Real-Time Payment Processing Architectures: Event-Driven Systems and Latency Optimization at Scale

Abstract

Real-time payment processing has become a critical infrastructure component in modern financial systems, requiring architectures capable of handling millions of transactions per second with sub-second latency guarantees. This review examines the evolution and current state of event-driven architectures (EDA) for payment processing, focusing on latency optimization techniques and scalability challenges. Event-driven systems leverage asynchronous message passing and distributed computing paradigms to achieve the performance requirements of contemporary payment networks. The adoption of microservices architecture (MSA) combined with event streaming platforms has enabled financial institutions to process transactions with latencies below 100 milliseconds while maintaining consistency and reliability. This paper synthesizes research on architectural patterns including Command Query Responsibility Segregation (CQRS), event sourcing, and saga patterns that address the complexities of distributed payment processing. We analyze optimization strategies encompassing network topology design, data partitioning schemes, caching mechanisms, and consensus protocols. The review also examines scalability solutions including horizontal scaling approaches, load balancing algorithms, and database sharding techniques. Key findings indicate that hybrid architectures combining synchronous and asynchronous processing, coupled with intelligent caching and optimized serialization protocols, can achieve throughput exceeding ten million transactions per second while maintaining ACID properties. The paper concludes by identifying emerging trends in quantum-resistant cryptography integration, machine learning-based fraud detection, and cross-border payment harmonization that will shape future payment processing architectures.