Anomaly Detection In Revenue Systems: Data-Driven Compliance And Cloud Governance

The study investigates how anomaly detection in revenue systems can be leveraged as a tool for ensuring data-driven compliance and strengthening cloud governance. It aims to identify systemic weaknesses, evaluate compliance risks, and assess the role of governance maturity in mitigating revenue and regulatory vulnerabilities. A mixed-method framework was adopted, integrating statistical and machine learning techniques for anomaly detection with compliance mapping and governance maturity assessment. Data from transaction records, tax entries, billing logs, and cloud infrastructure metrics were analyzed. Statistical models, clustering, and regression analysis were applied, alongside principal component analysis (PCA) to visualize compliance-driven anomaly clustering. Results show that anomalies in transactions, tax records, and invoices significantly predict compliance breaches, while governance maturity plays a mitigating role. Advanced detection methods, particularly autoencoders and isolation forests, outperformed traditional statistical approaches in precision and recall. Compliance evaluation highlighted PCI DSS adherence and audit log completeness as the most vulnerable areas, while encryption and access control achieved higher governance maturity scores. PCA revealed clear clustering of anomalies based on compliance severity, enabling risk-informed prioritization. The study underscores the importance of embedding anomaly detection into compliance frameworks and cloud governance practices. Organizations adopting data-driven approaches can strengthen revenue assurance, enhance regulatory adherence, and improve trust in digital financial ecosystems. This research advances the integration of anomaly detection with compliance and governance, shifting the focus from isolated irregularity detection to holistic, policy-driven revenue system assurance in cloud environments.