Graph Neural Networks (GNNs) have achieved remarkable success in machine learning tasks by learning the features of graph data. However, experiments show that vanilla GNNs fail to achieve good classification performance in the field of graph anomaly detection. To address this issue, we propose and theoretically prove that the high-Class Homophily Variance (CHV) characteristic is the reason behind the suboptimal performance of GNN models in anomaly detection tasks. Statistical analysis shows that in most standard node classification datasets, homophily levels are similar across all classes, so CHV is low. In contrast, graph anomaly detection datasets have high CHV, as benign nodes are highly homophilic while anomalies are not, leading to a clear separation. To mitigate its impact, we propose a novel GNN model named Homophily Edge Augment Graph Neural Network (HEAug). Different from previous work, our method emphasizes generating new edges with low CHV value, using the original edges as an auxiliary. HEAug samples homophily adjacency matrices from scratch using a self-attention mechanism, and leverages nodes that are relevant in the feature space but not directly connected in the original graph. Additionally, we modify the loss function to punish the generation of unnecessary heterophilic edges by the model. Extensive comparison experiments demonstrate that HEAug achieved the best performance across eight benchmark datasets, including anomaly detection, edgeless node classification and adversarial attack. We also defined a heterophily attack to increase the CHV value in other graphs, demonstrating the effectiveness of our theory and model in various scenarios.

Fraud detection is a typical data mining mission in the field of finance. In recent years, due to their capability of mining hidden associations between entities, graph neural networks (GNNs) have been widely applied to detect financial fraudsters. However, GNNs are fragile in their data aggregation process and will be attacked on purpose by fraudsters, therefore, some other pioneers have explored methods to enhance the robustness of GNN-based fraud detection models. But most existing models based on ideal settings, as real-life criminals tend to attack as far as they can reach, struggle to establish a unified effective approach for attacked and unattacked data of different scales in realistic scenarios. Furthermore, mainstream robust defense models indiscriminately modifying and truncating data will lose important information of the major unattacked parts in the original graph, which lowers their overall fraud detection precision. Therefore, in this work, we propose a novel generative fraud detection framework called GSRGNN. In particular, we first design a generative structure to obtain augmented node features. Then we prioritize the nodes with high degrees to create a more stable graph structure on local distributions. Finally, we pass the enhanced features and structure with the input ones in pairs through GNN layers, and create synthetic representations with abundant information and sufficient resistance to perturbations for subsequent fraud detection. In addition, we also design a novel black-box attack algorithm to realistically imitate the perturbations conducted by fraudsters on graph features and structure. Experiments on the world's leading electronic trading platform and public anti-fraud datasets demonstrate the outstanding performance of our proposed method compared with those state-of-the-art models, showing its superiority in precision and robustness on financial fraud detection missions.

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.

Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more sophisticated camouflage strategies. Specifically, fraudsters disguise themselves by mimicking the behavioral data collected by platforms, ensuring that their key characteristics are consistent with those of benign users to a high degree, which we call Adaptive Camouflage. Consequently, this narrows the differences in behavioral traits between them and benign users within the platform's database, thereby making current GFD models lose efficiency. To address this problem, we propose a relation diffusion-based graph augmentation model Grad. In detail, Grad leverages a supervised graph contrastive learning module to enhance the fraud-benign difference and employs a guided relation diffusion generator to generate auxiliary homophilic relations from scratch. Based on these, weak fraudulent signals would be enhanced during the aggregation process, thus being obvious enough to be captured. Extensive experiments have been conducted on two real-world datasets provided by WeChat Pay, one of the largest online payment platforms with billions of users, and three public datasets. The results show that our proposed model Grad outperforms SOTA methods in both various scenarios, achieving at most 11.10% and 43.95% increases in AUC and AP, respectively.

Medical insurance fraud has always been a crucial challenge in the field of healthcare industry. Existing fraud detection models mostly focus on offline learning scenes. However, fraud patterns are constantly evolving, making it difficult for models trained on past data to detect newly emerging fraud patterns, posing a severe challenge in medical fraud detection. Moreover, current incremental learning models are mostly designed to address catastrophic forgetting, but often exhibit suboptimal performance in fraud detection. To address this challenge, this paper proposes an innovative online learning method for medical insurance fraud detection, named POCL. This method combines contrastive learning pre-training with online updating strategies. In the pre-training stage, we leverage contrastive learning pre-training to learn on historical data, enabling deep feature learning and obtaining rich risk representations. In the online learning stage, we adopt a Temporal Memory Aware Synapses online updating strategy, allowing the model to perform incremental learning and optimization based on continuously emerging new data. This ensures timely adaptation to fraud patterns and reduces forgetting of past knowledge. Our model undergoes extensive experiments and evaluations on real-world insurance fraud datasets. The results demonstrate our model has significant advantages in accuracy compared to the state-of-the-art baseline methods, while also exhibiting lower running time and space consumption. Our sources are released at https://github.com/finint/POCL.