Real-time fraud detection in cloud-native fintech systems: A scalable approach using ai and stream processing

Narayanan, Abhilash (2025) Real-time fraud detection in cloud-native fintech systems: A scalable approach using ai and stream processing. Global Journal of Engineering and Technology Advances, 23 (1). pp. 410-419. ISSN 2582-5003

Modern financial institutions face increasingly sophisticated fraud threats in their digital ecosystems, necessitating advanced detection and prevention mechanisms. This article explores the integration of cloud-native architectures with artificial intelligence and stream processing to create robust fraud detection systems. The focus lies on real-time processing capabilities, automated response mechanisms, and scalable architectures that can adapt to evolving fraud patterns. By examining the trade-offs between real-time and batch processing, alongside implementation strategies and best practices, the article demonstrates how modern technology stacks can significantly improve fraud detection accuracy while maintaining operational efficiency. The transformation from traditional rule-based systems to AI-driven architectures represents a crucial evolution in financial security, enabling institutions to protect against emerging threats while providing seamless customer experiences.