Interoperable Financial Data Platforms through AI-Enhanced NFV Architectures
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Abstract
Interoperable financial data platforms are essential to the modern digital ecosystem, enabling seamless data exchange among banks, fintechs, regulatory bodies, and end-users. This paper explores how AI-enhanced Network Function Virtualization (NFV) can advance interoperability, scalability, and adaptability in financial data platforms. NFV allows dynamic instantiation of network functions—such as data translation, anonymization, compliance enforcement, and analytics—as virtualized services running on commodity hardware. Integrating AI capabilities—specifically machine learning (ML) models for protocol translation, anomaly detection, semantic alignment, and automated compliance checking—further elevates platform intelligence and responsiveness. The proposed architecture embeds AI modules into virtual network functions (VNFs), supporting tasks such as cross-format mapping (e.g., ISO 20022 ⇄ proprietary formats), real-time fraud detection, dynamic throttling in high-throughput scenarios, and semantic harmonization across disparate financial data schemas. The design supports plug-and-play interoperability, where new participants can onboard by deploying tailored AI-powered VNFs without disrupting existing workflows. Key advantages include flexible scaling, reduced vendor lock-in, intelligent routing and transformation, and automated compliance adaptation. Challenges include the complexity of orchestrating AI models across VNFs, performance overhead, regulatory constraints, and security issues in virtualized environments. A case-study implementation deploying AI-enhanced VNFs on OpenStack-based NFV infrastructure shows improved throughput (approx. 30 % lower latency), higher accuracy in format mapping (~95 % correct field alignments), and faster anomaly detection compared to static rule-based systems. We conclude that AI-enhanced NFV architectures present a promising avenue for next-generation interoperable financial data platforms. The paper proposes further refinement of orchestration policies, rigorous security hardening, standardized benchmarking, and extension to distributed ledger–enabled participants.
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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 AI-assisted
orchestration discussion in virtualized networks.)