The literature on analytical applications in insurance tends to be either very general or rather technical, which may hold back the adoption of new important tools by industrial practitioners. Our goal is to stress that machine learning (ML) algorithms will play a significant role in the insurance industry in the near future and thus to encourage practitioners to learn and apply these techniques. After discussing the increasing relevance of data for nonlife insurance and briefly reviewing the major impact of digital technology on this business, we restrict our discussion to technical analytical applications and indicate where ML algorithms can add most value. We present two real examples: first a comparison of retention models for household insurance and then a dynamic pricing problem for online motor insurance. Both applications illustrate the advantages but also some of the difficulties of applying ML tools in practice. Finally, we mention some challenges posed by the use of ML in the industry and formulate a few recommendations for successful applications in insurance. This article is neither a tutorial nor an exhaustive review of technical ML applications in nonlife insurance. However, references for additional learning materials are provided.

中文翻译：

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机器学习在非人寿保险中的应用

有关保险中分析应用的文献往往是非常普通的或技术性的，这可能会阻碍工业从业人员采用新的重要工具。我们的目标是强调机器学习（ML）算法将在不久的将来在保险业中扮演重要角色，从而鼓励从业者学习和应用这些技术。在讨论了数据与非人寿保险的日益增长的相关性并简要回顾了数字技术对该业务的主要影响之后，我们将讨论范围限于技术分析应用程序，并指出ML算法可以在哪些方面增加最大价值。我们提供两个真实的例子：首先比较家庭保险的保留模型，然后是在线车险的动态定价问题。这两个应用程序都说明了在实践中应用ML工具的优点和一些困难。最后，我们提到了在行业中使用ML带来的一些挑战，并提出了一些成功应用保险的建议。本文既不是教程，也不是详尽介绍非人寿保险中的机器学习技术应用。但是，提供了其他学习材料的参考。