An AI-Based Enterprise Data Quality Framework for Master Data Governance in Financial Institutions

High-quality master data is fundamental to operational efficiency, regulatory compliance, customer experience, and strategic decision-making within financial institutions. However, organizations frequently encounter challenges related to duplicate records, inconsistent data standards, incomplete customer information, and fragmented enterprise data repositories. Conventional data quality management approaches often depend on manually defined validation rules that struggle to adapt to evolving business environments and increasing data complexity. This paper presents an Artificial Intelligence-based Enterprise Data Quality Framework designed to strengthen master data governance through intelligent automation and predictive data quality assessment. The proposed framework combines machine learning-based anomaly detection, duplicate record identification, entity resolution, intelligent data profiling, and automated quality scoring to improve the accuracy and consistency of enterprise master data. In addition, explainable artificial intelligence techniques are incorporated to provide transparency into automated quality recommendations and facilitate governance decisions. The framework is evaluated using representative financial institution master data scenarios involving customer, vendor, and account information. The results demonstrate improvements in duplicate detection, data completeness, consistency assessment, and governance efficiency while reducing manual validation effort. The proposed framework offers financial organizations a scalable approach for modernizing enterprise data governance and supporting reliable data-driven decision-making.

Data Quality, Master Data Management, Data Governance, Artificial Intelligence, Machine Learning, Entity Resolution, Financial Institutions, Enterprise Data Management

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