Graph-based fraud detection (GFD) has garnered increasing attention due to its effectiveness in identifying fraudsters within multimedia data such as online transactions, product reviews, or telephone voices. However, the prevalent in-distribution (ID) assumption significantly impedes the generalization of GFD approaches to out-of-distribution (OOD) scenarios, which is a pervasive challenge considering the dynamic nature of fraudulent activities. In this paper, we introduce the Heterophilic Graph Invariant Learning Framework (HGIF), a novel approach to bolster the OOD generalization of GFD. HGIF addresses two pivotal challenges: creating diverse virtual training environments and adapting to varying target distributions. Leveraging edge-aware augmentation, HGIF efficiently generates multiple virtual training environments characterized by generalized heterophily distributions, thereby facilitating robust generalization against fraud graphs with diverse heterophily degrees. Moreover, HGIF employs a shared dual-channel encoder with heterophilic graph contrastive learning, enabling the model to acquire stable high-pass and low-pass node representations during training. During the Test-time Training phase, the shared dual-channel encoder is flexibly fine-tuned to adapt to the test distribution through graph contrastive learning. Extensive experiments showcase HGIF's superior performance over existing methods in OOD generalization, setting a new benchmark for GFD in OOD scenarios.

Collaborative fraud has become increasingly serious in telecom and social networks, but is hard to detect by traditional fraud detection methods. In this paper, we find a significant positive correlation between the increase of collaborative fraud and the degraded detection performance of traditional techniques, implying that those fraudsters that are difficult to detect with traditional methods are often collaborative in their fraudulent behavior. As we know, multiple objects may contact a single target object over a period of time. We define multiple objects with the same contact target as generalized objects, and their social behaviors can be combined and processed as the social behaviors of one object. We propose Fraud Detection Model based on Second-order and Collaborative Relationship Mining (COFD), exploring new research avenues for collaborative fraud detection. Our code and data are released at https://github.com/CatScarf/COFD-MM https://github.com/CatScarf/COFD-MM.