A new systematic survey published in Discover Computing by Ahmed Harbi argues that defenders monitoring only one layer of a fraud chain see roughly half of it and miss the relational coordination defining modern fraud rings. The survey reviews 22 primary studies from 2017 to 2025 across graph-based fraud detection, multi-access edge computing, federated training, and blockchain audit, organized along a five-dimensional taxonomy covering graph representation, temporal handling, edge and federated deployment, class-imbalance handling, and auditability. A coverage matrix shows no published system addresses all five dimensions simultaneously. The Communications Fraud Control Association reported global operator-side losses of approximately 28.3 billion US dollars in 2019, and recent industry estimates place annual telecom-fraud losses above 46 billion dollars, close to two percent of worldwide telecom revenues, concentrated in International Revenue Share Fraud, SIM-box bypass, Wangiri one-ring campaigns, and SIM-swap account take-overs. Graph neural networks have closed the relational gap left by classical detectors, which treat transactions as independent observations, and systems such as BTG, GAT-COBO, GDFGAT, LSG-FD, and MLCG-FD have pushed accuracy on telecom benchmarks. Yet the survey finds these systems are almost universally evaluated in offline, centralized batch settings on a single historical snapshot in a single trust domain, incompatible with the roughly 100-millisecond latency budget of card-present payment authorization; a centralized cloud round-trip from a user in Riyadh to