摘要翻译：
保险公司每年必须管理数以百万计的索赔。虽然大多数索赔是非欺诈性的，但欺诈检测是保险公司的核心。其最终目的是建立一个预测模型，将欺诈性索赔单挑出来，并立即支付非欺诈性索赔。现代机器学习方法很适合这类问题。医疗保险索赔通常具有分层和可变长度的数据结构。提出了一种基于分段前向神经网络（深度学习）和一种基于自关注神经网络的索赔管理模型。我们表明，在200万医疗保险索赔数据集上，所提出的方法优于基于单词袋的模型、手工设计的特征和基于卷积神经网络的模型。提出的自我注意方法表现最好。
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英文标题：
《A Self-Attention Network for Hierarchical Data Structures with an
  Application to Claims Management》
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作者：
Leander L\"ow, Martin Spindler, Eike Brechmann
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem. Health care claims often have a data structure that is hierarchical and of variable length. We propose one model based on piecewise feed forward neural networks (deep learning) and another model based on self-attention neural networks for the task of claim management. We show that the proposed methods outperform bag-of-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims. The proposed self-attention method performs the best.
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PDF链接：
https://arxiv.org/pdf/1808.10543