AI-Driven Data Quality Monitoring and Anomaly Detection for Financial and Analytics Systems

Abstract

Enterprise financial and analytics platforms depend on continuous, high-fidelity data validation across ingestion, transformation, and reporting layers. Most organizations still keep deterministic reconciliation controls and statistical or machine-learning-based anomaly detection in separate toolchains, an arrangement that leaves each approach blind to the failure class the other is built to catch. This article examines how enterprise data governance programs can structurally combine rule-based validation with adaptive anomaly detection to meet the operational demands of regulated financial and analytics environments. Drawing on established data quality theory, current enterprise governance frameworks, and recent empirical work on machine-learning-based anomaly detection, in which reconstruction-error-based detection methods have reported AUC values as high as 0.963 against conventional sequence-model baselines, the analysis develops a three-layer reference architecture: a deterministic validation layer for schema and completeness checks, a statistical and machine-learning layer for drift and anomaly scoring, and a governance layer that enforces policy and preserves audit accountability. The comparative review finds that layered approaches combining reconciliation-grade controls with adaptive detection consistently report stronger operational outcomes than either method used alone, particularly in reducing undetected data-quality incidents within high-volume transactional environments. The discussion situates this architecture within the practical constraints of enterprise data quality platforms and identifies where purely statistical methods fall short of audit and compliance expectations. The article contributes a practitioner-oriented synthesis intended for data engineering and governance teams responsible for maintaining data integrity in enterprise financial and analytics systems.