The Speed of Theft: Developing Real-Time Detection Protocols for Authorized Push Payment (APP) Fraud in Instant Payment Systems

Abstract

The rapid proliferation of instant payment systems has fundamentally transformed financial transactions, enabling near-instantaneous, irrevocable fund transfers across global markets. However, this advancement has simultaneously intensified the threat landscape, particularly through the rise of Authorized Push Payment (APP) fraud, wherein victims are deceived into initiating legitimate transactions to fraudulent recipients. The irreversibility and velocity of such transactions render traditional post hoc fraud detection mechanisms ineffective. This study develops a conceptual and analytical framework for real-time APP fraud detection, synthesizing current advances in machine learning, anomaly detection, and behavioral analytics within high-frequency transactional environments.

Through a systematic literature review and thematic synthesis, the study evaluates the limitations of rule-based systems and highlights the superiority of adaptive AI-driven models, including ensemble learning and unsupervised anomaly detection techniques, in identifying evolving fraud patterns. Key challenges, including data imbalance, distribution drift, latency constraints, and regulatory compliance, are critically examined.

The paper proposes a hybrid, low-latency detection architecture integrating explainable AI (XAI), streaming analytics, and human-in-the-loop validation to enhance both detection accuracy and operational trust. The study further formalizes the Value Detection Rate (VDR), a financially meaningful evaluation metric that prioritizes the monetary value of prevented fraud over traditional accuracy measures. The findings suggest that effective fraud mitigation in instant payment ecosystems requires not only technological innovation but also coordinated governance, data-sharing frameworks, and user awareness strategies. This research contributes a scalable and adaptive framework to advance real-time fraud detection and strengthen resilience in modern digital payment infrastructures.