Journal of Information System and Technology Auditing

Journal of Information System and Technology Auditing

Innovative Developments in IT Auditing: The Role of Artificial Intelligence in Enhancing Digital Auditing Processes

Document Type : Original Article

Author
Assistant Professor, Islamic Azad University, Bardaskan Branch
Abstract
With the expansion of digital technologies, Information Technology (IT) auditing has become a core pillar of financial oversight and internal control within organizations. The rapid development of Big Data, digital financial systems, electronic banking, and blockchain technology has introduced new levels of complexity and significant challenges for traditional auditing approaches. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, playing a critical role in enhancing audit accuracy, reducing human error, and optimizing auditing processes. This study adopts a review-based approach and employs a systematic literature review methodology to examine the applications of artificial intelligence in IT auditing, along with its benefits, challenges, and future prospects. Data were collected from reputable international academic sources and analyzed using qualitative analysis techniques. Accordingly, various machine learning models, Natural Language Processing (NLP) methods, and Big Data analytics techniques were comparatively evaluated to assess their effectiveness in auditing processes. The findings indicate that machine learning algorithms significantly outperform traditional auditing methods in fraud detection, financial pattern analysis, risk assessment, and financial report processing. Moreover, NLP enables the rapid and accurate analysis of financial documents, while Big Data analytics facilitates the identification of financial anomalies across large and complex datasets. Despite these advantages, the widespread implementation of AI-based auditing systems faces several challenges, including high implementation costs, technical complexity, legal constraints, ethical concerns, and the need for specialized training of auditors. These barriers remain critical factors limiting the large-scale adoption of artificial intelligence in IT auditing practices.
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Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement. Accounting Horizons, 31(3), 1–16.
https://doi.org/10.2308/acch-51641
Dai, J., & Vasarhelyi, M. A. (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 521. https://doi.org/10.2308/isys-51804     
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing. Journal of Emerging Technologies in Accounting, 13(2), 1–20.https://doi.org/10.2308/jeta-10511
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.https://doi.org/10.2308/jeta-51730
Moffitt, K. C., & Vasarhelyi, M. A. (2013). AIS in an age of big data. Journal of Information Systems, 27(2), 1–19. https://doi.org/10.2308/isys-10372
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
Sutton, S. G., Holt, M., & Arnold, V. (2016). Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60–73. https://doi.org/10.1016/j.accinf.2016.07.005
Tang, J., Karim, K., & Rutledge, R. W. (2021). Machine learning applications in auditing: A systematic review. International Journal of Accounting Information Systems, 40, 100503. https://doi.org/10.1016/j.accinf.2021.100503
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381–96.https://doi.org/10.2308/acch-51071
Volume 1, Issue 2 - Serial Number 2
September 2026
Pages 314-326

  • Receive Date 16 October 2025
  • Revise Date 16 December 2025
  • Accept Date 28 February 2026
  • Publish Date 23 September 2025