Graph-Based Fraud Detection for UPI Transaction Networks

Unified payment architectures operate through hyper-connected infrastructural layers that execute billions of digital settlements across distributed banking entities and retail interfaces. The rapid escalation in transaction density has catalysed sophisticated forms of financial deception that exploit structural opacity across multi-entity networks [3]. Traditional fraud containment systems primarily rely on isolated rule engines and tabular classifiers that fail to capture the multi-hop relational dependencies underlying modern illicit schemes [1].

Contemporary fraudulent behaviour increasingly manifests as coordinated money mule syndicates, layered identity disguises, and distributed circular settlement flows [3]. Conventional machine learning baselines inspect transactions as disjoint records, leaving severe analytical blind spots when perpetrators disperse stolen funds across intermediate virtual payment addresses, varying terminal identities, and multi-relational proxy endpoints [2]. These structural limitations necessitate topological frameworks capable of inspecting global network geometry without incurring latency penalties [6].

Graph Neural Networks (GNNs) offer a robust computational paradigm by representing payment ecosystems as dynamic, heterogeneous relational structures where users, devices, accounts, and transactions constitute interdependent nodes and edges [1], [2]. Incorporating relational message-passing, structural attention weights, and temporal aggregators enables the simultaneous detection of micro-level anomalies and macro-level conspiratorial subgraphs in streaming environments [3]. This research evaluates graph-centric learning paradigms to establish scalable, real-time defence mechanisms tailored for high-throughput retail payment fabrics.