DIaC: Safeguarding AI/ML-Driven Banking Applications with Data Integrity

Abstract

As the banking industry embraces the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML), ensuring the integrity of data becomes paramount. Data Integrity as a Code (DIaC) emerges as a strategic framework to safeguard AI/ML-driven banking applications. This paper explores the integration of DIaC principles within banking operations, showcasing its role in upholding the accuracy, reliability, and trustworthiness of AI/ML models. Through proactive anomaly detection, continuous monitoring, and regulatory compliance, DIaC fosters a culture of data integrity that fortifies the foundation of AI/ML-powered banking innovations.