Mitigating Risks with Data Integrity as Code (DIaC) in Banking AI/ML

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in the banking sector has revolutionized operations, offering enhanced customer experiences, risk management, and fraud detection. However, this evolution presents new challenges, particularly in ensuring data integrity, privacy, and regulatory compliance. To address these challenges, a novel approach known as Data Integrity as Code (DIaC) is gaining prominence. DIaC encompasses automated data quality checks, lineage tracking, and governance enforcement embedded directly into AI/ML workflows. This paper explores the application of DIaC in banking AI/ML implementations, highlighting its potential to mitigate risks, enhance transparency, and elevate customer trust. Through real-world examples and case studies, the paper demonstrates how DIaC can effectively address data-related challenges, drive regulatory compliance, and foster responsible AI deployment. By embracing DIaC, banks can fortify their AI-powered initiatives, ensuring data integrity from inception to decision-making and propelling the industry toward a future of secure and accountable AI/ML innovations.