Multi-Identity Transaction Graph Pretraining for Fraud Detection

Real-world payment risk modeling is constrained by scarce fraud labels and fragmented behavioral context across multiple payment identities. A physical transaction may appear simultaneously in multiple behavioral histories defined by different identities, while each identity induces distinct temporal dependencies. Existing sequence models typically encode these histories independently, duplicating the same physical event across trajectories and leaving cross-identity dependencies implicit. We propose Multi-Identity Transaction Graph Pretraining (MITraG), a self-supervised framework that organizes overlapping identity-conditioned transaction histories as a shared-event, multi-relational temporal graph. Each transaction is represented once as a node, while edges preserve identity-specific temporal relations and irregular time gaps. MITraG further uses Masked Transaction Node Reconstruction (MTNR) to learn contextual transaction representations from unlabeled histories before supervised adaptation to fraud detection. On real-world payment data under a strict out-of-time evaluation protocol, MITraG consistently outperforms transaction-level, sequence-pretraining, and temporal-graph baselines. These results suggest that modeling transactions as shared events connected by identity-specific temporal relations provides a general basis for self-supervised payment behavior representation learning.