Core banking data quality assessment: Automated validation frameworks for ML-ready datasets

This article presents a comprehensive framework for automated data quality assessment in core banking systems, focusing on preparing high-quality datasets for machine learning applications. We examine the unique challenges of banking data validation, including regulatory compliance, security requirements, and the complex relationships between financial data entities. The proposed framework integrates traditional banking data governance principles with modern machine learning validation techniques to create a robust system for ensuring data readiness. Through case studies, empirical analysis, and practical implementation guidelines, we demonstrate how financial institutions can leverage automated validation to improve decision-making processes, risk assessment, and customer experience while maintaining data integrity and compliance.