Automated Fiscal Compliance for Romanian Micro-Enterprises: An Integrated Blockchain, AI and IoT Architecture

Petronela Alice Grigorescu, Ruxandra Georgescu (Grigorescu), Alexandru Cătălin Neagu and Dan Marius Coman
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Petronela Alice Grigorescu: Doctoral School of Economics and Humanities, Valahia University of Târgoviște, Romania
Ruxandra Georgescu (Grigorescu): Doctoral School of Economics and Humanities, Valahia University of Târgoviște, Romania
Alexandru Cătălin Neagu: Doctoral School of Economics and Humanities, Valahia University of Târgoviște, Romania
Dan Marius Coman: Doctoral School of Economics and Humanities, Valahia University of Târgoviște, Romania

Economics and Applied Informatics, 2026, issue 1, 29-38

Abstract: Fiscal compliance represents a critical yet operationally demanding requirement for micro-enterprises, which typically lack dedicated accounting infrastructure. This article presents a functional proof-of-concept platform integrating Blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT) to automate fiscal compliance workflows. The prototype was developed using a Design Science Research methodology and validated through a simulated dataset of 18 financial transactions representative of Romanian micro-enterprise operations. Machine learning anomaly detection achieved 94% accuracy with a 6% false positive rate. Smart contract execution yielded AI decision confidence ranging from 75% to 96% across decision categories, and projected annual savings of €3,450 with a reduction of 127 hours of manual labour per month. Findings confirm the technical feasibility and economic viability of integrated BC–AI–IoT compliance architectures, while identifying scalability and regulatory alignment as priority directions for future research.

Keywords: smart contracts; anomaly detection, tax administration, digital transformation, design science research (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2026:i:1:p:29-38

DOI: 10.35219/eai15840409572

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