Scalable Distributed Systems for Real-Time Big Data Processing in Financial Technology

Abstract

Real-time stream processing in regulated financial environments requires simultaneous guarantees of low latency, data confidentiality, and auditability, requirements that existing systems struggle to satisfy jointly. Prior approaches either sacrifice performance for security or omit compliance mechanisms entirely, leaving a gap in practical, production-ready solutions. To address this, we propose a co-designed architecture integrating lightweight secure aggregation (LSA), adaptive micro-batching, and LSTM-based predictive autoscaling within Apache Flink. Evaluated on a real-world dataset of anonymized payment transactions, our system achieves a 99th-percentile latency of 178 ± 6 ms at a sustained throughput of 89k ± 1.2k events/sec, thereby meeting a strict 200-ms service-level objective while maintaining 100% compliance completeness. In contrast, a baseline employing homomorphic encryption (CryptoStream) incurs a significantly higher latency of 312 ± 18 ms and consumes roughly four times the CPU resources. Another secure baseline (Flink-SGX), while meeting the latency target (192 ± 9 ms), exhibits operational fragility under load. Ablation studies confirm the necessity of each component for balancing performance, stability, and regulatory adherence. Collectively, the results demonstrate a feasible path toward confidential, auditable, and high-performance stream processing for real-world financial infrastructure.