集体风险模型中的预测风险分析：历史频率和总体严重度之间的选择
Predictive Risk Analysis in Collective Risk Model: Choices between Historical Frequency and Aggregate Severity
摘要：
典型的保险风险分类程序包括由可观察的风险特征确定的先验风险分类和后验风险分类，其中调整保费以反映投保人的索赔历史。虽然在后验风险分类程序中使用完整的索赔历史数据是最佳的，即给出具有最小方差的保费估计值，但一些保险部门仅使用索赔历史的部分信息来确定适当的保费。经典的例子包括汽车保险的保费是由索赔频率数据和工人的赔偿保险是基于总的严重程度。这种做法的动机是有一个简化和有效的后验风险分类程序，这是定制所涉及的保险政策。本文比较了两种简化的后验风险分类的相对效率，即基于频率与严重程度，并提供了数学框架，以帮助从业人员选择最合适的做法。
英文摘要：
Typical risk classification procedure in insurance is consists of a priori risk classification determined by observable risk characteristics, and a posteriori risk classification where the premium is adjusted to reflect the policyholder's claim history. While using the full claim history data is optimal in a posteriori risk classification procedure, i.e. giving premium estimators with the minimal variances, some insurance sectors, however, only use partial information of the claim history for determining the appropriate premium to charge. Classical examples include that auto insurances premium are determined by the claim frequency data and workers' compensation insurances are based on the aggregate severity. The motivation for such practice is to have a simplified and efficient posteriori risk classification procedure which is customized to the involved insurance policy. This paper compares the relative efficiency of the two simplified posteriori risk classifications, i.e. based on frequency versus severity, and provides the mathematical framework to assist practitioners in choosing the most appropriate practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
