In this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From this automatic analysis, the aim is to predict if a claim is expected to be particularly severe or not. The difficulty is the rarity of such extreme claims in the database, and hence the difficulty, for classical prediction techniques like logistic regression to accurately predict the outcome. Since data is unbalanced (too few observations are associated with a positive label), we propose different rebalance algorithm to deal with this issue. We discuss the use of different embedding methodologies used to process text data, and the role of the architectures of the networks.

中文翻译：

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通过深度神经网络自动分析保险报告以识别严重索赔

在本文中，我们开发了一种方法，使用文本报告中包含的信息（在开始时编辑）自动对索赔进行分类。通过这种自动分析，目的是预测索赔是否预计会特别严重。困难在于这种极端声明在数据库中的罕见性，因此难以让经典预测技术（如逻辑回归）准确预测结果。由于数据是不平衡的（与正标签相关联的观察太少），我们提出了不同的重新平衡算法来处理这个问题。我们讨论了用于处理文本数据的不同嵌入方法的使用，以及网络架构的作用。