A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model

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Tác giả: Andrei L Badescu, Ian Weng Chan, Tsz Chai Fung, X. Sheldon Lin, Spark C Tseung

Ngôn ngữ: eng

Ký hiệu phân loại: 001.434 Experimental method

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 195814

A well-designed framework for risk classification and ratemaking in automobile insurance is key to insurers' profitability and risk management, while also ensuring that policyholders are charged a fair premium according to their risk profile. In this paper, we propose to adapt a flexible regression model, called the Mixed LRMoE, to the problem of a posteriori risk classification and ratemaking, where policyholder-level random effects are incorporated to better infer their risk profile reflected by the claim history. We also develop a stochastic variational Expectation-Conditional-Maximization algorithm for estimating model parameters and inferring the posterior distribution of random effects, which is numerically efficient and scalable to large insurance portfolios. We then apply the Mixed LRMoE model to a real, multiyear automobile insurance dataset, where the proposed framework is shown to offer better fit to data and produce posterior premium which accurately reflects policyholders' claim history.
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