policyholder information such as age and gender reflect the observable risk characteristics, their claim history may be regarded as a manifestation of unmeasurable and unobservable risk factors, which could vary drastically across different policyholders. This presentation introduces a flexible regression model, called the Mixed LRMoE, for a posteriori ratemaking, which leverages both policyholder information and their claim history, to categorize policyholders into groups with risk profiles, and to determine a premium that accurately captures the unobserved risks. Our proposed framework is shown to outperform the benchmark models in terms of goodness-of-fit to a real, multiyear automobile insurance dataset, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history.
报告人简介：Tsz Chai (Samson) Fung, FSA, is an Assistant Professor in the Department of Risk Management and Insurance, Georgia State University (GSU). Prior to joining GSU, he worked as a Postdoctoral Researcher at ETH Zurich under the supervision of Professor Mario Wuthrich during 2020-2021. He earned a PhD Statistics degree at the University of Toronto in 2020, supervised by Professor X.Sheldon Lin and Professor Andrei Badescu, and he was also awarded the Hickman Scholarship and the Ontario Trillium Scholarship during his PhD study. His current research interests include insurance loss modeling and statistical inference techniques with applications in actuarial problems, including pricing, reserving, and risk management. He has actively published in leading actuarial journals, including IME, ASTIN Bulletin, NAAJ, and SAJ.
讲座时间：2022年11月4日 上午9:30-10:30
报告地点：腾讯会议（会议ID：146-918-650）
邀 请 人：池义春
欢迎各位老师和同学积极参加！
