The rapid integration of artificial intelligence into financial management, accounting, and audit functions has fundamentally reshaped how organizations detect anomalies, perform reconciliations, manage risks, and ensure governance integrity. As enterprises face increasing data volumes, regulatory complexity, and pressure for faster financial closes, traditional rule-based and manual approaches have become insufficient. This research article develops a comprehensive, theory-driven, and empirically grounded examination of AI-enabled financial anomaly detection and reconciliation frameworks, drawing strictly from established professional surveys, governance frameworks, and peer-reviewed academic literature. Anchored in insights from global benchmarking studies, financial close surveys, AI governance standards, and machine learning research, the article explores how artificial intelligence transforms financial planning and analysis, bank reconciliation, fraud detection, and internal control systems.

The study adopts a qualitative, integrative research methodology that synthesizes findings from industry surveys by PwC and EY, conceptual models from COSO and NIST, and advanced machine learning approaches such as federated learning, autoencoders, naïve Bayes classifiers, and explainable AI techniques like SHAP. Rather than presenting mathematical formulations or empirical datasets, the article offers an extensive descriptive analysis of how these technologies operate within real organizational contexts, emphasizing governance, explainability, data privacy, and risk management. Particular attention is given to the tension between automation efficiency and human judgment, the evolving role of finance professionals, and the necessity of trustworthy AI systems in high-stakes financial environments.

The findings indicate that AI-driven anomaly detection significantly enhances the accuracy, timeliness, and scalability of financial oversight processes, while also introducing new categories of operational, ethical, and regulatory risk. Governance frameworks and internal controls emerge as essential mediating mechanisms that align technological capabilities with organizational accountability. The discussion highlights limitations related to data quality, model bias, explainability challenges, and cross-jurisdictional compliance, while outlining future research directions focused on hybrid human–AI audit models and globally harmonized AI governance structures. This article contributes to academic literature by offering a unified conceptual foundation for understanding AI-assisted financial anomaly detection and reconciliation as a socio-technical system rather than a purely technological innovation.