The Role of AI and Machine Learning in Enhancing Payment Fraud Detection and Prevention in Cloud-Native Payment Systems

Abstract

This article reviews how artificial intelligence and machine learning technologies are revolutionizing fraud detection in cloud-native payment systems. As digital payment channels proliferate, traditional rule-based detection methods have proven inadequate against sophisticated fraud tactics. The shift toward AI/ML-driven approaches enables financial institutions to identify both known fraud patterns and emerging threats with greater accuracy while reducing false positives. Cloud-native architectures provide the ideal foundation for these advanced capabilities through microservices, containerization, serverless computing, and edge deployment models that enable real-time transaction screening at scale. The article discusses supervised learning techniques for known fraud pattern identification, unsupervised approaches for anomaly detection, and the technical implementation challenges organizations face during adoption. Case studies demonstrate the transformative impact on operational efficiency, customer experience, and financial outcomes, while highlighting integration challenges with legacy systems and ethical considerations for model fairness across demographic groups.