BEYOND AUDIT AUTOMATION: MAPPING THE EMERGING LANDSCAPE OF ARTIFICIAL INTELLIGENCE RESEARCH IN AUDITING
DOI:
https://doi.org/10.35631/AIJBES.828030Keywords:
Artificial Intelligence, Auditing, Bibliometric Analysis, Machine LearningAbstract
Artificial intelligence (AI) is swiftly reshaping the auditing profession by strengthening audit efficiency, elevating the ability to detect fraud, and supporting more data-driven decision-making processes. Although there is growing scholarly interest in AI applications within auditing, the existing literature remains fragmented across multiple disciplines, resulting in a limited understanding of thematic evolution, intellectual structure, as well as the patterns of international research collaboration within the field. Accordingly, this study seeks to deliver an extensive bibliometric examination of AI scholarship within auditing published between 2006 and 2025. Using a PRISMA-informed screening approach, bibliographic records were extracted from the Scopus database via a sophisticated search strategy incorporating keywords associated with artificial intelligence and auditing. After applying systematic inclusion and exclusion criteria, a final corpus comprising 597 journal articles was selected for analytical purposes. The study employed Scopus Analyzer, OpenRefine, and VOSviewer software to examine publication trends, citation structures, co-authorship networks, as well as patterns of keyword co-occurrence. The results indicate a pronounced exponential upsurge in research on AI auditing, particularly after 2020, reflecting the increasing adoption of digital technologies within audit practices. The United States stood out as the leading contributor in publication volume, citation influence, and the robustness of international collaborative networks, with the United Kingdom and China ranking subsequently in that order. Keyword co-occurrence analysis further demonstrated that the field has evolved beyond technical automation themes toward broader discussions involving governance, explainability, ethical accountability, generative AI, and human–AI collaboration. Highly cited studies also indicate a strong interdisciplinary orientation integrating accounting, information systems, governance, and business ethics perspectives. Overall, this study delivers a structured and comprehensive examination of the intellectual evolution and developing research trajectories of artificial intelligence within the auditing field, yielding meaningful insights for scholars, practitioners, regulators, and policymakers aiming to facilitate the responsible and efficient incorporation of AI into auditing contexts.
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