Initial insurance losses are often reported with a textual description of the claim. The claims manager must determine the adequate case reserve for each known claim. In this paper, we present a framework for predicting the amount of loss given a textual description of the claim using a large number of words found in the descriptions. Prior work has focused on classifying insurance claims based on keywords selected by a human expert, whereas in this paper the focus is on loss amount prediction with automatic word selection. In order to transform words into numeric vectors, we use word cosine similarities and word embedding matrices. When we consider all unique words found in the training dataset and impose a generalised additive model to the resulting explanatory variables, the resulting design matrix is high dimensional. For this reason, we use a group lasso penalty to reduce the number of coefficients in the model. The scalable, analytical framework proposed provides for a parsimonious and interpretable model. Finally, we discuss the implications of the analysis, including how the framework may be used by an insurance company and how the interpretation of the covariates can lead to significant policy change. The code can be found in the TAGAM R package (github.com/scottmanski/TAGAM).

中文翻译：

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从精算应用的文本描述中提取信息

最初的保险损失通常与索赔的文字描述一起报告。索赔经理必须为每个已知索赔确定足够的案件储备金。在本文中，我们提出了一个框架，用于使用描述中的大量单词来预测索赔的文本描述给定的损失金额。先前的工作集中在根据人类专家选择的关键字对保险索赔进行分类，而在本文中，重点是通过自动词选择来预测损失金额。为了将单词转换为数字向量，我们使用单词余弦相似度和单词嵌入矩阵。当我们考虑在训练数据集中找到的所有唯一词并对产生的解释变量施加一个广义的加法模型时，产生的设计矩阵是高维的。为此原因，我们使用组套索惩罚来减少模型中的系数数量。所提出的可扩展的分析框架提供了一个简约且可解释的模型。最后，我们讨论了分析的含义，包括保险公司如何使用该框架以及协变量的解释如何导致重大的政策变化。代码可以在 TAGAM R 包中找到（github.com/scottmanski/TAGAM）。