In this paper, we present a novel approach to identify linked fraudulent activities or actors sharing similar attributes, using Graph Convolution Network (GCN). These linked fraudulent activities can be visualized as graphs with abstract concepts like relationships and interactions, which makes GCNs an ideal solution to identify the graph edges which serve as links between fraudulent nodes. Traditional approaches like community detection require strong links between fraudulent attempts like shared attributes to find communities and the supervised solutions require large amount of training data which may not be available in fraud scenarios and work best to provide binary separation between fraudulent and non fraudulent activities. Our approach overcomes the drawbacks of traditional methods as GCNs simply learn similarities between fraudulent nodes to identify clusters of similar attempts and require much smaller dataset to learn. We demonstrate our results on linked accounts with both strong and weak links to identify fraud rings with high confidence. Our results outperform label propagation community detection and supervised GBTs algorithms in terms of solution quality and computation time.

中文翻译：

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使用 GraphConvolution 网络识别关联的欺诈活动

在本文中，我们提出了一种使用图卷积网络 (GCN) 来识别关联的欺诈活动或共享相似属性的参与者的新方法。这些链接的欺诈活动可以被可视化为具有抽象概念（如关系和交互）的图形，这使得 GCN 成为识别用作欺诈节点之间链接的图边的理想解决方案。社区检测等传统方法需要在欺诈尝试（例如寻找社区的共享属性）之间建立强关联，而受监督的解决方案需要大量训练数据，这些数据在欺诈场景中可能不可用，并且最有效地提供欺诈和非欺诈活动之间的二元分离。我们的方法克服了传统方法的缺点，因为 GCN 只是学习欺诈节点之间的相似性来识别相似尝试的集群，并且需要更小的数据集来学习。我们在具有强链接和弱链接的关联帐户上展示了我们的结果，以高可信度地识别欺诈环。我们的结果在解决方案质量和计算时间方面优于标签传播社区检测和监督 GBTs 算法。