📄 Sciences Methods and Technologies
International Journal (SciMeTech)

Volume 2 · Issue 2 · 2026
ISSN: 3085-5284
Artificial Intelligence and Audit Effectiveness in Global Accounting: A Big Four Analysis
Saad Saadouni, Souad Habbani
Pages 133–154 · LIREFIMO Laboratory, Faculty of Law, Economics and Social Sciences, University Sidi Mohammed Ben Abdellah, Fez-Morocco
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Abstract
The Big Four accounting firms (Deloitte, PwC, Ernst & Young, and KPMG) face unprecedented challenges in financial risk management, compliance, and fraud detection amid rapid technological transformation and regulatory evolution. This study examines the relationship between AI adoption, employee workload, and audit effectiveness across Big Four firms from 2020-2025, analyzing high-risk cases, compliance violations, and fraud detection patterns across four major industries. We analyzed 100 audit engagement records spanning 2020-2025, employing descriptive statistics, correlation analysis, trend analysis, and comparative performance evaluation. The dataset encompasses 12 key variables including audit engagements, risk cases, compliance violations, fraud detection, AI adoption, employee workload, and satisfaction metrics. Our findings reveal significant variation in high-risk case management across firms, with mean high-risk cases of 277.73 (SD=135.74). Healthcare sector accounted for the highest audit engagements (68,004), followed by Technology (80,069). AI adoption showed mixed correlation with audit effectiveness. Revenue impact averaged 272.54 $M per engagement, with employee workload averaging 60.25 hours. The study demonstrates that audit effectiveness is multifactorial, influenced by AI adoption, workload management, and industry-specific risk profiles. Strategic AI integration, combined with optimal workload distribution, significantly enhances fraud detection and compliance outcomes.
Keywords: Big Four, Audit effectiveness, Financial risk, AI adoption, Compliance violations, Fraud detection, KPMG, Deloitte, PwC, Ernst & Young

References

  1. ACFE. (2022). Report to the Nations: 2022 Global Study on Occupational Fraud and Abuse. Association of Certified Fraud Examiners. https://acfepublic.s3.us-west-2.amazonaws.com/2022+Report+to+the+Nations.pdf
  2. Albitar, K., Gerged, A. M., Kikhia, H., & Hussainey, K. (2020). Auditing in times of social distancing: The effect of COVID-19 on auditing quality. International Journal of Accounting & Information Management, 29(1), 169-178. https://doi.org/10.1108/IJAIM-08-2020-0128
  3. Alles, M. G., & Gray, G. L. (2021). "The time has come," the accountant said: Has COVID-19 "walked the talk" for cloud-based continuous audit? Journal of Emerging Technologies in Accounting, 18(1), 119-130. https://doi.org/10.2308/IETA-2020-068
  4. Austin, A. A., Carpenter, T. D., Christ, M. H., & Nielson, C. S. (2021). The data analytics journey: Interactions among auditors, managers, regulation, and technology. Contemporary Accounting Research, 38(3), 1888-1924. https://doi.org/10.1111/1911-3846.12680
  5. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
  6. Beattie, V., Brandt, R., & Fearnley, S. (1999). Perceptions of auditor independence: UK evidence. Journal of International Accounting, Auditing and Taxation, 8(1), 67-107. https://doi.org/10.1016/S1061-9518(99)00005-0
  7. Becker, C. L., DeFond, M. L., Jiambalvo, J., & Subramanyam, K. R. (1998). The effect of audit quality on earnings management. Contemporary Accounting Research, 15(1), 1-24. https://doi.org/10.1111/j.1911-3846.1998.tb00547.x
  8. Bloomberg. (2023). Big Four Accounting Firms Face Record Staff Turnover. Bloomberg Business. https://www.bloomberg.com/news
  9. Brazel, J. F., Agoglia, C. P., & Hatfield, R. C. (2009). Electronic versus face-to-face review: The effects of alternative forms of review on auditors' performance. The Accounting Review, 79(4), 949-966. https://doi.org/10.2308/accr.2004.79.4.949
  10. Cameran, M., Francis, J. R., Marra, A., & Pettinichio, A. (2015). Are there adverse consequences of mandatory auditor rotation? Evidence from the Italian experience. Auditing: A Journal of Practice & Theory, 34(1), 1-24. https://doi.org/10.2308/ajpt-50663
  11. Cao, M., Chychyla, R., & Stewart, T. (2015). Big Data analytics in financial statement audits. Accounting Horizons, 29(2), 423-429. https://doi.org/10.2308/acch-51068
  12. Carcello, J. V., & Nagy, A. L. (2004). Client size, auditor specialization and fraudulent financial reporting. Managerial Auditing Journal, 19(5), 651-668. https://doi.org/10.1108/02686900410537775
  13. Carcello, J. V., Hermanson, R. H., & McGrath, N. T. (1992). Audit quality attributes: The perceptions of audit partners, preparers, and financial statement users. Auditing: A Journal of Practice & Theory, 11(1), 1-15.
  14. Casterella, J. R., Francis, J. R., Lewis, B. L., & Walker, P. L. (2004). Auditor industry specialization, client bargaining power, and audit pricing. Auditing: A Journal of Practice & Theory, 23(1), 123-140. https://doi.org/10.2308/aud.2004.23.1.123
  15. Choi, J. H., Kim, C., Kim, J. B., & Zang, Y. (2010). Audit office size, audit quality, and audit pricing. Auditing: A Journal of Practice & Theory, 29(1), 73-97. https://doi.org/10.2308/aud.2010.29.1.73
  16. Cohen, J. R., Krishnamoorthy, G., & Wright, A. (2011). Corporate Governance and the Audit Process. Cambridge University Press. ISBN: 978-0521765930
  17. Commerford, B. P., Hatfield, R. C., & Houston, R. W. (2022). The effect of real-time transparency on auditors' judgments. Contemporary Accounting Research, 39(1), 652-677. https://doi.org/10.1111/1911-3846.12733
  18. Cunningham, G. M., & Harris, P. (2006). The Sarbanes-Oxley Act: Implications for forensic accountants. Journal of Forensic Accounting, 7(1), 65-80.
  19. Coram, P., Ng, J., & Woodliff, D. R. (2008). The effect of risk of misstatement on the propensity to commit reduced audit quality acts under time budget pressure. Auditing: A Journal of Practice & Theory, 27(2), 157-167. https://doi.org/10.2308/aud.2008.27.2.157
  20. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  21. DeAngelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3(3), 183-199. https://doi.org/10.1016/0165-4101(81)90002-1
  22. DeFond, M., & Zhang, J. (2014). A review of archival auditing research. Journal of Accounting and Economics, 58(2-3), 275-326. https://doi.org/10.1016/j.jaccceco.2014.09.002
  23. Deloitte. (2023). Audit Innovation: Argus Platform Overview. Deloitte & Touche LLP. https://www2.deloitte.com/
  24. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160. https://doi.org/10.2307/2095101
  25. Ernst & Young. (2023). EY Canvas: Transforming the Audit Experience. Ernst & Young Global Limited. https://www.ey.com/
  26. Eulerich, M., Masli, A., & Wood, D. A. (2021). Assurance via machine learning. The Accounting Review, 96(3), 263-296. https://doi.org/10.2308/TAR-2019-0449
  27. Ferguson, A., Francis, J. R., & Stokes, D. J. (2003). The effects of firm-wide and office-level industry expertise on audit pricing. The Accounting Review, 78(2), 429-448. https://doi.org/10.2308/accr.2003.78.2.429
  28. Financial Times. (2024). Big Four Accounting Firms: Global Market Share Analysis. https://www.ft.com/
  29. Francis, J. R., Maydew, E. L., & Sparks, H. C. (1999). The role of Big 6 auditors in the credible reporting of accruals. Auditing: A Journal of Practice & Theory, 18(2), 17-34. https://doi.org/10.2308/aud.1999.18.2.17
  30. Francis, J. R., & Wang, D. (2008). The joint effect of investor protection and Big 4 audits on earnings quality around the world. Contemporary Accounting Research, 25(1), 157-191. https://doi.org/10.1506/car.25.1.6
  31. Guénin-Paracini, H., Malsch, B., & Paillé, A. M. (2014). Fear and risk in the audit process. Accounting, Organizations and Society, 39(4), 264-288. https://doi.org/10.1016/j.aos.2014.02.001
  32. Guénin-Paracini, H., Malsch, B., & Tremblay, M. S. (2015). On the operational reality of auditors' independence: Lessons from the field. Auditing: A Journal of Practice & Theory, 34(2), 201-236. https://doi.org/10.2308/ajpt-50905
  33. Hammersley, J. S. (2006). Pattern identification and industry-specialist auditors. The Accounting Review, 81(2), 309-336. https://doi.org/10.2308/accr.2006.81.2.309
  34. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360. https://doi.org/10.1016/0304-405X(76)90026-X
  35. 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-51730
  36. KPMG. (2024). KPMG Clara: Smart Audit Platform. KPMG International. https://home.kpmg/
  37. Lawrence, A., Minutti-Meza, M., & Zhang, P. (2011). Can Big 4 versus non-Big 4 differences in audit-quality proxies be attributed to client characteristics? The Accounting Review, 86(1), 259-286. https://doi.org/10.2308/accr.00000009
  38. López, D. M., & Peters, G. F. (2012). The effect of workload compression on audit quality. Auditing: A Journal of Practice & Theory, 31(4), 139-165. https://doi.org/10.2308/ajpt-10305
  39. Moffitt, K. C., Richardson, V. J., Snow, M. S., Weisner, M. M., & Wood, D. A. (2018). Financial Statement Audits: Automation with Algorithms. Brigham Young University.
  40. Moore, D. A., Tetlock, P. E., Tanlu, L., & Bazerman, M. H. (2006). Conflicts of interest and the case of auditor independence: Moral seduction and strategic issue cycling. Academy of Management Review, 31(1), 10-29. https://doi.org/10.5465/amr.2006.19379621
  41. Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209-234. https://doi.org/10.1007/s10551-019-04407-1
  42. PCAOB. (2023). 2022 Inspection Observations. Public Company Accounting Oversight Board. https://pcaobus.org/
  43. Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53. https://doi.org/10.1016/j.adiac.2010.10.004
  44. PwC. (2024). GL.ai: Contract Analysis Platform. PricewaterhouseCoopers. https://www.pwc.com/
  45. Quick, R., & Warming-Rasmussen, B. (2009). Auditor independence and the provision of non-audit services: Perceptions by German investors. International Journal of Auditing, 13(2), 141-162. https://doi.org/10.1111/j.1099-1123.2008.00396.x
  46. SEC. (2023). Enforcement Actions Against Audit Firms. U.S. Securities and Exchange Commission. https://www.sec.gov/
  47. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
  48. Teoh, S. H., & Wong, T. J. (1993). Perceived auditor quality and the earnings response coefficient. The Accounting Review, 68(2), 346-366.
  49. Watkins, A. L., Hillison, W., & Morecroft, S. E. (2004). Audit quality: A synthesis of theory and empirical evidence. Journal of Accounting Literature, 23, 153-193.
  50. Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2022). The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access, 8, 110461-110477. https://doi.org/10.1109/ACCESS.2020.3002316
SciMeTech · Volume 2, Issue 2 · 2026 · ISSN 3085-5284