论文标题
具有层次结构化数据的保险定价：与工人补偿保险投资组合的插图
Insurance pricing with hierarchically structured data: An illustration with a workers' compensation insurance portfolio
论文作者
论文摘要
精算师使用预测建模技术来评估合同的损失成本，这是可观察的风险特征的函数。最先进的统计和机器学习方法没有足够的能力来处理具有大量级别的层次结构化风险因素。在本文中，我们证明了当层次结构化的风险因素，特定于合同和外部收集的风险因素可用时，可以证明保险定价模型的数据驱动构建。我们使用层次信誉模型（Jewell，1975），Ohlsson的一般性线性和分层信誉模型（Ohlsson，2008年）和混合模型的组合检查了工人补偿保险产品的定价（Jewell，1975）。我们通过将线性混合模型与Tweedie广义线性混合模型进行比较，比较了这些模型的预测性能，并评估了分布假设对目标变量的影响。对于我们的案例研究，Tweedie分布非常适合对合同的损失成本进行建模和预测。此外，将特定于合同的风险因素纳入模型可改善我们工人薪酬保险投资组合中的预测绩效和风险差异。
Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learning methods are not well equipped to handle hierarchically structured risk factors with a large number of levels. In this paper, we demonstrate the data-driven construction of an insurance pricing model when hierarchically structured risk factors, contract-specific as well as externally collected risk factors are available. We examine the pricing of a workers' compensation insurance product with a hierarchical credibility model (Jewell, 1975), Ohlsson's combination of a generalized linear and a hierarchical credibility model (Ohlsson, 2008) and mixed models. We compare the predictive performance of these models and evaluate the effect of the distributional assumption on the target variable by comparing linear mixed models with Tweedie generalized linear mixed models. For our case-study the Tweedie distribution is well suited to model and predict the loss cost on a contract. Moreover, incorporating contract-specific risk factors in the model improves the predictive performance and the risk differentiation in our workers' compensation insurance portfolio.
