In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

中文翻译：

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词嵌入模型的精算应用

在保险分析中，索赔的文本描述通常被丢弃，因为传统的经验分析需要数字描述变量。本文演示了如何在保险分析中轻松使用文本数据。使用单词相似度的概念，我们说明了如何从文本中提取变量，并使用标准广义线性模型或广义加性回归模型将它们纳入索赔分析。此程序适用于威斯康星州地方政府财产保险基金 (LGPIF) 数据，以展示如何改进保险索赔管理和风险缓解程序。我们举例说明两种应用。首先，我们展示了如何使用文本信息来解决索赔分类问题。第二，我们分析了风险指标与大损失概率之间的关系。我们在这两种应用中都获得了良好的结果，其中保险索赔的简短文本描述用于提取特征。