TRANSFORMING TRADITIONAL COMPLIANCE PRACTICES THROUGH ARTIFICIAL INTELLIGENCE-ENABLED GOVERNANCE FOR ENTERPRISE FINANCIAL RISK MANAGEMENT AND REGULATORY CONTROL

Abstract

Enterprise financial compliance is increasingly challenged by complex regulations, growing transaction volumes, and limitations of traditional governance mechanisms. This research examines how artificial intelligence-enabled governance can transform conventional compliance practices into proactive financial risk management systems. Based on secondary analysis and conceptual synthesis, the study evaluates AI capabilities, governance requirements, and regulatory integration challenges. A conceptual framework is proposed by integrating AI intelligence, compliance monitoring, regulatory control, explainability, and enterprise governance mechanisms. The research contributes a structured understanding of trustworthy AI adoption for improving transparency, accountability, and regulatory effectiveness in modern financial environments.