Multi-Layered AI-enhanced compliance architecture for financial data engineering

Pulikonda, Naga Krishna Mahesh (2025) Multi-Layered AI-enhanced compliance architecture for financial data engineering. World Journal of Advanced Research and Reviews, 26 (2). pp. 3666-3673. ISSN 2581-9615

This article presents a comprehensive framework for regulatory intelligence and compliance automation in the financial services sector using large language models and cloud technologies. The article addresses the exponential growth in financial data volumes and the increasing complexity of regulatory frameworks by proposing a multi-layered security architecture that embeds AI capabilities throughout the compliance lifecycle. The framework integrates advanced identity and access intelligence, predictive data sensitivity tagging, intelligent monitoring with complete lineage tracking, and automated governance mechanisms to create a cohesive compliance ecosystem. Implementation on AWS demonstrates significant improvements in processing capacity, scalability, availability, and security while reducing operational costs. A case study of a tier-one financial institution highlights substantial efficiency gains in transaction processing and regulatory reporting accuracy. The article contributes valuable insights into the integration of AI and cloud technologies for next-generation financial compliance management while identifying future research directions and implications for compliance engineering practice.