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：金融欺诈每年造成数十亿美元的损失，但目前缺乏同时考虑用户画像及其在社交网络中行为的有效欺诈检测方法。社交网络形成图结构，而图神经网络（GNN）作为深度学习领域的前沿研究方向，能够无缝处理非欧几里得图数据。在金融欺诈检测中，通过分析用户画像及其行为（如交易、借贷等）以及社交连接性，可以识别犯罪分子的作案手法。当前，大多数GNN无法选择重要邻居，因为邻居的边属性（即行为）被忽略。本文提出一种新颖的行为信息聚合网络（BIAN），将用户行为与其他用户特征相结合。与图注意力网络（GAT）和图变换网络（GTN）等"近亲"不同，BIAN基于邻居边属性分布（即金融社交网络中的用户行为）进行邻域聚合。在真实大规模金融社交网络数据集DGraph上的实验结果表明，与最先进模型相比，BIAN在AUROC指标上获得了10.2%的提升。