Critical Evaluation of the Use of Artificial Intelligence in Auditing: A Systematic Literature Review of Research Trends, Methodological Approaches, and Gaps
Keywords:
Artificial Intelligence, Audit, Machine Learning, Systematic Literature Review, Quality Audit, Research Gap
Abstract
This study aims to analyze the development, trends, methodological approaches, and research gaps related to the use of Artificial Intelligence (AI) in auditing through a systematic literature review. The study employs the Systematic Literature Review (SLR) method following PRISMA guidelines, using data retrieved from the Scopus database based on keywords related to AI and auditing. The selection process resulted in 28 articles, which were analyzed using descriptive and thematic approaches. The findings indicate that the use of AI in auditing has increased significantly since 2020, with a primary focus on fraud detection, risk assessment, predictive analytics, and enhancing audit quality. The dominant approach is quantitative, particularly those based on machine learning, complemented by qualitative, mixed-methods, and conceptual studies. In addition, several key challenges were identified, including ethical concerns, transparency issues, data quality, and methodological limitations. Furthermore, this study reveals gaps in the integration of technical, behavioral, and regulatory aspects within the existing literature. In conclusion, AI plays a strategic role in the transformation of modern auditing; however, its implementation still requires further strengthening in methodological, ethical, and practical dimensions.
References
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Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1). https://doi.org/10.1016/j.joitmc.2024.100218
Allozi, Y., Nawaiseh, A., Al-Mawali, H., Abbod, M., & Alshurideh, M. (2025). The impact of artificial intelligence on the quality of external auditing in Jordanian commercial banks: The mediating role of the quality of financial reports. International Journal of Data and Network Science, 9(3), 667–676. https://doi.org/10.5267/j.ijdns.2024.7.013
Destefanis, G., Yousefi, L., Shepperd, M., Tucker, A., Swift, S., Counsell, S., & Arzoky, M. (2026). An audit of machine learning experiments on software defect prediction. Empirical Software Engineering, 31(4). https://doi.org/10.1007/s10664-025-10797-w
Estep, C., Griffith, E. E., & MacKenzie, N. L. (2024). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing? Review of Accounting Studies, 29(3), 2798–2831. https://doi.org/10.1007/s11142-023-09771-y
Fang, Q. (2022). Research on the Audit Prediction Model of “special Bonds + PPP” Project based on Machine Learning. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4174473
Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48. https://doi.org/10.1016/j.accinf.2022.100598
Hedback, D. (2025). Identifying Going Concern Audit Opinions Using Supervised Machine Learning. Intelligent Systems in Accounting, Finance and Management, 32(4). https://doi.org/10.1002/isaf.70020
Hu, K. H., Chen, F. H., Hsu, M. F., & Tzeng, G. H. (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. Journal of Finance and Data Science, 8, 138–144. https://doi.org/10.1016/j.jfds.2022.05.002
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. In Journal of Emerging Technologies in Accounting (Vol. 13, Number 2, pp. 1–20). American Accounting Association. https://doi.org/10.2308/jeta-10511
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100734
Lehner, O. M., Ittonen, K., Silvola, H., Ström, E., & Wührleitner, A. (2022). Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking. Accounting, Auditing and Accountability Journal, 35(9), 109–135. https://doi.org/10.1108/AAAJ-09-2020-4934
Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial Intelligence in Auditing: A Conceptual Framework for Auditing Practices. In Administrative Sciences (Vol. 14, Number 10). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/admsci14100238
Mabelane, K., Mongwe, W. T., Mbuvha, R., & Marwala, T. (2023). An Analysis of Local Government Financial Statement Audit Outcomes in a Developing Economy Using Machine Learning. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010012
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Muñoz-Izquierdo, N., Segovia-Vargas, M. J., Camacho-Miñano, M. del M., & Pérez-Pérez, Y. (2022). Machine learning in corporate credit rating assessment using the expanded audit report. Machine Learning, 111(11), 4183–4215. https://doi.org/10.1007/s10994-022-06226-4
Musa, A. M. H., & Lefkir, H. (2024). The role of artificial intelligence in achieving auditing quality for small and medium enterprises in the Kingdom of Saudi Arabia. International Journal of Data and Network Science, 8(2), 835–844. https://doi.org/10.5267/j.ijdns.2023.12.021
Prokofieva, M. (2023). Integrating data analytics in teaching audit with machine learning and artificial intelligence. Education and Information Technologies, 28(6), 7317–7353. https://doi.org/10.1007/s10639-022-11474-x
Rikhardsson, P., Thórisson, K. R., Bergthorsson, G., & Batt, C. (2022). Artificial intelligence and auditing in small- and medium-sized firms: Expectations and applications. AI Magazine, 43(3), 323–336. https://doi.org/10.1002/aaai.12066
Schiff, D. S., Kelley, S., & Camacho Ibáñez, J. (2024). The emergence of artificial intelligence ethics auditing. Big Data and Society, 11(4). https://doi.org/10.1177/20539517241299732
Spinelli, D., Berta, P., & Santoro, A. (2025). Which tax audits should be increased? Evidence from Italy using a machine learning approach. International Tax and Public Finance. https://doi.org/10.1007/s10797-025-09925-5
Wang, J., & Yan, C. (2025). Using machine learning to identify audit opinion shopping. China Journal of Accounting Research, 18(3). https://doi.org/10.1016/j.cjar.2025.100436
Zhang, J. (2022). Dynamic Audit of Internet Finance Based on Machine Learning Algorithm. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/7072955
Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1). https://doi.org/10.1016/j.joitmc.2024.100218
Allozi, Y., Nawaiseh, A., Al-Mawali, H., Abbod, M., & Alshurideh, M. (2025). The impact of artificial intelligence on the quality of external auditing in Jordanian commercial banks: The mediating role of the quality of financial reports. International Journal of Data and Network Science, 9(3), 667–676. https://doi.org/10.5267/j.ijdns.2024.7.013
Destefanis, G., Yousefi, L., Shepperd, M., Tucker, A., Swift, S., Counsell, S., & Arzoky, M. (2026). An audit of machine learning experiments on software defect prediction. Empirical Software Engineering, 31(4). https://doi.org/10.1007/s10664-025-10797-w
Estep, C., Griffith, E. E., & MacKenzie, N. L. (2024). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing? Review of Accounting Studies, 29(3), 2798–2831. https://doi.org/10.1007/s11142-023-09771-y
Fang, Q. (2022). Research on the Audit Prediction Model of “special Bonds + PPP” Project based on Machine Learning. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4174473
Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48. https://doi.org/10.1016/j.accinf.2022.100598
Hedback, D. (2025). Identifying Going Concern Audit Opinions Using Supervised Machine Learning. Intelligent Systems in Accounting, Finance and Management, 32(4). https://doi.org/10.1002/isaf.70020
Hu, K. H., Chen, F. H., Hsu, M. F., & Tzeng, G. H. (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. Journal of Finance and Data Science, 8, 138–144. https://doi.org/10.1016/j.jfds.2022.05.002
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. In Journal of Emerging Technologies in Accounting (Vol. 13, Number 2, pp. 1–20). American Accounting Association. https://doi.org/10.2308/jeta-10511
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100734
Lehner, O. M., Ittonen, K., Silvola, H., Ström, E., & Wührleitner, A. (2022). Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking. Accounting, Auditing and Accountability Journal, 35(9), 109–135. https://doi.org/10.1108/AAAJ-09-2020-4934
Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial Intelligence in Auditing: A Conceptual Framework for Auditing Practices. In Administrative Sciences (Vol. 14, Number 10). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/admsci14100238
Mabelane, K., Mongwe, W. T., Mbuvha, R., & Marwala, T. (2023). An Analysis of Local Government Financial Statement Audit Outcomes in a Developing Economy Using Machine Learning. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010012
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Muñoz-Izquierdo, N., Segovia-Vargas, M. J., Camacho-Miñano, M. del M., & Pérez-Pérez, Y. (2022). Machine learning in corporate credit rating assessment using the expanded audit report. Machine Learning, 111(11), 4183–4215. https://doi.org/10.1007/s10994-022-06226-4
Musa, A. M. H., & Lefkir, H. (2024). The role of artificial intelligence in achieving auditing quality for small and medium enterprises in the Kingdom of Saudi Arabia. International Journal of Data and Network Science, 8(2), 835–844. https://doi.org/10.5267/j.ijdns.2023.12.021
Prokofieva, M. (2023). Integrating data analytics in teaching audit with machine learning and artificial intelligence. Education and Information Technologies, 28(6), 7317–7353. https://doi.org/10.1007/s10639-022-11474-x
Rikhardsson, P., Thórisson, K. R., Bergthorsson, G., & Batt, C. (2022). Artificial intelligence and auditing in small- and medium-sized firms: Expectations and applications. AI Magazine, 43(3), 323–336. https://doi.org/10.1002/aaai.12066
Schiff, D. S., Kelley, S., & Camacho Ibáñez, J. (2024). The emergence of artificial intelligence ethics auditing. Big Data and Society, 11(4). https://doi.org/10.1177/20539517241299732
Spinelli, D., Berta, P., & Santoro, A. (2025). Which tax audits should be increased? Evidence from Italy using a machine learning approach. International Tax and Public Finance. https://doi.org/10.1007/s10797-025-09925-5
Wang, J., & Yan, C. (2025). Using machine learning to identify audit opinion shopping. China Journal of Accounting Research, 18(3). https://doi.org/10.1016/j.cjar.2025.100436
Zhang, J. (2022). Dynamic Audit of Internet Finance Based on Machine Learning Algorithm. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/7072955
Published
2026-05-18
How to Cite
Ryketeng, M., Haliah, & Darmawati. (2026). Critical Evaluation of the Use of Artificial Intelligence in Auditing: A Systematic Literature Review of Research Trends, Methodological Approaches, and Gaps. International Journal of Education, Vocational and Social Science , 5(02), 2052-2064. https://doi.org/10.63922/ijevss.v5i02.3218
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
