Establishing Robust Data Governance Structures for Artificial Intelligence Deployment in Financial Institutions: A Compliance and Trust Perspective - Yogesh Kumar (International Journal of Computational and Experimental Science and Engineering, 2026)
The adoption of Artificial Intelligence in the banking industry has the potential to deliver significant benefits, but it also poses new challenges in terms of data governance. This article explores the importance of effective data governance frameworks for ensuring the integrity, security, and ethical use of data in AI implementations within the banking sector. It highlights the regulatory landscape, including the General Data Protection Regulation and the California Consumer Privacy Act, and the financial implications of non-compliance. The article discusses key principles of data governance for AI in banking, such as establishing clear policies, ensuring data quality, implementing access controls, and addressing data privacy and security concerns. It also emphasizes the importance of ethical considerations and the need for rigorous testing and monitoring of AI models. The article further examines best practices for integrating AI into existing data governance frameworks, including conducting risk assessments, establishing dedicated governance structures, defining roles and responsibilities, and investing in staff training. Finally, it underscores the importance of transparency and accountability in building trust among stakeholders and fostering a positive perception of AI adoption in the banking industry.