The Future of Fiscal Governance: AI and G2G Platforms in Digital Public Administration

Abstract

This research develops the application of artificial intelligence (AI), Big Data, and Government-to-Government (G2G) platforms in Peru’s digital tax administration, emphasizing interoperability among SUNAT, RENIEC, and SUNARP as a key condition to optimize revenue collection and reduce the taxpayer’s administrative burden. From its public and academic relevance, the study addresses concepts of automation, tax efficiency, compliance, and transparency, and formulates a general objective aimed at analyzing, through a literature review, the effectiveness of AI, Big Data, and G2G in fiscal management. Specifically, it examines the contributions of AI/Big Data to efficiency, the role of G2G in inter-institutional coordination, the benefits, challenges, and limitations of their integration, and systematizes the evidence to propose a framework applicable to the Peruvian context. The methodology follows a qualitative approach with a non experimental, analytical, deductive, and inductive methods through content analysis and comparative synthesis of scientific literature, current regulations, and institutional experiences. The corpus includes 25 studies (2018–2025) selected for relevance and full-text availability. The main finding is a G2G model proposal supported by AI and Big Data that interconnects the intranets and databases of SUNAT, RENIEC, and SUNARP for the secure, timely, and automated exchange of fiscally relevant information, ensuring traceability and access control, aligned with Peru’s documentary, and cross-sectional design, exploratory–descriptive–propositional scope, and combines legal framework (Supreme Decree No. 164-2021-PCM, Legislative Decree No. 1412, Law No. 31814, and axes 5 and 8 of the 2021 2026 General Government Policy).