Predicting the Pivot: Real-Time Fraud Prevention in High-Volume Payment Gateways Using Graph Neural Networks

Abstract

High-volume payment gateways must decide whether each transaction is legitimate within a few tens of milliseconds, while the fraud they seek to catch is rare, adversarial, and increasingly organised into coordinated rings rather than isolated events. Conventional fraud controls—rule engines and tabular gradient-boosted classifiers—score each transaction in isolation and therefore miss the relational signal that distinguishes a fraud ring from ordinary commerce. This article evaluates whether graph neural networks (GNNs), and specifically a temporal, heterogeneous, inductive architecture designed for streaming inference, can catch fraudulent transactions as they happen without violating the latency budget of a production gateway. We formalise real-time gateway fraud detection as inductive node-and-edge classification over a dynamic, attributed transaction graph whose nodes represent cards, merchants, devices and network identifiers, and we propose a Temporal Relational Graph Attention model with a per-node memory module (TR-GAM) that combines time-aware message passing, relation-specific attention, inductive neighbour aggregation, and label-aware sampling for extreme class imbalance, deployed through a Lambda-style pipeline that separates batch training from low-latency streaming scoring. The architecture is assessed against tabular and graph baselines—LightGBM, GCN, GAT, GraphSAGE, CARE-GNN, PC-GNN and a standard Temporal Graph Network—on the public IEEE-CIS, Elliptic, Amazon and YelpChi benchmarks, using area under the ROC and precision–recall curves, macro-F1, recall at a fixed false-positive operating point, and per-transaction inference latency. Drawing on a reproduction protocol and a synthesis of reported benchmark results, the evaluation indicates that temporal relational GNNs deliver materially higher recall at the low false-positive rates that production demands, that their advantage is largest precisely where fraud is organised, and that inductive, memory-based designs can sustain sub-100-millisecond scoring at gateway scale. We discuss the principal obstacles to deployment—neighbour-explosion latency, concept drift, label delay, and adversarial camouflage—and argue that the architecture’s real-time viability, not its offline accuracy alone, is the decisive criterion for fraud prevention.