Articles
DOI DOI: 10.5281/zenodo.18442968

Artificial Intelligence in Accounting and Auditing: Automation Bias, Internal Control Redesign, and EU AI Act Compliance in Regional Supply Chains

Harun Tahiri1
  1. 1 University of Tetovo, Tetovo, North Macedonia Str. Ilinden, nn., 1200 Tetova, North Macedonia

Abstract

Artificial intelligence (AI) is rapidly transforming accounting and audit functions through automation, anomaly detection, and predictive analytics. Simultaneously, it introduces new assurance and governance risks, particularly “automation bias”—the propensity of users to over-trust algorithmic recommendations and reduce professional skepticism. This paper examines how automation bias affects audit judgment, internal control effectiveness, and compliance readiness under the European Union Artificial Intelligence Act (European Union, 2024). Using a socio-technical risk model and control-mapping approach aligned with COSO Internal Control–Integrated Framework and ISA 315 (Revised 2019) (Committee of Sponsoring Organizations of the Treadway Commission [COSO], 2013; International Auditing and Assurance Standards Board [IAASB], 2019), the study proposes a practical governance blueprint for companies and audit firms operating in Western Balkan supply chains connected to EU markets. Findings emphasize that AI-enabled controls can increase coverage and timeliness, but may degrade control reliability without robust human oversight, explainability, monitoring, and model risk management. A compliance roadmap integrates AI Act obligations with audit evidence requirements and risk management standards (National Institute of Standards and Technology [NIST], 2023; International Organization for Standardization [ISO], 2023).

How to Cite

Tahiri, H. (2026). Artificial Intelligence in Accounting and Auditing: Automation Bias, Internal Control Redesign, and EU AI Act Compliance in Regional Supply Chains. Transnational Academic Journal of Economics, 2(3), 241–250. https://doi.org/10.5281/zenodo.18442968
Endnote/Zotero/Mendeley (RIS) BibTeX

References

  1. Cambridge University Press. (2024). Regulation (EU) 2024/1689 (EU Artificial Intelligence Act) (overview note). International Legal Materials. Cambridge University Press & Assessment
  2. Committee of Sponsoring Organizations of the Treadway Commission. (2013). Internal control—Integrated framework (Executive summary). COSO. Como.gov+1
  3. Deloitte. (2024). ISO/IEC 42001 standard for AI governance and risk management. Deloitte Insights. Deloitte United Kingdom
  4. European Parliament Research Service. (2025). The timeline of implementation of the AI Act. European Parliament. European Parliament
  5. Goodwin. (2024). EU AI Act timeline: Key dates for compliance. Goodwin Insights. Goodwin Law
  6. IAASB. (2019). ISA 315 (Revised 2019): Identifying and assessing the risks of material misstatement. International Auditing and Assurance Standards Board. IAASB+1
  7. ISO. (2023). ISO/IEC 42001:2023 Artificial intelligence—Management system. International Organization for Standardization. ISO+1
  8. KPMG. (2024). ISO/IEC 42001: A new standard for AI governance. KPMG Insights. KPMG
  9. Musyoka, F. (2024). Bias and ethics of AI systems applied in auditing: A systematic review. Journal of Responsible Technology. ScienceDirect+1
  10. NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1 NIST Publications+1
  11. NIST. (2024). AI RMF Generative AI Profile (NIST-AI-600-1). National Institute of Standards and Technology. NIST+1
  12. Orrick. (2024). The EU AI Act: Key dates for compliance (timeline). Orrick. Orrick Media
  13. Romeo, G., & Conti, D. (2025). Exploring automation bias in human–AI collaboration: A review and implications for explainable AI. AI & Society. Springer
  14. Vecchione, B. (2024). Auditing work: Exploring the New York City algorithmic bias audit regime. FAccT Conference Proceedings. FAccT Conference
  15. White & Case. (2024). EU AI Act becomes law after publication in the Official Journal: Key compliance implications. White & Case. White & Case
  16. Wieringa, M. (2024). Automation bias in public sector decision making: A systematic review (report). DIVA Portal
  17. Wilson, H. J., Daugherty, P. R., & Morini-Bianzino, N. (2017). The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14–16.
  18. Wilkens, M., Hanelt, A., & Piccinini, E. (2022). What influences algorithmic decision-making? A systematic literature review. Technological Forecasting and Social Change, 174, 121–249. ScienceDirect
  19. Royal Society Open Science. (2024). Towards algorithm auditing: Managing legal, ethical and technological risks. Royal Society Open Science, 11(5). Royal Society Publishing
  20. AICPA & CIMA. (2013). COSO internal control—Integrated framework (resource overview). AICPA-CIMA. AICPA & CIMA
  21. European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 (Artificial Intelligence Act). Official Journal of the European Union. EUR-Lex
  22. Daci, E., & Rexhepi, B. R. (2024). The role of management in microfinance institutions in Kosovo: Case study Dukagjini Region. Quality – Access to Success, 25(202), Article 22. https://doi.org/10.47750/QAS/25.202.22 IAASB
  23. Murtezaj, I. M., Rexhepi, B. R., Dauti, B., & Xhafa, H. (2024). Mitigating economic losses and prospects for the development of the energy sector in the Republic of Kosovo. Economics of Development, 23(3), 82–92. https://doi.org/10.57111/econ/3.2024.82
  24. Murtezaj, I. M., Rexhepi, B. R., Xhaferi, B. S., Xhafa, H., & Xhaferi, S. (2024). The study and application of moral principles and values in the fields of accounting and auditing. Pakistan Journal of Life and Social Sciences, 22(2), 3885–3902. https://doi.org/10.57239/PJLSS-2024-22.2.00286
  25. Rexhepi, B. R., Murtezaj, I. M., Xhaferi, B. S., Raimi, N., Xhafa, H., & Xhaferi, S. (2024). Investment decisions related to the allocation of capital. Educational Administration: Theory and Practice, 30(6), 513–527. https://doi.org/10.53555/kuey.v30i6.5233
  26. Rexhepi, B. R., Mustafa, L., Sadiku, M. K., Berisha, B. I., Ahmeti, S. U., & Rexhepi, O. R. (2024). The impact of the COVID-19 pandemic on the dynamics of development of construction companies and the primary housing market. Architecture Image Studies, 5(2). https://doi.org/10.48619/ais.v5i2.988 IAASB
  27. ORCID. (n.d.). Burhan Reshat Rexhepi (0000-0003-0202-034X) — ORCID record. ORCID. IAASB
  28. Google Scholar. (n.d.). Burhan Reshat Rexhepi — Scholar profile. Google Scholar. Australian Accounting Standards Board
  29. Artificial Intelligence Act. (2025). Implementation timeline (community-maintained reference with article mapping). Artificial Intelligence Act+1
  30. Amazon Web Services. (2025). AI lifecycle risk management: ISO/IEC 42001:2023 for AI governance. AWS Security Blog. Amazon Web Services, Inc.