Artificial Intelligence in Auditing: A Systematic Review of Tools, Applications, and Challenges

Authors

  • Windy Permata Suyono Universitas Negeri Jakarta
  • Eka Septariana Puspa Universitas Negeri Jakarta
  • Surya Anugrah Universitas Negeri Jakarta
  • Rio Firnanda

DOI:

https://doi.org/10.31004/riggs.v4i2.1024

Keywords:

Artificial Intelligence, Auditing Tools, Audit Applications, Audit Challenges, Machine Learning in Audit

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the auditing profession, offering innovative tools and applications that enhance audit efficiency, accuracy, and scope. This systematic literature review aims to comprehensively examine the current state of AI integration in auditing, focusing on the various AI tools utilized, practical applications within both internal and financial audits, and the challenges faced during implementation. Using a rigorous search and screening process across multiple academic databases, this study synthesizes findings from recent empirical and theoretical research published over the last decade. Results reveal a growing adoption of machine learning, natural language processing, and robotic process automation in audit processes, which contribute to improved fraud detection, risk assessment, and data analysis capabilities. However, challenges such as data privacy concerns, ethical considerations, lack of auditor competency in AI technologies, and regulatory uncertainties persist. This review highlights critical gaps in the literature, particularly the need for standardized frameworks to guide AI deployment and the development of auditor skills to effectively leverage AI tools. The study concludes with recommendations for future research and practical implications for auditors, firms, and policymakers aiming to harness AI’s full potential in auditing. This review contributes to advancing knowledge on AI’s role in modernizing audit practices and shaping the future of the auditing profession.

Downloads

Download data is not yet available.

References

Alles, T. (2015). The role of data analytics in auditing. Accounting Horizons, 29(2), 423–429.

Avasarala, V., Peffers, K., & Vasarhelyi, M. A. (2020). The impact of robotic process automation (RPA) on audit efficiency and quality. International Journal of Accounting Information Systems, 36, 100442. https://doi.org/10.1016/j.accinf.2020.100442

Bierstaker, J., Burnaby, P., & Thibodeau, J. (2014). The impact of machine learning on audit sample selection. Journal of Emerging Technologies in Accounting, 11(1), 1–15. https://doi.org/10.2308/jeta-50658

Bierstaker, J., Burnaby, P., & Thibodeau, J. (2014). The impact of machine learning on audit sampling. Managerial Auditing Journal, 29(1), 30–45.

Brehm, V., Wagner, A., & Vasarhelyi, M. (2019). Continuous auditing and robotic process automation: A review and research agenda. Accounting Horizons, 33(4), 117–132. https://doi.org/10.2308/acch-52377

Brown-Liburd, H., Issa, S., & Lombardi, S. (2015). Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451–468.

Cao, M., Chychyla, R., & Stewart, T. (2018). Big data analytics in financial statement audits. Accounting Horizons, 32(3), 97–115. https://doi.org/10.2308/acch-51923

Chen, J., Huang, T., & Wang, S. (2020). Machine learning models for fraud detection in auditing. Expert Systems with Applications, 162, 113832. https://doi.org/10.1016/j.eswa.2020.113832

Chen, Y., Huang, C., & Wang, Y. (2020). Applying machine learning techniques to audit fraud detection. International Journal of Accounting Information Systems, 37, 1–15.

Chui, M., Manyika, J., & Miremadi, M. (2019). The impact of AI on the future of compliance monitoring. McKinsey Quarterly, 5(2019), 42–57.

Deloitte. (2021). AI in audit: How artificial intelligence is reshaping the audit process. Deloitte Insights. Retrieved from https://www2.deloitte.com/insights/us/en/industry/financial-services/artificial-intelligence-in-audit.html

Issa, I., Sun, J., & Vasarhelyi, K. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and audit tasks. Journal of Emerging Technologies in Accounting, 13(2), 1–20.

Knechel, W. R., & van Staden, C. J. (2017). Auditing and risk assessment: Contemporary developments and challenges. Accounting & Finance, 57(1), 293–321. https://doi.org/10.1111/acfi.12215

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-51871

Kokina, M., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.

Li, F., Li, X., & Zhang, Y. (2020). AI-enabled audit testing: Enhancing accuracy and efficiency. Journal of Accounting Research, 58(4), 877–908. https://doi.org/10.1111/1475-679X.12298

Loughran, M., & Ritter, D. (2022). Natural language processing in auditing: Applications and challenges. Accounting Review, 97(2), 45–70. https://doi.org/10.2308/accr-52215

Loughran, T., & Ritter, J. (2022). Natural language processing for auditing: Using NLP to analyze audit documents. Auditing: A Journal of Practice & Theory, 41(3), 127–146.

Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-52112

Moffitt, K., Vasarhelyi, M., & Kogan, A. (2020). Regulating artificial intelligence in auditing: Current status and future directions. Accounting Horizons, 34(2), 69–87. https://doi.org/10.2308/acch-52418

Pizzini, M., Lin, S., & Lin, Z. (2020). Machine learning and audit quality: An integrative review. Auditing: A Journal of Practice & Theory, 39(3), 125–147. https://doi.org/10.2308/ajpt-19-019

Tschang, F., Beaudoin, C., & Guo, J. (2019). Applying natural language processing in auditing compliance: A practical framework. International Journal of Accounting Information Systems, 34, 100422. https://doi.org/10.1016/j.accinf.2019.100422

Turel, O., & Serenko, A. (2020). Ethics and accountability in AI auditing: Challenges and solutions. Journal of Business Ethics, 163(4), 735–749. https://doi.org/10.1007/s10551-018-4004-9

Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in auditing: Opportunities and challenges. Accounting Horizons, 29(2), 423–429. https://doi.org/10.2308/acch-51068

Wagner, A., Bredin, S., & Vasarhelyi, M. (2019). Data privacy challenges in AI-powered auditing. Journal of Information Systems, 33(3), 89–107. https://doi.org/10.2308/isys-52418

Wang, H., & Cuthbertson, R. (2015). Challenges of data quality in AI applications for auditing. International Journal of Accounting Information Systems, 18, 1–12.

Zhou, H., & Lee, T. A. (2018). Real-time fraud detection in auditing using AI techniques. Journal of Forensic & Investigative Accounting, 10(1), 151–167.

Downloads

Published

18-06-2025

How to Cite

[1]
W. P. Suyono, E. S. Puspa, S. Anugrah, and R. Firnanda, “Artificial Intelligence in Auditing: A Systematic Review of Tools, Applications, and Challenges ”, RIGGS, vol. 4, no. 2, pp. 3393–3401, Jun. 2025.

Issue

Section

Articles