The accelerating digitization of enterprises and financial institutions has resulted in unprecedented volumes, velocities, and varieties of data flowing through organizational information systems. While these developments have enabled sophisticated analytics, automation, and artificial intelligence–driven decision-making, they have simultaneously magnified the risks associated with poor data quality, weak governance structures, and undetected anomalies. Anomalies in enterprise and financial data—ranging from benign system glitches to critical indicators of fraud, cyber intrusion, or process failure—pose significant threats to organizational integrity, regulatory compliance, and stakeholder trust. This research article presents a comprehensive and theoretically grounded exploration of how data quality foundations, governance mechanisms, and modern anomaly detection techniques can be systematically integrated to support reliable, AI-driven financial and enterprise information systems.

Drawing strictly on established literature in anomaly detection, novelty detection, data quality, record linkage, data governance, enterprise system modernization, and recent advances in AI-assisted financial reporting, this study synthesizes diverse conceptual streams into a unified analytical framework. Classical perspectives on anomaly detection and novelty detection are revisited to establish foundational definitions and taxonomies, emphasizing their relevance across structured enterprise data, transactional financial records, and complex IT system logs. These perspectives are then connected to data quality theory, highlighting how dimensions such as accuracy, completeness, consistency, timeliness, and validity directly influence the performance and interpretability of machine learning–based anomaly detection models.

The article further examines entity resolution, record linkage, and matching dependencies as critical enablers of trustworthy anomaly detection, particularly in large-scale, heterogeneous enterprise environments. By integrating insights from ERBlox and foundational record linkage literature, the discussion demonstrates how unresolved entity ambiguity can propagate false anomalies or conceal genuine risks. In parallel, the role of data governance is explored as an institutional and organizational scaffold that ensures accountability, standardization, and ethical oversight for AI-driven anomaly detection systems.

Special attention is given to financial and accounting domains, where recent research highlights the growing adoption of deep learning architectures, autoencoders, generative models, and hybrid machine learning frameworks for anomaly detection in transactions, accounting entries, and IT systems. These approaches are critically analyzed not as isolated technical solutions, but as socio-technical systems whose effectiveness depends on data quality controls, governance maturity, and alignment with regulatory requirements such as multi-GAAP reconciliation and financial close processes.

Methodologically, this article adopts a qualitative, integrative research design grounded in extensive theoretical elaboration and cross-domain synthesis. Rather than proposing new algorithms, it provides a detailed interpretive analysis of how existing methods interact with organizational data ecosystems. The results are presented as a set of conceptual findings that articulate causal and reinforcing relationships between governance structures, data quality practices, and anomaly detection performance. The discussion situates these findings within broader debates on explainability, trust, scalability, and ethical AI in enterprise contexts, while also acknowledging limitations related to empirical validation and contextual variability.

The article concludes by arguing that the future of AI-driven financial and enterprise systems depends not on incremental improvements in detection accuracy alone, but on holistic integration of data quality theory, governance design, and anomaly detection methodologies. Such integration is positioned as essential for achieving resilient, transparent, and trustworthy information systems capable of supporting strategic decision-making in increasingly complex digital environments.