AI-Enabled Data Governance as the Foundation of Reliable Financial Decision Systems

Abstract

Artificial intelligence is reshaping financial analysis, yet the value of advanced algorithms depends heavily on the quality of the data and governance systems surrounding them. This review examines how artificial intelligence strengthens data governance and how governance maturity converts technical capability into dependable financial decisions. The discussion integrates evidence on data quality, metadata, lineage, privacy, security, regulatory compliance, and organisational accountability across banking, insurance, payments, capital markets, and financial technology. It argues that artificial intelligence should not be treated as an independent decision engine. Instead, it functions most effectively as part of a socio-technical governance architecture that connects automated classification, anomaly detection, risk analytics, and compliance monitoring with human responsibility and institutional controls. Evidence from the source review indicates strong relationships between artificial intelligence integration, governance capability, and financial outcomes, with governance acting as a central pathway through which algorithmic capacity becomes measurable decision value. The review also considers differences between early-stage and mature institutions, practical implementation priorities, policy needs, and unresolved research gaps. It concludes that trustworthy financial intelligence requires simultaneous investment in models, data stewardship, transparent controls, and continuous oversight.