EXPLAINABLE AI FOR ENTERPRISE FINANCIAL COMPLIANCE GOVERNANCE: A CONCEPTUAL FRAMEWORK FOR TRANSPARENT REGULATORY DECISION-MAKING

Abstract

The increasing complexity of financial regulations has created significant challenges for traditional compliance governance systems. Artificial Intelligence (AI) offers enhanced capabilities for risk detection, regulatory monitoring, and automated compliance analysis; however, limited transparency creates concerns regarding accountability and trust. This research develops a conceptual framework for Explainable AI (XAI)-enabled financial compliance governance using secondary literature analysis. The framework integrates AI compliance intelligence, explainability mechanisms, and governance controls to support transparent regulatory decision-making. The study highlights how XAI can improve auditability, decision justification, and responsible AI adoption within enterprise financial environments.