Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual claims, or large claim triangles. In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features. Unfortunately, such large data sets are in short supply in the actuarial literature. Accordingly, one needs to turn to synthetic data. Although the ultimate objective of these methods is application to real data, the use of synthetic data containing features commonly observed in real data is also to be encouraged.

While there are a number of claims simulators in existence, each valuable within its own context, the inclusion of a number of desirable (but complicated) data features requires further development. Accordingly, in this paper we review those desirable features, and propose a new simulator of individual claim experience called SynthETIC.

Our simulator is publicly available, open source, and fills a gap in the non-life actuarial toolkit. The simulator specifically allows for desirable (but optionally complicated) data features typically occurring in practice, such as variations in rates of settlements and development patterns; as with superimposed inflation, and various discontinuities, and also enables various dependencies between variables. The user has full control of the mechanics of the evolution of an individual claim. As a result, the complexity of the data set generated (meaning the level of difficulty of analysis) may be dialed anywhere from extremely simple to extremely complex. The default version is parameterized so as to include a broad (though not numerically precise) resemblance to the major features of experience of a specific (but anonymous) Auto Bodily Injury portfolio, but the general structure is suitable for most lines of business, with some amendment of modules.

近年来，机器学习在保险损失准备金方面的应用迅速增加。当应用于大型数据集时，它们会产生最大的价值，例如个人索赔或大型索赔三角形。简而言之，它们可能在分析任何数据集时很有用，这些数据集的数量足以掩盖其特征的肉眼视图。不幸的是，如此庞大的数据集在精算文献中供不应求。因此，人们需要转向合成数据。尽管这些方法的最终目标是应用于真实数据，但也鼓励使用包含真实数据中常见特征的合成数据。

虽然存在许多索赔模拟器，每个模拟器都在其自身的上下文中有价值，但包含许多理想（但复杂）的数据特征需要进一步开发。因此，在本文中，我们回顾了这些理想的特征，并提出了一种名为SynthETIC的新的个人索赔体验模拟器。

我们的模拟器是公开可用的开源软件，填补了非寿险精算工具包的空白。模拟器特别允许在实践中通常出现的理想（但可选复杂）数据特征，例如定居率和开发模式的变化；与叠加通货膨胀和各种不连续性一样，还可以实现变量之间的各种依赖关系。用户可以完全控制个人索赔的演变机制。因此，生成的数据集的复杂性（意味着分析的难度级别）可以从极其简单到极其复杂的任何地方进行调整。