Tax administrations and compliance-sensitive businesses face the same operational dilemma: revenue risk is concentrated in a minority of returns, yet audit resources are limited and many small and medium-sized enterprise (SME) errors arise from weak recordkeeping, filing complexity and digital capability gaps rather than deliberate evasion. This article develops a data-driven framework that converts taxpayer mismatches into a structured revenue-protection system. The framework integrates third-party data matching, tax-type risk features, estimated revenue-at-risk ranking, audit exception dashboards and targeted SME education. It is grounded in tax-compliance theory, compliance risk management, evidence on third-party reporting, behavioral compliance research and recent work on explainable machine learning for tax and audit planning. Using an illustrative synthetic dataset of 5,400 taxpayer-period records, the article demonstrates how risk heat maps, revenue-at-risk matrices, a risk-decile capture curve and education-need segmentation can support audit prioritization while preserving voluntary compliance. The results show how compliance teams can distinguish cases requiring enforcement from cases better suited to correction notices, desk review or education. The framework contributes a practical operating model for revenue authorities, tax-compliance units and advisory teams seeking to protect public revenue without imposing unnecessary compliance costs on lower-risk taxpayers.
Ganyani et al. (Tue,) studied this question.
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