Ensuring Data Quality and Integrity in AI/ML Banking Implementations

Abstract

Data quality and integrity are paramount in the successful implementation of AI/ML technologies within the banking sector. As financial institutions increasingly rely on AI/ML algorithms to make critical decisions, the accuracy and reliability of the underlying data become essential to ensure fair, transparent, and accountable outcomes. This paper explores the challenges and strategies for ensuring data quality and integrity in AI/ML banking implementations. It discusses the importance of data preprocessing, feature engineering, and model validation to mitigate biases, errors, and inconsistencies in the data. Additionally, the paper presents regulatory considerations and industry best practices that contribute to maintaining data quality and integrity throughout the AI/ML implementation lifecycle. By adopting a comprehensive approach to data quality and integrity, banks can enhance customer trust, optimize operational efficiency, and achieve robust and ethical AI/ML-powered solutions.