Transforming Accounting through Artificial Intelligence: A Bibliometric Analysis of Trends, Challenges, and Opportunities

Riana Iren Radu et al.

Economics and Applied Informatics2025https://doi.org/10.35219/eai15840409485article
ABDC C
Weight
0.37

What the paper says

This paper investigates through an in-depth bibliometric research the transformative effect of artificial intelligence (AI) on the accounting profession.By analyzing more than 1,500 papers retrieved from the Web of Science database, the study reveals significant fieldspecific trends, emerging topics, and cooperative networks.Results highlight a significant increase in academic interest post-2020, with a focus on automating accounting processes, fraud detection, and cloud integration.The analysis reveals that countries such as the United States, China, and the United Kingdom lead global collaborations, contributing to the development of innovative solutions.The bibliometric methodology utilized, encompassing tools like RStudio and Bibliometrix, provided insights into temporal and geographic patterns, as well as thematic correlations.Key findings emphasize the dual role of AI in enhancing operational efficiency and redefining professional competencies within the field of accounting.However, challenges such as the lack of technological skills and data security risks remain prominent.This study underscores the need for an integrated approach combining technological innovation, ethical considerations, and continuous professional development to harness AI's potential fully.The findings offer valuable guidance for researchers and practitioners aiming to navigate the dynamic interplay between AI and accounting, fostering innovation and addressing global economic demands.

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https://doi.org/https://doi.org/10.35219/eai15840409485

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@article{riana2025,
  title        = {{Transforming Accounting through Artificial Intelligence: A Bibliometric Analysis of Trends, Challenges, and Opportunities}},
  author       = {Riana Iren Radu et al.},
  journal      = {Economics and Applied Informatics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.35219/eai15840409485},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.