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APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

机译：APATE：使用基于网络的扩展自动检测信用卡交易欺诈的新方法

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摘要

In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of REM (Recency Frequency Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98. (C) 2015 Elsevier B.V. All rights reserved.
机译：在过去的十年中，便捷的在线支付方式为电子商务带来了许多新的机会，从而降低了零售业的地域界限。尽管电子商务仍在流行，但也是欺诈者的游乐场，他们试图滥用在线购物的透明度和信用卡记录的转移。本文提出了APATE，这是一种检测在线商店中欺诈性信用卡交易的新颖方法。我们的方法将（1）利用REM（Recency Frequency Monetary）的基本原理结合从传入交易的特征和客户支出历史中获得的固有特征； （2）通过利用信用卡持有者和商人的网络并为每个网络对象得出时间相关的可疑度分数来获得基于网络的特征。我们的结果表明，固有特征和基于网络的特征都是同一张图片的两个相互交织的方面。这两类功能的组合导致性能最佳的模型，其AUC得分高于0.98。 （C）2015 Elsevier B.V.保留所有权利。

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