The objective of this paper is to propose a novel deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. It lays the groundwork for understanding how insights can be gained into the fraudulent behavior of an insured person with minimum effort. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each model's dynamics is discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately. Both qualitative and quantitative performance evaluations are conducted, although a greater emphasis is placed on qualitative evaluation. To broaden the scope of reference of fraud detection setting, various metrics are used in the qualitative evaluation.

中文翻译：

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使用无监督深度学习进行保险欺诈检测

本文的目的是提出一种新颖的深度学习方法，以使用无监督变量重要性获得对被保险人行为的务实见解。它为了解如何以最少的努力深入了解被保险人的欺诈行为奠定了基础。从对现有欺诈检测模型的局限性进行初步调查开始，我们提出了一种新的变量重要性方法，结合了两个突出的无监督深度学习模型，即自动编码器和变分自动编码器。讨论了每个模型的动态，以告知读者如何针对欺诈检测调整模型以及如何适当地感知结果。进行定性和定量的绩效评估，虽然更加强调定性评价。为了扩大欺诈检测设置的参考范围，在定性评估中使用了各种指标。