Data Governance Strategies for AI/ML in Banking Applications

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into banking operations has transformed the industry, offering new avenues for customer engagement, risk assessment, and operational efficiency. However, the success of AI/ML implementations in the banking sector is contingent upon robust data governance strategies that ensure data quality, integrity, and compliance. This paper explores the essential data governance strategies required to effectively harness the potential of AI/ML in banking applications. It delves into the intricacies of data governance frameworks, data quality management, regulatory compliance, and the role of human oversight in AI/ML-driven banking. By elucidating the critical interplay between data governance and AI/ML, this paper provides valuable insights for banking institutions seeking to maximize the benefits of AI/ML technologies while upholding data integrity and industry standards.