Scalable Distributed Systems for Real-Time Big Data Processing in Financial Technology
Keywords:
secure stream processing, adaptive batching, lightweight cryptography, regulatory compliance, real-time analyticsAbstract
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.References
1. M. R. Dhanagari, "Scaling with MongoDB: Solutions for handling big data in real-time," Journal of Computer Science and Technology Studies, vol. 6, no. 5, pp. 246-264, 2024.
2. H. Zhang, X. Jia, C. Chen, S. Bachani, J. K. Goel, M. Tarun, and E. C. Anyanwu, "Deep learning-based real-time data quality assessment and anomaly detection for large-scale distributed data streams," International Journal of Medical and All Body Health Research, vol. 6, no. 1, pp. 01-11, 2025.
3. S. D. Pasham, "Scalable Graph-Based Algorithms for Real-Time Analysis of Big Data in Social Networks," The Metascience, vol. 2, no. 1, pp. 92-129, 2024.
4. S. D. Pasham, "Privacy-preserving data sharing in big data analytics: A distributed computing approach," The Metascience, vol. 1, no. 1, pp. 149-184, 2023.
5. L. Theodorakopoulos, A. Karras, A. Theodoropoulou, and G. Kampiotis, "Benchmarking big data systems: Performance and decision-making implications in emerging technologies," Technologies, vol. 12, no. 11, p. 217, 2024. doi: 10.3390/technologies12110217
6. J. I. Akerele, A. Uzoka, P. U. Ojukwu, and O. J. Olamijuwon, "Data management solutions for real-time analytics in retail cloud environments," Engineering Science & Technology Journal, vol. 5, no. 11, pp. 3180-3192, 2024.
7. X. Sun, Y. He, D. Wu, and J. Z. Huang, "Survey of distributed computing frameworks for supporting big data analysis," Big Data Mining and Analytics, vol. 6, no. 2, pp. 154-169, 2023. doi: 10.26599/bdma.2022.9020014
8. A. Hammad, and R. Abu-Zaid, "Applications of AI in decentralized computing systems: harnessing artificial intelligence for enhanced scalability, efficiency, and autonomous decision-making in distributed architectures," Applied Research in Artificial Intelligence and Cloud Computing, vol. 7, no. 6, pp. 161-187, 2024.
9. J. Zhu, T. Xu, Y. Zhang, and Z. Fan, "Scalable edge computing framework for real-time data processing in fintech applications," International Journal of Advance in Applied Science Research, vol. 3, pp. 85-92, 2024.
10. S. A. Ionescu, and V. Diaconita, "Transforming financial decision-making: the interplay of AI, cloud computing and advanced data management technologies," International Journal of Computers Communications & Control, vol. 18, no. 6, 2023.
11. D. Mhlanga, "The role of big data in financial technology toward financial inclusion," Frontiers in big Data, vol. 7, p. 1184444, 2024. doi: 10.3389/fdata.2024.1184444
12. T. Saba, K. Haseeb, A. Rehman, and G. Jeon, "Blockchain-enabled intelligent iot protocol for high-performance and secured big financial data transaction," IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 1667-1674, 2023. doi: 10.1109/tcss.2023.3268592
13. J. K. R. Burugulla, "The Future of Digital Financial Security: Integrating AI, Cloud, and Big Data for Fraud Prevention and Real Time Transaction Monitoring in Payment Systems," MSW Management Journal, vol. 34, no. 2, pp. 711-730, 2024.
14. A. K. Vishwakarma, S. Chaurasia, K. Kumar, Y. N. Singh, and R. Chaurasia, "Internet of things technology, research, and challenges: a survey," Multimedia Tools and Applications, vol. 84, no. 11, pp. 8455-8490, 2025. doi: 10.1007/s11042-024-19278-6
15. F. Liu, "Improve the Bi-LSTM Model of University Financial Information Management Platform Construction," Journal of Electrical Systems, vol. 20, no. 1, 2024.
