AI-Driven NFV Optimization for Data Modernization in the Financial Sector

Abstract

In pursuit of resilient, agile, and scalable infrastructure, financial institutions increasingly leverage data modernization strategies built upon Network Function Virtualization (NFV). Yet, traditional NFV deployments may lack optimization, dynamic response, and integration with compliance frameworks. This paper proposes an AI-driven NFV optimization framework specifically designed for the financial sector’s modernization requirements. The architecture integrates virtualized data flow transformations—such as encryption gateways, compliance filters, and traffic analyzers—with an AI-based orchestration engine that dynamically allocates, chains, and scales NFV services based on evolving demand, regulatory context, or detected anomalies. We develop and evaluate a prototype using containerized VNFs orchestrated through OpenStack Tacker, combined with a machine learning engine trained on synthetic financial data patterns to recommend optimized VNF deployment configurations. Performance testing simulates typical banking scenarios: high transaction volumes, regulatory audit requests, and cyber-threat surges. Results show that AI-guided orchestration reduces latency overhead by 25%, improves throughput by 20%, and aligns resource provisioning with demand—lowering over-provisioning costs by approximately 30%. Additionally, dynamic chaining assures compliance with data governance policies (e.g., auditready logs and policy alignment). However, operating complexity, model explainability, and incremental orchestration delays pose challenges. The study confirms that AI-empowered NFV enhances data modernization by providing adaptable, efficient, and compliant infrastructure. Future investigations should focus on multi-branch deployments, continuous learning with live data, and explainable AI safeguards.