Financial Reporting and XBRL
Open access
Peer reviewed
Abstract
Abstract
Purpose : This paper aims to map the evolution of artificial intelligence (AI) in accounting research through a corpus-wide and decade-based thematic analysis. We identify dominant themes, key contributors, recent research hotspots, and chart future research trajectories that link historical shifts to emerging priorities. Methodology/approach : We conducted a bibliometric analysis of a SCOPUS dataset (1984–2024). Using a PRISMA screening protocol, 2,863 records were reduced to 451 peer-reviewed articles, followed by thematic synthesis and network mapping in VOSviewer. Findings : Research accelerates sharply after 2015, paralleling advances in machine learning and data availability. The analysis identified four thematic clusters: (1) financial analysis and decision support; (2) regulatory and strategic implications; (3) education and professional adaptation; and (4) digital transformation of accounting information systems. Thematic evolution progresses from early expert systems toward dense, multidisciplinary data-driven ecosystems. Hotspots between 2020 and 2024 include generative AI, automated sustainability reporting, blockchain applications, and predictive analytics, with implications for reporting accuracy, compliance, automation, and skills. Research limitations/implications : Reliance on a single database (SCOPUS) may exclude relevant studies, while PRISMA-based screening and eligibility decisions introduce subjectivity in coverage. Practical implications : The findings are relevant for regulators, educators, and practitioners by outlining emerging themes, governance/ethics, and competency implications. Originality/value : This research delivers an up-to-date, corpus-wide, decade-resolved map that offers a consolidated baseline and practical roadmap for future research and expectations in AI-based accounting.
Direct answer
What can I do from this paper page?
Use this page to scan "Artificial Intelligence in accounting: A corpus-wide bibliometric review and decade-by-decade thematic evolution" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Financial Reporting and XBRL research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Tocev2026Artificial,
title = {Artificial Intelligence in accounting: A corpus-wide bibliometric review and decade-by-decade thematic evolution},
author = {Todor Tocev and Atanasko Atanasovski},
journal = {Zeszyty Teoretyczne Rachunkowości},
year = {2026},
doi = {10.5604/01.3001.0055.7853},
url = {https://doi.org/10.5604/01.3001.0055.7853}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Financial Reporting and XBRL research papers?
Follow Financial Reporting and XBRL research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Artificial Intelligence in accounting: A corpus-wide bibliometric review and decade-by-decade thematic evolution. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app