ARTIFICIAL INTELLIGENCE AND AUDIT QUALITY: AN EMPIRICAL LITERATURE REVIEW FROM SCOPUS DATABASE

Juli Riyanto Tri Wijaya, Irwan Prasetyo, Dien Noviany Rahmatika, Dewi Indriasih

Abstract

This study aims to explore the development and influence of Artificial Intelligence (AI) on audit quality through a comprehensive empirical literature review. Utilizing a qualitative research approach, the study systematically analyzes 325 peer-reviewed articles published in Scopus-indexed journals between 2010 and 2025. The selection was based on the keyword “Artificial Intelligence on Audit Quality†and focused exclusively on finalized, English-language scientific journal articles. The findings reveal a significant rise in scholarly interest in AI applications in auditing, particularly over the past five years, with AI being applied to enhance fraud detection, real-time data analysis, and risk assessment. However, the results also highlight substantial gaps, including limited auditor readiness, ethical and regulatory concerns, and uneven research distribution across regions and institutions. The conclusions emphasize that while AI holds transformative potential for auditing, its optimal impact requires integration with human expertise, ethical frameworks, and regulatory oversight. The study’s implications suggest that future research should prioritize explainable AI, interdisciplinary collaboration, and broader global participation to ensure equitable and effective adoption. Overall, this review provides critical insights for researchers, practitioners, and policymakers on aligning AI innovations with the strategic and ethical demands of modern auditing to improve audit quality and public trust in financial reporting

Keywords

Artificial Intelligence, Audit Quality, Digital Auditing, Machine Learning, Ethical Governance

Full Text:

PDF

References

Abu Huson, Y., Sierra García, L., García Benau, M. A., & Mohammad Aljawarneh, N. (2025). Cloud-based artificial intelligence and audit report: the mediating role of the auditor. VINE Journal of Information and Knowledge Management Systems. https://doi.org/10.1108/VJIKMS-03-2024-0089

Abu Huson, Y., Sierra-García, L., & Garcia-Benau, M. A. (2024). A bibliometric review of information technology, artificial intelligence, and blockchain on auditing. Total Quality Management and Business Excellence, 35(1–2), 91–113. https://doi.org/10.1080/14783363.2023.2256260

Alareeni, B., & Hamdan, A. (2022). The Impact of Artificial Intelligence on Accounting and Auditing in Light of the COVID-19 Pandemic. In Accounting, Finance, Sustainability, Governance and Fraud (pp. 3–7). Springer. https://doi.org/10.1007/978-981-19-1036-4_1

Alastal, A. Y. M., Farhan, J. A., & Allaymoun, M. H. (2024). Auditors’ Perceptions in Gulf Countries Towards Using Artificial Intelligence in Audit Process. In Studies in Systems, Decision and Control (Vol. 487, pp. 867–878). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35828-9_73

Albawwat, I., & Frijat, Y. A. (2021). An analysis of auditors’ perceptions towards artificial intelligence and its contribution to audit quality. Accounting, 7(4), 755–762. https://doi.org/10.5267/j.ac.2021.2.009

Alhazmi, A. H. J., Islam, S. M. N., & Prokofieva, M. (2025). The Impact of Artificial Intelligence Adoption on the Quality of Financial Reports on the Saudi Stock Exchange. International Journal of Financial Studies, 13(1). https://doi.org/10.3390/ijfs13010021

Ananda, R. F., Rahmadhani, S. N., & Harun, M. S. (2024). Artificial intelligence based on audit quality towards sustainable business. In Technopreneurship in Small Businesses for Sustainability (pp. 34–49). IGI Global. https://doi.org/10.4018/979-8-3693-3530-7.ch003

Ashraf, M. (2025). Does automation improve financial reporting? Evidence from internal controls. Review of Accounting Studies, 30(1), 436–479. https://doi.org/10.1007/s11142-024-09822-y

Aslan, L. (2021). The evolving competencies of the public auditor and the future of public sector auditing. In Contemporary Studies in Economic and Financial Analysis (Vol. 105, pp. 113–129). Emerald Group Holdings Ltd. https://doi.org/10.1108/S1569-375920200000105008

Benhayoun, I., Bougrine, S., & Sassioui, A. (2025). Readiness for artificial intelligence adoption by auditors in emerging countries – a PLS-SEM analysis of Moroccan firms. Journal of Financial Reporting and Accounting. https://doi.org/10.1108/JFRA-07-2024-0448

Berghout, E., & Fijneman, R. (2023). Auditing Complexity. In Progress in IS: Vol. Part F2545 (pp. 9–14). Springer Medizin. https://doi.org/10.1007/978-3-031-11089-4_2

Chen, S., & Yang, J. (2024). Intelligent manufacturing, auditor selection and audit quality. Management Decision. https://doi.org/10.1108/MD-09-2023-1518

Chen, S., & Yang, J. (2025). Intelligent manufacturing, auditor selection and audit quality. Management Decision, 63(3), 964–997. https://doi.org/10.1108/MD-09-2023-1518

Choi, S. U., Lee, K. C., & Na, H. J. (2022). Exploring the deep neural network model’s potential to estimate abnormal audit fees. Management Decision, 60(12), 3304–3323. https://doi.org/10.1108/MD-07-2021-0954

Crawford, J., & Nilsson, F. (2023). Integrating ESG Risks into Control and Reporting: Evidence from Practice in Sweden. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 255–277). Springer Nature. https://doi.org/10.1007/978-981-19-4460-4_12

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

Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x

Goryunova, T. I., Goryunova, V. V, & Kukhtevich, I. I. (2020). Modeling of Complexly Structured Reporting Forms and Requests in the Tasks of Automated Provision of Public Services. Proceedings - 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2020, 318–322. https://doi.org/10.1109/SUMMA50634.2020.9280791

Hamdan, S. A. R., & Al Habashneh, A. K. (2024). The Advantages and Difficulties of Using AI and BT in the Auditing Procedures: A Literature Review. In Studies in Systems, Decision and Control (Vol. 503, pp. 111–126). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43490-7_9

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

Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2023). Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-022-00436-4

Huang, Z., Liu, Q.-L., & Hu, J.-B. (2011). Web-service oriented computer audit system based on agents. International Conference on Management and Service Science, MASS 2011. https://doi.org/10.1109/ICMSS.2011.5999077

Khan, F., Ullah Jan, S., & Zia-ul-haq, H. M. (2024). Artificial intelligence adoption, audit quality and integrated financial reporting in GCC markets. Asian Review of Accounting. https://doi.org/10.1108/ARA-03-2024-0085

Kwok, S., Omran, M., & Yu, P. (2024). Audit digitalization, do-calculus, and professional judgment: A practical evidence from China. In Harnessing Technology for Knowledge Transfer in Accountancy, Auditing, and Finance (pp. 1–25). IGI Global. https://doi.org/10.4018/9798369313312.ch001

Lam, K., Lange, B., Blili-Hamelin, B., Davidovic, J., Brown, S., & Hasan, A. (2024). A Framework for Assurance Audits of Algorithmic Systems. 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024, 1078–1092. https://doi.org/10.1145/3630106.3658957

Li, Y., & Goel, S. (2025). Artificial intelligence auditability and auditor readiness for auditing artificial intelligence systems. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100739

Malthouse, E. C., Maslowska, E., Strycharz, J., Block, M., & Araujo, T. (2024). Data Quality Measures for Computational Research: Ensuring Informed Decisions with Emerging Data Sources. Journal of Advertising, 53(5), 644–660. https://doi.org/10.1080/00913367.2024.2403609

Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150. https://doi.org/10.1016/j.techfore.2019.119751

Manuel, A., & Arumugam, S. K. (2024). AI in IA: Impact of Artificial Intelligence in Internal Audit: A Qualitative Study. In Contributions to Finance and Accounting: Vol. Part F3769 (pp. 451–463). Springer Nature. https://doi.org/10.1007/978-3-031-67547-8_38

Melnychenko, O. (2019). APPLICATION OF ARTIFICIAL INTELLIGENCE IN CONTROL SYSTEMS OF ECONOMIC ACTIVITY. Virtual Economics, 2(3), 30–40. https://doi.org/10.34021/ve.2019.02.03(3)

Mohaidin, N. J., Aman, A., Ilias, A., Keliwon, K. B., & Hassan, H. (2024). eXtensible Business Reporting Language Data Assurance Challenges and Strategic Approaches: A Study in the Malaysian Business Reporting System Context. Journal of Information Technology Management, 16(3), 173–191. https://doi.org/10.22059/jitm.2024.98619

Mohammed, A. M., & Wahha, A. (2024). THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND E-ACCOUNTING PROGRAMS: IMPACT ON THE QUALITY OF FINANCIAL REPORTS IN IRAQI BANKS. Financial and Credit Activity: Problems of Theory and Practice, 6(59), 180–193. https://doi.org/10.55643/fcaptp.6.59.2024.4522

Moloi, T., & George, B. (Eds.). (2024). International Conference of Accounting and Business, iCAB 2023. In Springer Proceedings in Business and Economics. Springer Nature. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185789545&partnerID=40&md5=98926fdd03540b050bcfa185088396f7

Muter, K. J., Al Dulaimi, J. A. A. B., & Younis, N. N. (2024). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE TONE OF ACCOUNTING DISCLOSURE IN FINANCIAL REPORTS AND ITS REFLECTION ON AUDIT QUALITY. International Journal of EBusiness and EGovernment Studies, 16(3), 43–62. https://doi.org/10.34109/ijebeg.2024160303

Nelson, K. M., Kogan, A., Srivastava, R. P., Vasarhelyi, M. A., & Lu, H. (2000). Virtual auditing agents: The EDGAR Agent challenge. Decision Support Systems, 28(3), 241–253. https://doi.org/10.1016/S0167-9236(99)00088-3

Noordin, N. A., Hussainey, K., & Hayek, A. F. (2022). The Use of Artificial Intelligence and Audit Quality: An Analysis from the Perspectives of External Auditors in the UAE. Journal of Risk and Financial Management, 15(8). https://doi.org/10.3390/jrfm15080339

Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors’ perception on the impact of artificial intelligence on professional skepticism and judgment in oman. Universal Journal of Accounting and Finance, 9(5), 1184–1190. https://doi.org/10.13189/ujaf.2021.090527

Rahman, M. J., Zhu, H., & Yue, L. (2024). Does the adoption of artificial intelligence by audit firms and their clients affect audit quality and efficiency? Evidence from China. Managerial Auditing Journal, 39(6), 668–699. https://doi.org/10.1108/MAJ-03-2023-3846

Rahman, M. J., & Ziru, A. (2023). Clients’ digitalization, audit firms’ digital expertise, and audit quality: evidence from China. International Journal of Accounting and Information Management, 31(2), 221–246. https://doi.org/10.1108/IJAIM-08-2022-0170

Ren, Q., & Chen, L. (2011). Nonaudit services and financial restatements: Evidence from Chinese listed companies. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 435–438. https://doi.org/10.1109/AIMSEC.2011.6010357

Sachan, S., Almaghrabi, F., Yang, J.-B., & Xu, D.-L. (2024). Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm. International Review of Financial Analysis, 93. https://doi.org/10.1016/j.irfa.2024.103149

Sánchez-Medina, A. J., Blázquez-Santana, F., & Alonso, J. B. (2019). Do Auditors Reflect the True Image of the Company Contrary to the Clients’ Interests? An Artificial Intelligence Approach. Journal of Business Ethics, 155(2), 529–545. https://doi.org/10.1007/s10551-017-3496-4

Seethamraju, R., & Hecimovic, A. (2023). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780–800. https://doi.org/10.1177/03128962221108440

Shapovalova, A., Kuzmenko, O., Polishchuk, O., Larikova, T., & Myronchuk, Z. (2023). MODERNIZATION OF THE NATIONAL ACCOUNTING AND AUDITING SYSTEM USING DIGITAL TRANSFORMATION TOOLS. Financial and Credit Activity: Problems of Theory and Practice, 4(51), 33–52. https://doi.org/10.55643/fcaptp.4.51.2023.4102

Shaqqour, O. F., Harb, A. S. M., Ballout, O. M. K., & Jaber, R. J. (2023). Digital Audit During Covid-19 in Jordanian Audit Firms a Study of the Reality and Outlook the Future. In Studies in Systems, Decision and Control (Vol. 216, pp. 263–272). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-10212-7_23

Singh, A. K., Kiriti, M. K., Singh, H., & Shrivastava, A. (2025). Education AI: exploring the impact of artificial intelligence on education in the digital age. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-025-02755-y

Sinha, A. N., Srivastava, V., & Sinha, K. (2025). Quality assurance in marketing data: Ensuring accuracy and reliability. In Data Engineering for Data-Driven Marketing (pp. 121–142). Emerald Publishing. https://doi.org/10.1108/978-1-83662-326-720251018

Sun, T., & Vasarhelyi, M. A. (2018). Embracing textual data analytics in auditing with deep learning. International Journal of Digital Accounting Research, 18, 49–67. https://doi.org/10.4192/1577-8517-v18_3

Tao, L. (2011). Notice of Retraction: On how to improve company’s internal audit. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 1258–1260. https://doi.org/10.1109/AIMSEC.2011.6010588

Tarnate, K. J. M., Devaraj, M., & De Goma, J. C. (2020). Overcoming the vanishing gradient problem of recurrent neural networks in the ISO 9001 quality management audit reports classification. International Journal of Scientific and Technology Research, 9(3), 6683–6686. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082858448&partnerID=40&md5=0fd1d3400917cf36421674b09989a444

XU, P., PENG, Z., & ZHANG, C. (2024). The self-governance model and implementation path of UGC participants: A multi-case and grounded theory study. Journal of Industrial Engineering and Engineering Management, 38(2), 255–270. https://doi.org/10.13587/j.cnki.jieem.2024.02.019

DOI: https://doi.org/10.34152/fe.20.1.61-76

Refbacks

  • There are currently no refbacks.