Backend Latency Optimization in Real-Time Fraud Detection Systems

Abstract

This technical article explores advanced optimization techniques for backend systems in real-time fraud detection, focusing on achieving minimal latency while maintaining high accuracy in transaction processing. Through comprehensive implementation of in-memory databases, multi-level caching strategies, and asynchronous processing pipelines, the system achieved a 99th percentile response time of 212 microseconds, representing a 77% improvement over traditional architectures. The integration of serverless computing for dynamic scaling enabled handling of up to 228,000 transactions per second, while edge computing deployment reduced network latency from 95ms to 22.3ms per transaction. The system maintained 99.985% fraud detection accuracy while reducing CPU utilization from 82% to 38% and memory utilization from 88% to 52%. The article presents detailed performance benchmarks from production environments, demonstrating these significant improvements in response times, throughput, and resource utilization. Additionally, the article addresses implementation challenges and solutions across different architectural layers, from data storage to model deployment, while considering crucial aspects such as privacy preservation and regulatory compliance in financial systems. The optimizations resulted in a 37% reduction in operational costs while maintaining strict compliance with data protection regulations.