Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks

ArXi:2603.06632v1 Announce Type: new Illicit transaction detection is often driven by transaction level attributes however, fraudulent behavior may also manifest through network structure such as central hubs, high flow intermediaries, and coordinated neighborhoods. This paper presents a time respecting, leakage safe (causal) graph feature extraction protocol for temporal transaction networks and evaluates its utility for illicit entity classification.