An integrated framework for AI governance in economic institutions: Managing ethical, operational, and regulatory compliance risks

Abstract

This article proposes an integrated governance framework for artificial intelligence (AI) in economic institutions, addressing ethical, operational, and regulatory compliance risks. As AI systems increasingly shape decision-making in financial institutions, the paper argues that fragmented governance approaches are insufficient to manage emerging risks such as algorithmic bias, opacity, cybersecurity threats, and regulatory uncertainty. Drawing on international principles from organizations such as the OECD and the European Commission, the framework emphasizes responsible AI grounded in fairness, accountability, transparency, and data stewardship. The study introduces a structured risk taxonomy aligned with institutional objectives and fiduciary responsibilities, highlighting the need for coordinated oversight across internal governance bodies, regulators, and market participants. By integrating risk assessment, monitoring, auditability, and continuous improvement mechanisms across the AI lifecycle, the framework supports both innovation and compliance. The paper contributes to policy and practice by offering a scalable, evidence-based approach for aligning AI deployment with ethical standards, operational resilience, and evolving regulatory expectations in economic institutions.