Financial crime detection using graph learning improves financial safety and
efficiency. However, criminals may commit financial crimes across different
institutions to avoid detection, which increases the difficulty of detection
for financial institutions which use local data for graph learning. As most
financial institutions are subject to strict regulations in regards to data
privacy protection, the training data is often isolated and conventional
learning technology cannot handle the problem. Federated learning (FL) allows
multiple institutions to train a model without revealing their datasets to each
other, hence ensuring data privacy protection. In this paper, we proposes a
novel two-stage approach to federated graph learning (2SFGL): The first stage
of 2SFGL involves the virtual fusion of multiparty graphs, and the second
involves model training and inference on the virtual graph. We evaluate our
framework on a conventional fraud detection task based on the
FraudAmazonDataset and FraudYelpDataset. Experimental results show that
integrating and applying a GCN (Graph Convolutional Network) with our 2SFGL
framework to the same task results in a 17.6\%-30.2\% increase in performance
on several typical metrics compared to the case only using FedAvg, while
integrating GraphSAGE with 2SFGL results in a 6\%-16.2\% increase in
performance compared to the case only using FedAvg. We conclude that our
proposed framework is a robust and simple protocol which can be simply
integrated to pre-existing graph-based fraud detection methods.

使用图学习进行金融犯罪检测改善金融安全和效率，提出了一种基于联邦图学习的新型框架（2SFGL），实验证明与仅使用 FedAvg 的情况相比，将 GCN 与 2SFGL 集成应用于此任务可以在几个常见指标上提高 17.6％-30.2％的性能，而将 GraphSAGE 与 2SFGL 集成应用可以提高 6％-16.2％的性能，从而得出结论，我们提出的框架是一种稳健且简单易用的协议，可以与现有的基于图的欺诈检测方法简单集成。