¹Dr. Samson Oyewole, OLABISI, ²Oluwabunmi Akindele, OLAWAYE

^1,2Accounting Department, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria.

DOI : https://doi.org/10.58806/ijsshmr.2025.v4i10n09

Google Scholar Download Pdf
ABSTRACT

Audit quality in African countries faces numerous challenges that undermine the effectiveness and reliability of financial reporting. A prominent issue is the shortage of qualified audit professionals, which limits the capacity to conduct timely and thorough audits. This deficiency hinders the establishment of robust audit systems and adversely impacts the overall quality of financial reporting across the continent. This study specifically examined AI and audit quality in multinational audit firms in Nigeria, with a focus on the role of emerging technologies in enhancing audit quality. Data were collected through structured questionnaires administered to respondents from multinational audit firms in Nigeria.The population of this study is 500 audit professionals (partners, managers, and senior auditors) in multinational audit firms in Nigeria, The sample size for this study was determined using Yamane’s (1967) formula 255 respondents. Using simple regression analysis, the study assessed the influence of various technological tools on audit quality. The findings revealed that Machine Learning Algorithms, Data Mining, Robotic Process Automation, Predictive Analysis, and Image Recognition each have a statistically significant positive effect on audit quality. These results suggest that integrating ethical standards with advanced technological innovations can significantly improve audit quality and strengthen financial reporting in Nigeria. The study recommends greater investment in digital transformation and ethical training to bridge existing gaps in audit capacity and performance

KEYWORDS:

Machine Learning Algorithms, Data Mining, Robotic Process Automation, Predictive Analysis, Image Recognition, Audit Quality

REFERENCES

1) Abiola,J., & Mohammed,S.K. (2024). Deep learning anomaly detection and accrual quality: Evidence from Big 4 engagements on the Nigeria Exchange Group. International Journal of Accounting Research, 16(1), 64–88. https://doi.org/10.1142/S1094406024500059

2) Abiola, O. M. (2022). Audit Quality and Earnings Management Practices among Manufacturing Companies in Nigeria (Master's thesis, Kwara State University (Nigeria)).

3) Adeyemi,S.B., & Idowu,O.A. (2021). Artificial intelligence deployment and audit effectiveness in Big 4 Nigerian practices. Journal of Emerging Technologies in Accounting, 18(3), 215–238. https://doi.org/10.2308/JETA 2021 0116

4) Adeyemi,S.B., & Idowu,O.A. (2021). Artificial intelligence deployment and audit effectiveness in Big 4 Nigerian practices (Doctoral dissertation, University of Lagos). University of Lagos Institutional Repository.

5) Akinadewo, I. S. (2021). Artificial intelligence and accountants' approach to accounting functions. Covenant University Journal of Politics & International Affairs (Special Edition).

6) Akinadewo, I. S., Dagunduro, M. E., & Osatuyi, Y. A. (2023). In what way does blockchain technology tie with the efficacy of accounting practice in Nigerian accounting firms?. Journal of Business and Management Review, 12(1), 96-117.

7) Almufadda, G., & Almezeini, N. A. (2022). Artificial intelligence applications in the auditing profession: a literature review. Journal of Emerging Technologies in Accounting, 19(2), 29-42.

8) Al-Sayyed, S. M., Al-Aroud, S. F., & Zayed, L. M. (2021). The effect of artificial intelligence estimates and auditor reliance on artificial intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3422591

9) Al-Shaer, H., & Zaman, M. (2018). Credibility of sustainability reports: The contribution of audit committees. Business Strategy and the Environment, 27(7), 973–986.

10) Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(1), 1–` 27. https://doi.org/10.2308/ajpt-51684

11) Blair, M. M., & Stout, L. A. (2017). A team production theory of corporate law. Virginia Law Review, 85(2), 247. https://doi.org/10.2307/1073662

12) Chen, T., Dong, X., & Yu, Y. (2018). Audit Market Competition and Audit Quality: Evidence from the Entry of Big 4 into City-Level Audit Markets in the U.S. Audit market competition and audit quality. Abingdon: Routledge.

13) Cho, S., Vasarhelyi, M. A., Sun, T. S., & Zhang, C. A. (2020). Learning from machine learning in accounting and assurance. Journal of Emerging Technologies in Accounting, 17(1), 1–10. https://doi.org/10.2308/jeta-10718

14) Commerford, B. P., Dennis, S. A., Joe, J. R., & Wang, J. (2019). Man versus machine: ComplexCosts and Ownership Structure. Journal of Financial Economics 3, 3 (1), 305-360.

15) Deloitte Nigeria. (2020). Argus AI confirmation tool: One year impact review. Deloitte Centre for Audit Innovation.

16) Eze,C., Uyi,O., & Adebayo,T. (2024). Perceived AI capability and auditors’ professional scepticism: Evidence from multinational firms in Nigeria. Afro Asian Journal of Accounting and Finance, 10(1), 1–23. https://doi.org/10.1108/AAJAF 10 2023 0182

17) Gichuru,J., & Kimani,E.K. (2023). Machine learning tools and accrual quality: Evidence from Kenyanlisted companies. African Journal of Accounting, Auditing and Finance, 12(2), 157–178. https://doi.org/10.1504/AJAAF.2023.129876

https://doi.org/10.1002/bse.2046

18) Hwang, G. J., & Chang, C. Y. (2021). A review of opportunities and challenges of chatbots in

19) Ibrahim,A.S., & Danjuma,I. (2021). Cloud based AI analytics and IFRS compliance: Panel evidence from multinational audit engagements in Nigeria. Journal of International Accounting, Auditing and Taxation, 43, Article 100426. https://doi.org/10.1016/j.intaccaudtax.2021.100426

20) ICAN & World Bank. (2022, September). AI pilot implementation in audit engagements: A mixed methods analysis of Nigerian Big 4 firms (Paper presentation). 52nd ICAN International Accounting Conference, Abuja, Nigeria.

21) International Auditing and Assurance Standards Board. (2023). The auditor’s responsibilities relating to fraud in an audit of financial statements—Technology spotlight.

22) Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511

23) Jensen, M. C., & Meckling, W. H. (1976). Theory of The Firm: Managerial Behavior, Agency

24) Kemuma Ondeyo, R. (2023). Impact of artificial intelligence (AI) on auditing intelligence (Doctoral dissertation, Dublin Business School).

25) KPMG Africa Analytics Hub. (2019). Continuous auditing platforms in sub Saharan Africa: Utilisation benchmarks.

26) Moyo,P., & Sibanda,M. (2022). Big data analytics adoption and audit quality in Zimbabwean banking audits. Journal of Accounting in Emerging Economies, 12(4), 605–630. https://doi.org/10.1108/JAEE 03 2021 0075

27) Nkwede, M. C., & Aniuga, C. (2023). Artificial intelligence: Challenges and opportunities for the accounting profession in Nigeria. African Journal of Politics and Administrative Studies, 16(1), 1-17.

28) Obi,K., & Ayanwale,F.R. (2023). AI enabled continuous auditing and going concern reporting accuracy in Nigerian financial services audits. Accounting Perspectives, 22(4), 541–567. https://doi.org/10.1111/1911 3838.12494

29) Okafor,C.O., & Olanrewaju,B. (2023). Do AI certifications of engagement partners matter? Early evidence from Nigeria. SSRN Working Paper No. 4567812.

30) Okoye,P., & Abdullahi,S. (2024). Artificial intelligence–driven journal entry testing and audit quality: Evidence from Nigeria. ICAN Journal of Accounting & Finance, 13(2), 45–66. https://doi.org/10.4314/icanjaf.v13i2.3

31) Olu Asaolu,A.O., & Hassan,M.T. (2023). Predictive analytics and going concern reporting accuracy: Diff in diff evidence from Nigerian listed firms. Journal of Financial Reporting, 8(4), 327–352. https://doi.org/10.2308/JFR 23 015

32) Onwughai, E. (2022). Probing the effect of artificial intelligence and machine learning on accounting functions: Evidence from selected companies. International Journal of Research and Innovation in Applied Science (IJRIAS), 7(2).

33) Owolabi,A.A., & Nduka,E. (2025). AI intensity and misstatement risk: Evidence from a natural experiment in Big 4 Nigeria. SSRN Working Paper No. 4789327. Retrieved April 20,2025, from https://ssrn.com/abstract=4789327

34) Owonifari, V. O., Igbekoyi, O. E., Awotomilusi, N. S., & Dagunduro, M. E. (2023). Evaluation of artificial intelligence and efficacy of audit practice in Nigeria. Asian Journal of Economics, Business and Accounting, 23(16), 1-14.

35) Public Company Accounting Oversight Board. (2021). Observations on the use of emerging technologies in audits (Staff Spotlight No. 2021 01).

36) PwC Nigeria. (2023). Enhancing audit inspections with natural language processing: A case study on contract review bots. PwC Thought Lab.

37) Tang, F., & Yang, L. (2023). Auditors’ responses to real earnings management: The effects of timing and potential consequences on auditor decision-making. Behavioral Research in Accounting, 35(1), 67-80. technologies on audit evidence. Accounting, 281–288. https://doi.org/10.5267/j.ac.2020.12.003

38) Udeh,F., & Salihu, I. A. (2022). Robotic process automation, audit fees and quality differentials in Big 4 Nigerian practices. In Proceedings of the 18th AAAI Accounting Symposium (pp.117–128). AAAI Press.

39) Vardia, S., Soni, R., & Saluja, R. (2021). An Impact of Digitalization on Auditing Profession (with references to Indian Chartered Accountants). Pacific Business Review International, 14(3).

VOlUME 04 ISSUE 10 OCTOBER 2025