This article presents several actuarial applications of categorical embedding in the context of non-life insurance risk classification. In non-life insurance, many rating factors are naturally categorical, and often the categorical variables have a large number of levels. The high cardinality of categorical rating variables presents challenges in the implementation of traditional actuarial methods. Categorical embedding that is proposed in the machine learning literature for handling categorical variables has recently received attention in actuarial studies. The method is inspired by the neural network language models for learning text data and maps a categorical variable into a real-valued representation in the Euclidean space. Using a property insurance claims we demonstrate the use of categorical embedding in three applications. The first shows how embeddings are used to construct rating classes and calculate rating relativities for a single insurance risk. The second concerns predictive modeling for multivariate insurance risks and emphasizes the effects of dependence on tail risks. The third focuses on pricing new products where transfer learning is used to gather knowledge from existing products.

本文介绍了非人寿保险风险分类背景下分类嵌入的几种精算应用。在非寿险中，许多评级因素自然是分类的，并且分类变量通常具有大量级别。分类评级变量的高基数给传统精算方法的实施带来了挑战。机器学习文献中提出的用于处理分类变量的分类嵌入最近在精算研究中受到了关注。该方法受到用于学习文本数据的神经网络语言模型的启发，并将分类变量映射到欧几里得空间中的实值表示。通过财产保险索赔，我们在三个应用程序中演示了分类嵌入的使用。第一个展示了如何使用嵌入来构建评级类别并计算单个保险风险的评级相关性。第二个涉及多元保险风险的预测模型，并强调对尾部风险的依赖的影响。第三个重点是新产品的定价，其中使用迁移学习从现有产品中收集知识。