Artificial Intelligence in Auditing : A Bibliometric Review of the State of the Art and Future Directions
DOI:
https://doi.org/10.71420/ijref.v3i10.424Keywords:
Artificial intelligence, Auditing, Machine learning, generative AI, Bibliometric analysis, Science mappingAbstract
The integration of artificial intelligence (AI) is profoundly reshaping audit practices, drawing growing scholarly attention. This article offers a bibliometric analysis of research on the integration of AI into auditing, with the aim of mapping its structure and dynamics, and the areas that remain underexplored. The study draws on a corpus of 596 documents published between 2016 and 2026, retrieved from the Scopus and Web of Science databases under a PRISMA protocol and analysed using the R package bibliometrix and its biblioshiny interface. The findings reveal a young yet rapidly expanding and inherently interdisciplinary field. The analysis traces the progression of AI techniques, from data analytics and machine learning to generative artificial intelligence, its most recent frontier. While the technical capabilities of AI have been widely studied, the associated risks (cybersecurity and data confidentiality), together with the training of auditors, remain largely overlooked. Geographically, output is dominated by a handful of major scientific powers, whereas African countries, and Morocco in particular, remain weakly represented. This study provides a structured overview of a field still under construction and identifies several priority avenues for future research, foremost among them the management of risks and the training of professionals.
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