This paper explores the utilization of Temporal Graph Networks (TGN) for
financial anomaly detection, a pressing need in the era of fintech and
digitized financial transactions. We present a comprehensive framework that
leverages TGN, capable of capturing dynamic changes in edges within financial
networks, for fraud detection. Our study compares TGN's performance against
static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph
neural network baselines using DGraph dataset for a realistic financial
context. Our results demonstrate that TGN significantly outperforms other
models in terms of AUC metrics. This superior performance underlines TGN's
potential as an effective tool for detecting financial fraud, showcasing its
ability to adapt to the dynamic and complex nature of modern financial systems.
We also experimented with various graph embedding modules within the TGN
framework and compared the effectiveness of each module. In conclusion, we
demonstrated that, even with variations within TGN, it is possible to achieve
good performance in the anomaly detection task.

本文探讨了利用时间图网络（TGN）进行金融异常检测的技术，通过实验证明 TGN 在捕捉金融网络中边的动态变化方面具有显著优势，表明 TGN 在适应现代金融系统动态复杂性方面具有潜力，从而成为检测金融欺诈的有效工具。