It has become standard practice in the non-life insurance industry to employ generalized linear models (GLMs) for insurance pricing. However, these GLMs traditionally work only with a priori characteristics of policyholders, while nowadays we increasingly have a posteriori information of individual customers available across multiple product categories. In this paper, we therefore develop a framework to capture this a posteriori information over several product lines using a dynamic claim score. More specifically, we extend the bonus-malus-panel model of Boucher and Inoussa (2014) and Boucher and Pigeon (2018) to include claim scores from other product categories and to allow for nonlinear effects of these scores. The application of the proposed multi-product framework to a Dutch property and casualty insurance portfolio shows that customers’ individual claims experience can have a significant impact on the risk classification. Moreover, it indicates that considerably more profits can be gained by accounting for their multi-product claims experience.

中文翻译：

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保险业动态多产品风险分类的预测理赔分数

使用广义线性模型 (GLM) 进行保险定价已成为非寿险行业的标准做法。然而，这些 GLM 传统上只适用于先验投保人的特点，而现在我们越来越多后验的跨多个产品类别的单个客户的信息。因此，在本文中，我们开发了一个框架来捕捉这一点后验的使用动态索赔分数的多个产品线的信息。更具体地说，我们扩展了 Boucher 和 Inoussa（2014 年）以及 Boucher 和 Pigeon（2018 年）的奖金-malus-panel 模型，以包括来自其他产品类别的索赔分数，并考虑到这些分数的非线性影响。将提议的多产品框架应用于荷兰财产和意外伤害保险组合表明，客户的个人索赔经验可能对风险分类产生重大影响。此外，这表明通过考虑他们的多产品索赔经验可以获得更多的利润。