Temporal Graph Networks for Graph Anomaly Detection in Financial
  Networks
Yejin Kim Youngbin Lee Minyoung Choe Sungju Oh Yongjae Lee
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.