Cloud Based AI Policy Engine Platform for Real-Time Compliance in Financial and Healthcare Transactions

The dramatic escalation in the velocity and volume of financial and healthcare transactions has rendered traditional retrospective audit models increasingly ineffective, incurring regulatory risk, operational inefficiency, and mounting penalties. This paper presents a technical and architectural framework for a cloud-based, AI-driven policy engine designed for real-time compliance monitoring of financial (e.g., payments, loans) and healthcare (e.g., electronic health record [EHR] access) transactions. The system leverages rule-aware artificial intelligence—combining machine learning (ML), natural language processing (NLP), and policy-as-code—to monitor data streams, interpret evolving regulatory mandates, and detect or flag non-compliant events as they occur. The platform’s architecture emphasizes scalable ingestion and processing, adaptive policy enforcement, and robust security with comprehensive auditability to address sector-specific regulatory regimes such as HIPAA, GLBA, PCI DSS, and the EU AI Act. This work analyzes the state of the art, describes the detailed implementation of the core system and subsystems (with diagrams), proposes metrics for objective evaluation, and critically examines technical and operational challenges including explainability, integration, privacy, and fairness.