Neighbor-enhanced Graph Pre-training and Prompt Learning Framework for Fraud Detection

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Nowadays, as more users turn to WeChat Pay and other e-commerce platforms for transactions, an increasing number of fraudsters are being attracted to these platforms to conduct fraudulent activities, thereby stealing money. To address this issue, Graph Neural Networks (GNNs) have been widely adopted and have shown great success. However, with the rise of various transaction methods, users are increasingly engaging in multiple transaction networks, which creates a new scenario that requires models to detect fraud across these diverse networks. Unfortunately, current GNN-based fraud detection strategies often exhibit suboptimal performance and high time complexity in this evolving scenario, as they typically can handle only one transaction network at a time. Recently, advancements in graph prompt learning have demonstrated great success in managing various types of graph data and improving the generalization capabilities of the model, showing great promise for addressing this new fraud detection scenario. Nevertheless, the practical application of graph prompt learning in real-world fraud detection is still constrained, as they may exhibit bias when dealing with multiplex transaction networks and may fail to model the intrinsic relationships between nodes and their neighbors, which is crucial for effective fraud detection. To address these two challenges, we propose GPCF, an efficient graph pre-training and prompt learning framework. GPCF first incorporates a meta-learning-based strategy within neighbor-enhanced contrastive learning to pre-train the GNN model across diverse transaction networks. Then it aligns fraud detection tasks with the well-pre-trained model by simply fine-tuning the prompts. Extensive experiments demonstrate that GPCF achieves state-of-the-art results on open-access fraud and transaction datasets, as well as on real-world fraud datasets from WeChat Pay, one of the largest e-commerce platforms globally, showing the effectiveness of GPCF in practical applications.

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