Enhancing Banking Operations with AI/ML: A Data Integrity Approach

Raj Singh

Raj Singh

Keywords: Artificial Intelligence, Machine Learning, Banking Operations, Data Integrity, Data Governance, Data Quality, AI/ML Applications, Financial Industry, Fraud Detection, Risk Assessment, Customer Service, Data Security, Data Consistency, Data Accuracy, Data Integrity Approach (DIaC), Algorithms, Decision-Making, Information Processing, Data Management, Banking Innovations

Abstract

This research paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into various aspects of banking operations, emphasizing a novel approach centered around data integrity. As the financial industry increasingly relies on AI/ML for decision-making, fraud detection, customer service, and risk assessment, ensuring the accuracy, consistency, and security of data becomes paramount. The proposed Data Integrity Approach (DIaC) leverages advanced algorithms and data governance strategies to uphold the quality of information processed and generated by AI/ML systems within the banking sector. This paper highlights the significance of maintaining data integrity as a foundational principle for AI/ML applications in banking, offering insights into the challenges, opportunities, and best practices in this domain.