The future of payments: Building high-throughput transaction systems with AI and Java Microservices
Digital Banking, JP Morgan Chase, USA.
Review Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 1401-1411
Publication history:
Received on 22 October 2022; revised on 20 December 2022; accepted on 29 December 2022
Abstract:
The exponential growth in digital payment volumes has exposed critical limitations in traditional payment processing architectures, particularly in handling real-time fraud detection and maintaining sub-second transaction latencies at scale. This paper presents an innovative hybrid architecture combining AI-driven fraud prevention with Java Spring Boot microservices for high-throughput payment orchestration. Our proposed system integrates Apache Kafka for event streaming, Redis for caching, and a novel ensemble learning model that combines gradient boosting with deep neural networks for real-time fraud detection. The methodology employs feature engineering techniques that extract both transactional patterns and behavioral biometrics, achieving a 99.7% fraud detection accuracy while maintaining average transaction processing times of 47ms. Experimental evaluation using a dataset of 2.3 million transactions demonstrates a 340% improvement in throughput compared to traditional monolithic systems, with a 67% reduction in false positive rates. The system successfully processes 50,000 transactions per second while maintaining ACID compliance and PCI-DSS security standards. This research addresses the critical gap between payment system scalability and real-time security requirements, providing a foundation for next-generation financial technology infrastructure.
Keywords:
Payment Systems; Microservices Architecture; Fraud Detection; Machine Learning; Real-Time Processing; Java Spring Boot; Apache Kafka
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0