REAL-TIME COMPLIANCE MONITORING IN BANKING OPERATIONS USING AI

The increasing complexity of regulatory requirements in the banking sector has created a critical need for real-time, automated compliance solutions. Traditional compliance mechanisms, which rely on periodic audits and manual rule enforcement, are proving inadequate in detecting violations promptly or adapting to dynamic regulations. This paper proposes an AI-driven framework for real-time compliance monitoring that leverages machine learning, natural language processing, and anomaly detection techniques to ensure continuous supervision of banking operations. The system is designed to ingest and process live transactional data, analyze regulatory texts using NLP, detect suspicious behavior patterns, and adapt to evolving compliance rules through continuous learning. Experimental evaluations demonstrate the effectiveness of the framework in identifying policy violations with high accuracy and low latency, while case studies highlight its practical relevance across multiple banking scenarios. The proposed solution significantly enhances operational transparency, auditability, and regulatory responsiveness, ultimately reducing risks of non-compliance in financial institutions.