Detecting Financial Fraud in Real-Time Transactions Using Graph Neural Networks and Anomaly Detection Techniques
Rafi Muhammad Zakaria, Mohammad Mahmudur Rahman, ME Choudhury, Md. Hasibur Rahman, Muhammad Ather Rafi, Anisuzzaman Minto, Md Sibbir Hossain, Shariar Islam Saimon
Abstract
Real-time fraud detection must balance accuracy with millisecond-level latency as adversaries evolve tactics across accounts, devices, merchants, and networks. This paper presents a streaming framework that models payment ecosystems as dynamic, heterogeneous graphs and detects anomalies by fusing Graph Neural Networks (GNNs) with online anomaly detectors. Incoming transactions update a temporal multi-relational graph (card–device–merchant–IP), from which a lightweight GNN (GraphSAGE/GAT variants with edge features and time encoding) produces embeddings on the fly. These embeddings feed (a) a cost-sensitive classifier for known fraud and (b) unsupervised detectors (e.g., Isolation Forest/Deep SVDD) to surface novel, label-sparse attacks. To cope with class imbalance and concept drift, we employ streaming reweighting, adaptive thresholds tuned on precision@k, and continual learning via replay and drift triggers. The system exposes local explanations (subgraph rationales via GNNExplainer/motif scores) to support analyst review and regulatory needs, while a deployment blueprint (feature cache, micro-batching, and asynchronous inference) meets <50–100 ms decision budgets. We evaluate on mixed synthetic/industry datasets with evolving fraud scenarios, reporting ROC-AUC/PR-AUC, detection delay, alert volume, and business impact under cost constraints. Results show consistent gains over rule-based, tabular ML, and static graph baselines, particularly for low-footprint fraud and fast-moving attack campaigns. The proposed design offers a practical path to accurate, auditable, and scalable fraud screening in production payment streams.