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Artificial intelligence in public finance: A bibliometric exploration

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  • Azwar Azwar

  • Abur Hamdi Usman

Abstract

Artificial intelligence (AI) is increasingly transforming public finance, influencing transparency, efficiency, and decision-making in government financial management. This study maps the research landscape on AI applications in public finance to identify dominant trends, including patterns in publication growth, commonly discussed topics, contributions from leading institutions and researchers, emerging areas of inquiry, and underexplored domains. The study utilizes bibliometric analysis of Scopus-indexed publications from 2015 to 2025. The results show rapid growth in research output, with dominant themes including AI applications in taxation, budgeting, performance forecasting, and financial integration, while emerging topics such as ethics, sustainable development goals, carbon emissions, and pandemic-related fiscal strategies are gaining prominence. Conversely, AI’s role in poverty alleviation, inflation control, and tax risk management remains underexplored. The findings suggest that AI can enhance audit capabilities, strengthen policy evaluation, and improve public sector accountability. Theoretically, this research expands the intersection of AI and public finance governance, while practically, it offers policymakers insights to prioritize AI-driven reforms. The novelty lies in providing a comprehensive bibliometric mapping that identifies strategic research gaps, guiding future studies toward areas with high potential for innovation and policy impact.

Suggested Citation

  • Azwar Azwar & Abur Hamdi Usman, 2025. "Artificial intelligence in public finance: A bibliometric exploration," Jurnal Tata Kelola dan Akuntabilitas Keuangan Negara, Badan Pemeriksa Keuangan Republik Indonesia, vol. 11(2).
    • Handle: RePEc:bsa:jtaken:v:11:y:2025:i:2:id:2059
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    References listed on IDEAS

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    1. Mukherjee, Debmalya & Lim, Weng Marc & Kumar, Satish & Donthu, Naveen, 2022. "Guidelines for advancing theory and practice through bibliometric research," Journal of Business Research, Elsevier, vol. 148(C), pages 101-115.
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