THE IMPACT OF ARTIFICIAL INTELLIGENCE ON AUDIT QUALITY AND AUDITOR JUDGEMENT: A MULTI COUNTRY ANALYSIS

Vince Ariany

doi:10.62207/qpy82263

Authors

Vince Ariany Politeknik Ganesha Medan, North Sumatera, Indonesia Author

DOI:

https://doi.org/10.62207/qpy82263

Keywords:

Artificial Intelligence, audit quality, professional skepticism, AI reliability, AI transparency, cross-jurisdiction

Abstract

The rapid integration of Artificial Intelligence (AI) into audit practice has revolutionized the professional process by improving accuracy and efficiency, especially in fraud detection, business continuity analysis, and risk assessment. However, there is no global consensus on how auditors assess and respond to AI outputs, leading to potential variability in audit quality. Auditors’ perceptions of the reliability and transparency of AI systems are key factors that shape their level of trust and maintain professional skepticism, which are significantly influenced by the cultural and regulatory contexts in different jurisdictions. This study aims to examine how auditors’ perceptions of the reliability and transparency of AI affect their level of reliance and skepticism in a cross-border audit context. Using a narrative literature review approach, this study explores scientific literature from leading databases such as Scopus, Web of Science, ScienceDirect, and Emerald Insight over the period 2013 to 2025. The data are analyzed thematically to identify key patterns and build a conceptual framework that integrates the Technology Acceptance Model, Trust in Automation Framework, and Audit Judgment and Decision-Making Framework. The results of the analysis show that positive perceptions of the reliability and transparency of AI significantly increase auditors’ propensity to rely on the technology. Factors such as explainable AI, user control, provider reputation, and organizational culture contribute to the formation of auditor trust. However, a high level of reliance without adequate professional skepticism can reduce audit quality. In addition, contextual factors such as differences in national culture, legal systems (rules-based versus principles-based), and regulatory frameworks also influence auditor responses to the use of AI. These findings emphasize the importance of balancing the use of AI with the application of professional skepticism to maintain audit integrity and quality. Practical implications include the need for clear regulatory guidelines, transparent AI system design, and comprehensive auditor training to optimize the use of technology without neglecting the principles of professionalism.

References

Almufadda, G. and Almezeini, N. (2021). Artificial intelligence applications in the auditing profession: a literature review. Journal of Emerging Technologies in Accounting, 19(2), 29-42. https://doi.org/10.2308/jeta-2020-083

Alvarado, R. (2022). What kind of trust does ai deserve, if any?. Ai and Ethics, 3(4), 1169-1183. https://doi.org/10.1007/s43681-022-00224-x

Ananda, R., Rahmadhani, S., Pane, A., & Wiratama, N. (2024). Assessment audit: how artificial intelligence affected audit quality of sustainability report based on auditors perspective. Information Management and Business Review, 16(3(I)S), 152-158. https://doi.org/10.22610/imbr.v16i3(i)s.4049

Ayling, J. and Chapman, A. (2021). Putting ai ethics to work: are the tools fit for purpose?. Ai and Ethics, 2(3), 405-429. https://doi.org/10.1007/s43681-021-00084-x

Bach, T., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2022). A systematic literature review of user trust in ai-enabled systems: an hci perspective. International Journal of Human-Computer Interaction, 40(5), 1251-1266. https://doi.org/10.1080/10447318.2022.2138826

Balasubramaniam, N., Kauppinen, M., Hiekkanen, K., & Kujala, S. (2022). Transparency and explainability of ai systems: ethical guidelines in practice., 3-18. https://doi.org/10.1007/978-3-030-98464-9_1

Bik, O. and Hooghiemstra, R. (2016). The effect of national culture on auditor-in-charge involvement. Auditing a Journal of Practice & Theory, 36(1), 1-19. https://doi.org/10.2308/ajpt-51487

Bik, O. and Hooghiemstra, R. (2018). Cultural differences in auditors' compliance with audit firm policy on fraud risk assessment procedures. Auditing a Journal of Practice & Theory, 37(4), 25-48. https://doi.org/10.2308/ajpt-51998

Bodemer, O. (2023). Artificial intelligence in governance: a comprehensive analysis of ai integration and policy development in the german government.. https://doi.org/10.36227/techrxiv.24639588

Brown, S., Davidovic, J., & Hasan, A. (2021). The algorithm audit: scoring the algorithms that score us. Big Data & Society, 8(1). https://doi.org/10.1177/2053951720983865

Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080

Commerford, B., Dennis, S., Joe, J., & Ulla, J. (2021). Man versus machine: complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research, 60(1), 171-201. https://doi.org/10.1111/1475-679x.12407

Commerford, B., Eilifsen, A., Hatfield, R., Holmstrom, K., & Kinserdal, F. (2024). Control issues: how providing input affects auditors' reliance on artificial intelligence. Contemporary Accounting Research, 41(4), 2134-2162. https://doi.org/10.1111/1911-3846.12974

DataSnipper. (2024). AI: Improving trust in audit and finance. Retrieved from https://www.datasnipper.com/resources/ai-improving-trust-audit-finance

Datricks. (2024). AI and auditing: Addressing the PCAOB’s audit inspection priorities. Retrieved from https://datricks.com/blog/ai-and-auditing-addressing-the-pcaobs-audit-inspection-priorities

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

Fujimori, R., Liu, K., Soeno, S., Naraba, H., Ogura, K., Hara, K., … & Goto, T. (2022). Acceptance, barriers, and facilitators to implementing artificial intelligence–based decision support systems in emergency departments: quantitative and qualitative evaluation. Jmir Formative Research, 6(6), e36501. https://doi.org/10.2196/36501

Gitnux. (2024). AI in the accounting industry statistics. Retrieved from https://gitnux.org/ai-in-the-accounting-industry-statistics

Hu, K., Chen, F., Hsu, M., & Tzeng, G. (2020). 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

Jermutus, E., Kneale, D., Thomas, J., & Michie, S. (2022). Influences on user trust in healthcare artificial intelligence: a systematic review. Wellcome Open Research, 7, 65. https://doi.org/10.12688/wellcomeopenres.17550.1

Kend, M. and Nguyen, L. (2020). Big data analytics and other emerging technologies: the impact on the australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282. https://doi.org/10.1111/auar.12305

Kend, M. and Nguyen, L. (2022). The emergence of audit data analytics in existing audit spaces: findings from three technologically advanced audit and assurance service markets. Qualitative Research in Accounting & Management, 19(5), 540-563. https://doi.org/10.1108/qram-01-2021-0005

Kim, C., Gadgil, S., DeGrave, A., Cai, Z., Daneshjou, R., & Lee, S. (2023). Fostering transparent medical image ai via an image-text foundation model grounded in medical literature.. https://doi.org/10.1101/2023.06.07.23291119

Kim, S., Kim, T., Pae, S., & Kim, S. (2018). Audit fees via an indirect payment channel and professional skepticism. Managerial Auditing Journal, 33(5), 517-534. https://doi.org/10.1108/maj-12-2016-1490

KPMG. (2024). AI in financial reporting survey 2024. Retrieved from https://kpmg.com/us/en/media/news/ai-in-financial-reporting-kpmg-2024.html

Kusumawati, A. and Syamsuddin, S. (2018). The effect of auditor quality to professional skepticsm and its relationship to audit quality. International Journal of Law and Management, 60(4), 998-1008. https://doi.org/10.1108/ijlma-03-2017-0062

Mehrotra, S., Jorge, C., Jonker, C., & Tielman, M. (2023). Building appropriate trust in ai: the significance of integrity-centered explanations.. https://doi.org/10.3233/faia230121

Minkkinen, M., Laine, J., & Mäntymäki, M. (2022). Continuous auditing of artificial intelligence: a conceptualization and assessment of tools and frameworks. Digital Society, 1(3). https://doi.org/10.1007/s44206-022-00022-2

Mpofu, F. (2023). The application of artificial intelligence in external auditing and its implications on audit quality? a review of the ongoing debates. International Journal of Research in Business and Social Science (2147-4478), 12(9), 496-512. https://doi.org/10.20525/ijrbs.v12i9.2737

Noordin, N., Hussainey, K., & Hayek, A. (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), 339. https://doi.org/10.3390/jrfm15080339

Nugrahaeni, S., Samin, S., & Nopiyanti, A. (2019). The effect of auditor professional skepticism, competence, independence and audit complexity on audit quality. Equity, 21(2), 181. https://doi.org/10.34209/.v21i2.643

Rahman, M., Zhu, H., & Li, Y. (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

Rhea, A., Markey, K., D’Arinzo, L., Schellmann, H., Sloane, M., Squires, P., … & Stoyanovich, J. (2022). An external stability audit framework to test the validity of personality prediction in ai hiring. Data Mining and Knowledge Discovery, 36(6), 2153-2193. https://doi.org/10.1007/s10618-022-00861-0

Ríkharðsson, P., Þórisson, K., 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

Rodrigues, L., Pereira, J., Silva, A., & Ribeiro, H. (2023). The impact of artificial intelligence on audit profession. Journal of Information Systems Engineering & Management, 8(1), 19002. https://doi.org/10.55267/iadt.07.12743

Seethamraju, R. and Hecimovic, A. (2022). Adoption of artificial intelligence in auditing: an exploratory study. Australian Journal of Management, 48(4), 780-800. https://doi.org/10.1177/03128962221108440

Tang, Y. and Cai, J. (2023). Impact and prediction of ai diagnostic report interpretation type on patient trust. Frontiers in Computing and Intelligent Systems, 3(3), 59-65. https://doi.org/10.54097/fcis.v3i3.8567

The Australian. (2024, April 2). Most Australian companies now using AI for financial processes. Retrieved from https://www.theaustralian.com.au/business/financial-services/most-australian-companies-now-using-ai-for-financial-processes/news-story/6d589b08a1ab7373bc7964ac486a2b7e

Thielsch, M., Meeßen, S., & Hertel, G. (2018). Trust and distrust in information systems at the workplace. Peerj, 6, e5483. https://doi.org/10.7717/peerj.5483

Thottoli, M. (2024). Leveraging information communication technology (ict) and artificial intelligence (ai) to enhance auditing practices. Accounting Research Journal, 37(2), 134-150. https://doi.org/10.1108/arj-09-2023-0269

Wang, B., Asan, O., & Mansouri, M. (2023). What may impact trustworthiness of ai in digital healthcare: discussion from patients’ viewpoint. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 12(1), 5-10. https://doi.org/10.1177/2327857923121001