Financial frauds cause billions of losses annually and yet it lacks efficient approaches in detecting frauds considering user profile and their behaviors simultaneously in social network . A social network forms a graph structure whilst Graph neural networks (GNN), a promising research domain in Deep Learning, can seamlessly process non-Euclidean graph data . In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc. as well as their social connectivity. Currently, most GNNs are incapable of selecting important neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored. In this paper, we propose a novel behavior information aggregation network (BIAN) to combine the user behaviors with other user features. Different from its close "relatives" such as Graph Attention Networks (GAT) and Graph Transformer Networks (GTN), it aggregates neighbors based on neighboring edge attribute distribution, namely, user behaviors in financial social network. The experimental results on a real-world large-scale financial social network dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with the State-Of-The-Art models.
翻译:摘要:金融欺诈每年造成数十亿的损失,但目前缺乏高效的方法在社交网络中同时考虑用户概况和行为来检测欺诈行为。社交网络形成图结构,而图神经网络(GNN)是深度学习中一个有前途的研究领域,可以无缝地处理非欧几里得图数据。在金融欺诈检测中,通过分析用户的概况和行为(如交易、借贷等)以及他们的社交连接,可以识别罪犯的作案模式。目前,大多数 GNN 不能选择重要的邻居,因为邻居的边属性(即行为)被忽略了。本文提出一种新颖的行为信息聚合网络(BIAN),以将用户行为与其他用户特征相结合。与其近亲图注意力网络(GAT)和图变压器网络(GTN)不同,BIAN 根据邻域边属性分布进行邻居聚合,即金融社交网络中的用户行为。在实际大规模金融社交网络数据集 DGraph 上的实验证据表明,与现有最先进的模型相比,BIAN 的 AUROC 增益为 10.2%。