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Artificial Intelligence and Auditing: A Bibliometric Study

Nicolas Epelbaum & Penny Farrell

Accounting Perspectives2026https://doi.org/10.1111/1911-3838.70008article
ABDC B
Weight
0.50

Abstract

Artificial intelligence (AI) tools are used to assist auditors in analyzing large amounts of data, automating audit tasks, and identifying risks and opportunities. AI can help improve the efficiency and accuracy of the auditing process as well as create value for both audit firms and their clients. Research on AI has grown rapidly over recent decades, yet the conceptualization and research structure remain disintegrated. This study aims to identify the current state of knowledge on AI and auditing using a bibliometric analysis. We analyze 219 papers collected using the Scopus database and highlight the most relevant manuscripts, countries, keywords, and journals and the most influential authors in this domain. The findings also highlight specific themes and elucidate gaps for future research on AI and auditing.

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https://doi.org/https://doi.org/10.1111/1911-3838.70008

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@article{nicolas2026,
  title        = {{Artificial Intelligence and Auditing: A Bibliometric Study}},
  author       = {Nicolas Epelbaum & Penny Farrell},
  journal      = {Accounting Perspectives},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/1911-3838.70008},
}

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Artificial Intelligence and Auditing: A Bibliometric Study

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.