Towards Fraud Detection Via Fine-Grained Classification of User Behavior

The mass volume of data in the modern business world requires fraud detection to be automated. Hence, some researchers constructed the fraud scenario into graph data and proposed graph-based fraud detection methods. These methods treat the problem of fraud detection as a binary node classification task. However, the differences between the nodes of the same class are ignored. In this paper, we try to distinguish differences in behavior among nodes of the same class to improve the model's ability to detect deviation, i.e., we make a fine-grained classification of user behavior (called prototypes) and propose an adaptive prototype-based graph neural network (APGNN) for fraud detection. APGNN learns node behavior representations by extracting both neighborhood and global information, supplying preliminary knowledge for the adaptive creation of several prototypes, each representing a distinct behavior pattern. Subsequently, a new loss function is employed to enhance the prototypes' capacity to capture these behavior patterns and to amplify the feature differences between different prototypes. Nodes are then projected onto these prototypes to derive the final behavior patterns. Extensive experiments on four real-world datasets show that this method can provide better fraud detection as well as a more understandable result.