Strategies for Maintaining Data Integrity in AI/ML-Driven Banking Transactions

Abstract

The integration of Artificial Intelligence and Machine Learning (AI/ML) technologies into banking transactions has revolutionized the financial sector, enhancing efficiency and customer experiences. However, ensuring the integrity of data in AI/ML-driven banking transactions is paramount to maintain trust, prevent errors, and comply with regulatory standards. This paper presents a comprehensive analysis of strategies for maintaining data integrity in AI/ML-driven banking transactions. Through a combination of data governance, quality assurance, and technology-driven approaches, banks can establish a robust framework that safeguards data integrity throughout the transaction lifecycle. The paper explores key challenges, such as data inaccuracies and biases, and proposes effective solutions to address them. By prioritizing data integrity, banks can harness the full potential of AI/ML technologies while upholding ethical and regulatory principles.