Leveraging Data Analytics for Tax Compliance Monitoring in Public Revenue Administration

Abstract

This study presents evidence-based analysis of data-driven tax compliance monitoring within public revenue systems, emphasizing rigorous methodology, transparent reporting, and measurable outcomes. Emerging trends in the digitalization of the economy and public service delivery increasingly motivate the use of big data analytics by tax authorities for efficient revenue collection, including compliance monitoring. Political pressure or judicial rulings often push decision-making and applied analytics into unsafe territories. In contrast, peer-reviewed modelling and analysis support legitimate and transparent data-driven compliance monitoring. Digital economy taxation gap analytics is one area gaining widespread research and implementation attention. Another core aspect of tax compliance risk assessment is the detection of tacit and explicit noncompliance. Risk-assessment support for audit selection or the identification of high-risk sectors and segments is increasingly provided by data-analytical methods.

While demonstrated in a financial-supply-chain context, the approach is transferable beyond the scope and context of any one implementation. Empirical examinations validate the proposed concepts, metrics, and models, establishing the methodological foundation for a broader application across public revenue systems. Supply-side data companies maintain data about every firm across digital supply chains. Cross-jurisdictional public-sector data-sharing silos constitute uncontested evidence of tax gap size and sector non-compliance risk or willingness summaries. Non-participation in regulatory audits, data-feedback mechanisms, and information-exchange strategies results in an ever-increasing compliance gap. Tax administrations and regulators have decision responsibility and operational authority for compliance governance, yet any failure to embrace any one of the risk-and-remediation strategies should also be held to account.