Bonus-Malus Scale premiums for Tweedie’s compound Poisson models

IF 1.5 Q3 BUSINESS, FINANCE Annals of Actuarial Science Pub Date : 2024-05-21 DOI:10.1017/s1748499524000113
Jean-Philippe Boucher, Raïssa Coulibaly
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Abstract

Based on the recent papers, two distributions for the total claims amount (loss cost) are considered: compound Poisson-gamma and Tweedie. Each is used as an underlying distribution in the Bonus-Malus Scale (BMS) model. The BMS model links the premium of an insurance contract to a function of the insurance experience of the related policy. In other words, the idea is to model the increase and the decrease in premiums for insureds who do or do not file claims. We applied our approach to a sample of data from a major insurance company in Canada. Data fit and predictability were analyzed. We showed that the studied models are exciting alternatives to consider from a practical point of view, and that predictive ratemaking models can address some important practical considerations.
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特威迪复合泊松模型的奖金-马勒斯标度溢价率
根据最近的论文,考虑了两种索赔总额(损失成本)分布:复合泊松-伽马分布和特威迪分布。这两种分布都被用作奖金-损失率模型(BMS)的基础分布。BMS 模型将保险合同的保费与相关保单的保险经验的函数联系起来。换句话说,该模型的思路是为投保人索赔或不索赔时的保费增减建立模型。我们将这一方法应用于加拿大一家大型保险公司的数据样本。我们对数据的拟合度和可预测性进行了分析。我们表明,从实用角度来看,所研究的模型是令人兴奋的替代方案,预测性费率决策模型可以解决一些重要的实际问题。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
期刊最新文献
Generalized Poisson random variable: its distributional properties and actuarial applications Optimizing insurance risk assessment: a regression model based on a risk-loaded approach Bonus-Malus Scale premiums for Tweedie’s compound Poisson models Risk analysis of a multivariate aggregate loss model with dependence Valuation of guaranteed minimum accumulation benefits (GMABs) with physics-inspired neural networks
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