TRUST: Transaction Risk via Unified Sequence and Topology

Abuse detection in e-commerce platforms is critical for preventing operational losses, particularly for transaction types vulnerable to abuse such as Return-to-Origin (RTO) in Cash-on-Delivery (COD) workflows. Detecting such abuse accurate, real-time decisions to intercept malicious orders before placement, imposing stringent sub-second latency requirements on deployed systems. In this work, we present TRUST, a deployed, production-scale abuse detection system based on a unified architecture of heterogeneous Graph Neural Networks (GNNs) and Transformer-based sequence encoders. This design enables joint reasoning over multi-relational entity interactions and temporal behavioural signals, allowing the model to combine complementary information for effective abuse detection when either modality is sparse or absent. TRUST processes millions of transactions daily with an average inference latency of ~25 ms, achieving a ~9.6% absolute precision improvement over a strong XGBoost baseline in live RTO detection. We report systematic ablation studies across both graph and sequence stages, evaluating GNN variants, sampling strategies, sequence lengths, and positional encoding schemes to guide architectural choices. Deployed end-to-end in a high-throughput environment, TRUST demonstrates that GNN–Transformer cascades can deliver state-of-the-art accuracy, scalability, and operational reliability in real-world abuse detection, offering a reproducible blueprint for similar industry-scale applications.