How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task.

翻译：如何获取交易的信息性表征并进而识别欺诈交易，是确保金融安全的关键环节。近期研究将图神经网络（GNN）应用于交易欺诈检测问题。然而，由于结构限制，这些方法在学习时空信息方面面临挑战。此外，现有基于GNN的检测器极少认识到融入包含相似行为模式并能为判别性表征学习提供有价值洞察的全局信息的重要性。因此，我们提出一种名为时空感知图Transformer（STA-GT）的新型异构图神经网络，用于解决交易欺诈检测问题。具体地，我们设计了一种时序编码策略来捕获时间依赖性，并将其融入图神经网络框架中，以增强时空信息建模并提升表达能力。进一步地，我们引入Transformer模块来学习局部和全局信息。成对节点间的交互克服了GNN结构的局限性，并建立了目标节点与远距离节点之间的交互。在两个金融数据集上的实验结果表明，与通用GNN模型及基于GNN的欺诈检测器相比，我们提出的STA-GT方法在交易欺诈检测任务上表现有效。