Inteligência fiscal e análise de dados aplicadas à fiscalização tributária municipal

Vollständiger Abstract

Worum geht es in dieser Arbeit?

The modernization of municipal tax administrations has expanded the use of electronic documents, database cross-checking, artificial intelligence, and risk models to identify tax inconsistencies. This study aimed to analyze applications of fiscal intelligence and data analytics in municipal tax inspection, considering their effects on revenue collection efficiency, taxpayer selection, fraud prevention, and information governance. A qualitative, descriptive-analytical narrative literature review was conducted through searches performed in August 2026 in SciELO, the CAPES Journal Portal, and Crossref. Ten original articles published between 2020 and 2026 and ten relevant legal instruments were included. The thematic analysis indicated that the Electronic Service Invoice improved the timeliness of records, reduced manual tasks, and enhanced control of the Tax on Services of Any Nature. Data cross-checking, anomaly detection, supervised learning, and network analysis improved audit screening but did not constitute automatic evidence of an infringement. The findings further indicated that data quality, institutional capacity, explainability, and human oversight conditioned system reliability. The discussion found that revenue collection efficiency and data protection were not incompatible objectives, provided that information processing complied with purpose limitation, security, and administrative oversight. It was concluded that technology adoption can improve municipal tax audits, reduce operating costs, and strengthen tax compliance. Limitations remained regarding the scarcity of municipal investigations and the absence of comparable primary data across different localities.