Real-Time Payments Security: Threat Landscape, Regulatory Pressures and Cloud-Native AI/ML Defense Architectures
A Technical Review and Architectural Framework.
Review
International Journal of Frontiers in Engineering and Technology Research, 2026, 10(02), 036-045.
Article DOI: 10.53294/ijfetr.2026.10.2.0013
Publication history:
Received on 06 March 2026; revised on 13 April 2026; accepted on 16 April 2026
Abstract:
Background: The global adoption of real-time payment (RTP) systems has fundamentally transformed the speed and accessibility of financial transactions, while simultaneously creating an expanded attack surface that outpaces the capabilities of legacy fraud-prevention infrastructure. This paper presents a technical review and architectural framework addressing the security challenges inherent to high-velocity payment environments.
Methods: A structured literature review was conducted across peer-reviewed publications (2018–2024), industry standards bodies (PCI Security Standards Council, SWIFT, Federal Reserve), and practitioner reports from financial technology research firms. Cloud-native architecture patterns were synthesized from vendor documentation, publicly available reference architectures, and comparative deployment case studies. No original empirical data collection or human subjects were involved.
Results: Synthesized evidence from the reviewed literature indicates that AI/ML-based anomaly detection systems achieve transaction-level fraud detection accuracy in the range of 90–99.7% with sub-100 ms latency constraints, and are associated with fraud loss reductions of 40–60% compared with rule-based systems. A cloud-native multi-layer defense architecture—spanning network ingress, identity verification, behavioral analytics, and tokenized data vaults—is presented as a practical framework for organizations seeking PCI DSS 4.0 alignment.
Conclusion: Real-time payment security demands a paradigm shift from reactive rule-sets to adaptive, ML-driven architectures. The framework proposed here provides a vendor-agnostic reference model applicable to major cloud platforms, with quantified trade-offs between detection sensitivity, operational latency, and compliance cost.
Keywords:
Real-time payments; Fraud detection; Machine learning; PCI DSS 4.0; Cloud-native architecture; Anomaly detection; Authorized push payment fraud; Tokenization
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0