Modernizing Financial Infrastructure with AIPowered NFV and Data Interoperability
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Abstract
Modern financial systems face growing pressure to handle diverse data formats, regulatory regimes, and emergent threats. This paper proposes a novel architecture that modernizes financial infrastructure by integrating Artificial Intelligence (AI) into Network Function Virtualization (NFV) environments to enable seamless data interoperability. NFV enables the deployment of virtualized network services—such as message translation, compliance enforcement, and analytics—as software-based Virtual Network Functions (VNFs), running on commodity infrastructure. Embedding AI into these VNFs empowers systems to adapt intelligently to new data formats, detect anomalies in real time, and enforce compliance dynamically. Our architecture includes AI-driven VNFs for financial messaging translation (e.g., converting between ISO 20022 and legacy/proprietary formats), semantic schema alignment, compliance-rule inference, and fraud or anomaly detection. These VNFs are chained and orchestrated dynamically via NFV management and orchestration (MANO), enabling flexible scalability and plug-and-play interoperability across institutions. A prototype implementation on an OpenStack-based NFV infrastructure demonstrates significant gains: translation accuracy improves by ~20 percentage points over static pipelines; anomaly detection precision surpasses 90%; and onboarding time for new messaging standards drops from days to hours. Latency overhead remains manageable, and the system scales effectively under increased load. The study concludes that AI-powered NFV presents a compelling evolution in financial infrastructure—offering agility, intelligence, and interoperability. However, challenges remain, including governance of AI models, orchestration complexity, security within virtualized environments, and regulatory transparency. We suggest future work focusing on enhancing explainability, integrating trusted execution environments, piloting live deployments, and contributing to standardization efforts for AI-NFV interfaces.
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1. Mijumbi, R., Serrat, J., Gorricho, J. L., Bouten, N., De Turck, F., & Boutaba, R. (2016). Network Function
Virtualization: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials, 18(1), 236–
262.
2. Hussain, S., Hussain, A., & Gani, A. (2017). Software Defined Cloud Computing: A Systematic Review on
Architecture, Applications, Challenges and Future Directions. Journal of Network and Computer Applications, 74,
1–40.
3. Madakam, S., & Ramaswamy, R. (2017). Migration from SWIFT to ISO 20022: A Proposed Strategy for
Financial Institutions. International Journal of Computer Applications, 167(9), 27–32.
4. Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction Aggregation as a
Strategy for Credit Card Fraud Detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
5. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The Application of Data Mining Techniques in
Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. Decision
Support Systems, 50(3), 559–569.
6. Khan, L. U., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2020). Edge-Computing-Enabled Smart Cities: A
Case Study on Privacy-Preserving Traffic Counting. IEEE Network, 34(5), 18–23. (Includes discussion of AIassisted orchestration in virtualized settings.)