Diji̇tal Dönüşüm ve Yapay Zeka Destekli̇ Deneti̇m: 2025'te İç Deneti̇m Süreçleri̇nde Teknoloji̇Nnin Rolü

Abstract

Son dönemde yapay zeka (AI) teknolojileri ve dijital dönüşüm kavramları iç denetim faaliyetlerinde değişimlere yol açmıştır. Geleneksel denetim teknikleri genellikle teknolojiden çok fazla yararlanılmadan gerçekleştirilirken, yeni dijital denetimler teknolojik altyapıları kullanarak bu süreçleri tamamlıyor. Bu dönüşüm denetçilerin çalışma şeklini değiştirmekte ve böylece denetim süreçlerinde yeni bir değer sistemi oluşturmaktadır. Bu yeni değer sistemi yapay zeka destekli denetim sistemidir. Yapay zekâ destekli denetim sistemleri, işletmelerde oluşabilecek potansiyel risklerin daha erken bir aşamada tespit edilmesine yardımcı olmaktadır. Böylece işletmelerin finansal raporlamalarının güvenilirliğini artıracaktır. İşletmelerdeki hata ve usulsüzlükleri tespit etmek için harcanan zamanı önemli ölçüde azaltacaktır. Ayrıca denetçilerin rutin ve zaman alan işlemler yerine katma değerli faaliyetlere odaklanmasını sağlayacaktır. Özellikle denetim faaliyetlerinde hatalı analizler, oluşabilecek etik sorunlar vb. durumlar teknolojik gelişmeler ve yapay zekâdan kaynaklanan olumsuz durumlar olarak karşımıza çıkmaktadır. Ayrıca denetim faaliyetlerinde güvenlik ve gizliliğin önemini yitirmesi gibi tehlikeli durumların varlığı teknolojik sistemler açısından yeni kontrol mekanizmalarının oluşturulması ve kurulması gerekliliğini doğurmaktadır. Bu çalışma, yapay zeka ve teknolojik sistemlerin başta denetim olmak üzere iç denetim faaliyetleri üzerindeki etkilerini belirlemeyi amaçlamaktadır. Bu amaçla literatürde saha araştırması yapılmıştır. Çalışmada dijital dönüşüm ve yapay zekânın iç denetim fonksiyonlarının daha verimli ve güvenilir hale gelmesine katkı sağladığı ancak yeni riskleri de beraberinde getirdiği sonucuna ulaşılmıştır.

Keywords

• Dijital Dönüşüm
• Yapay Zeka
• İç Denetim
• Denetim Teknolojileri

Digital Transformation and Artificial Intelligence-Assisted Auditing: The Role of Technology in Internal Audit Processes in 2025

Abstract

Recently, artificial intelligence (AI) technologies and digital transformation concepts have led to changes in internal audit activities. While traditional audit techniques are usually performed without much use of technology, new digital audits complete these processes by using technological infrastructures. This transformation changes the way auditors work and thus creates a new value system in audit processes. This new value system is the artificial intelligence assisted audit system. Artificial intelligence assisted audit systems help to detect potential risks that may occur in enterprises at an earlier stage. Thus, it will increase the reliability of financial reporting of enterprises. It will significantly reduce the time spent to detect errors and irregularities in businesses. It will also enable auditors to focus on value-added activities instead of routine and time-consuming operations. In particular, situations such as incorrect analysis in audit activities, ethical problems that may occur, etc. appear as negative situations arising from technological developments and artificial intelligence. In addition, the existence of dangerous situations such as the loss of the importance of security and confidentiality in audit activities creates the necessity to establish and establish new control mechanisms in terms of technological systems. This study aims to determine the effects of artificial intelligence and technological systems on internal audit activities, especially auditing. For this purpose, field research was conducted in the literature. The study concludes that digital transformation and artificial intelligence contribute to making internal audit functions more efficient and reliable, but also bring new risks.

Keywords

• Digital Transformation
• Artifical Intelligence
• Internal Audit

References

1. Albrecht, W. S., Albrecht, C. C., & Albrecht, C. O. (2021). Fraud Examination. Cengage Learning.
2. Ali M. M., Abdullah A.S., & Khattab G.S. (2022). The Effect of Activating Artificial Intelligence techniques on Enhancing Internal Auditing Activities “Field Study”. Alexandria Journal of Accounting Research, 3(6), 1-40.
3. Álvarez-Foronda, R., De-Pablos-Heredero, C., & Rodríguez-Sánchez, J. L. (2023). Implementation model of data analytics as a tool for improving internal audit processes. Front Psychol. 2023 Feb 10;14:1140972. doi: 10.3389/fpsyg.2023.1140972. PMID: 36844358; PMCID: PMC9950503
4. American Institute of Certified Public Accountants (AICPA), (2017). Guide to audit analytics an overview. Available at: https://www.aicpa.org/resources/article/guide-to-audit-data-analytics-an-overview
5. Anderson, U.L., Head, M.J., Ramamoorti, S., Riddle, C., Salamasick, M., & Sobel, P.J. (2017). Internal Auditing: Assurance and Advisory Services, 4th ed., Institute of Internal Auditors Research Foundation.
6. 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(4): 1-27.
7. Belle, V. (2019). The Quest for Interpretable and Responsible Artificial Intelligence. Biochem (Lond), 41(5): 16–19. https://doi.org/10.1042/BIO04105016
8. Brown, P. and Wong, J. (2023). AI and the Future of Internal Audit. Journal of Emerging Technologies in Accounting, 20(2), 67-78.

9. Bubinger, H., & David Dinneen, J. (2021). Actionable Approaches to Promote Ethical AI in Libraries. Proceedings of the Association for Information Science and Technology, 58(1), 682 – 684. https://doi.org/10.1002/pra2.52
10. Cetinic, E., & She, J. (2022). Understanding and Creating Art with AI: Review and Outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(2), 1 – 22. https://doi.org/10.1145/3475799
11. Changchit, C., & Holsapple, C.W. (2004). The development of an expert system for managerial evaluation of internal controls. Intelligent Systems in Accounting, Finance and Management, 12: pp. 103-120. https://doi.org/10.1002/isaf.246
12. Chiu, V., Liu, Q., & Vasarhelyi, M. A. (2014). The development and intellectual structure of continuous auditing research. Journal of Accounting Literature, 33(1-2), 37-57.
13. Colavizza, G., Blanke, T., Jeurgens, C., & Noordegraaf, J. (2021). Archives and AI: An Overview of Current Debates and Future Perspectives. https://dl.acm.org/doi/10.1145/3479010
14. Cular, M., Slapni_car, S., & Vuko, T. (2020), “The effect of internal auditors’ engagement in risk management consulting on external auditors’ reliance decision”, European Accounting Review, 29(5), 999-1020.
15. Daly, A., Hagendorff, T., Hui, L., Mann, M., Marda, V., Wagner, B., Wang, W., & Witteborn, S. (2019). Artificial Intelligence Governance and Ethics: Global Perspectives. The Chinese University of Hong Kong Faculty of Law Research Paper No. 2019-15, University of Hong Kong Faculty of Law Research Paper No. 2019/033, Available at SSRN: https://ssrn.com/abstract=3414805 or http://dx.doi.org/10.2139/ssrn.3414805
16. Demir, E., & Çelik, A. (2024). Dijital Dönüşümün İç Denetim Üzerine Etkileri. Muhasebe ve Denetim Araştırmaları Dergisi, 16(1), 28-45.
17. Du, X., Hargreaves, C., Sheppard, J., Anda, F., Sayakkara, A., Le-Khac, N. A., & Scanlon, M. (2020). SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation. https://dl.acm.org/doi/10.1145/3407023.3407068
18. Eining M.M., & Dorr, P.B. (1991). The impact of expert system usage on experiential learning in an auditing setting, Journal of Information Systems, 1-16
19. Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., G. Fiedorowicz, J., L. Smith, A., & Solmi, M. (2024). How to optimize the systematic review process using AI tools. JCPP Adv. 2024 Apr 23;4(2):e12234. doi: 10.1002/jcv2.12234. PMID: 38827982; PMCID: PMC11143948.
20. Fedyk, A., Hodson, J., Khimich, N., & Fedyk T. (2022). Is artificial intelligence improving the audit process?. Review Account Studies, 27, 938-985 https://doi.org/10.1007/s11142-022-09697-x
21. Gibson, L., & Patel, R. (2025). AI-Driven Auditing and Regulatory Challenges. Journal of Accounting & Technology, 31(2), 85-102.
22. Giles, K. M. (2019). How Artificial Intelligence & Machine Learning Will Change the Future of Financial Auditing: An Analysis of The University of Tennessee's Accounting Graduate Curriculum.
23. Gonzalez, R., Lee, S. & Park, J. (2024). Digital Transformation in Internal Auditing. Accounting Horizons, 38(1), 32-45.
24. Guan, H., Dong, L., & Zhao, A. (2022). Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making. Behav Sci (Basel). 2022 Sep 16;12(9):343. doi: 10.3390/bs12090343. PMID: 36135147; PMCID: PMC9495402.
25. H. Stanfill, M. & T. Marc, D. (2019). Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management. Yearb Med Inform. 2019 Aug;28(1):56-64. doi: 10.1055/s-0039-1677913. Epub 2019 Aug 16. PMID: 31419816; PMCID: PMC6697524.
26. Harrison, J. (2024). Transparency and Accountability in AI Auditing. Regulatory Compliance Review, 19(4), 134-150.
27. IAASB. (2018). Feedback statement-exploring the growing use of technology in the audit, with a focus on data analytics. New York: IAASB, https://www.iaasb.org/publications/feedback-statement-exploring-growing-use-technology-audit-focus-data-analytics
28. 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. doi:10.2308/jeta-10511
29. Jiang, L., Messier, F.W., Jr, & Wood, A. D. (2020). The association between internal audit operations related services and firm operating performance, Auditing: A Journal of Practice and Theory, 39(1), pp. 101-124. Johnson, M. & Smith, R. (2024). Blockchain and AI in Audit: Synergies and Challenges. The CPA Journal, 94(1), 78-92.
30. Jones, M., Smith, T. & Garcia, P. (2024). Machine Learning in Internal Audits: Future Trends. Journal of Financial Auditing, 28(1), 112-130.
31. Karmańska, A. (2022). Artificial Intelligence in audit. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 66(4), 87-99.
32. Kaya, H. (2023). Yapay Zekâ Destekli Denetimin Geleceği. Türkiye Muhasebe ve Denetim Dergisi, 24(3), 52-65.
33. Khaliq, Z., Umar Farooq, S., & Ashraf Khan, D. (2022). Artificial Intelligence in Software Testing: Impact, Problems, Challenges and Prospect. [PDF]
34. Lassa, T. (2012). The Beginning of the End of Driving. Available at: http://www.motortrend.com/news/the-beginning-of-the-end-ofdriving/
35. Li, E., Xu, H., & Li, G. (2020, April). Analysis on Improvement of Internal Audit in China’s Listed Companies Based on Artificial Intelligence. In 3rd International Conference on Advances in Management Science and Engineering(IC-AMSE2020) (pp.25-30). Atlantis Press.
36. Mahyoro, A.K. & Kasoga, P.S. (2021). Attributes of the internal audit function and effectiveness of internal audit services: evidence from local government authorities in Tanzania. Managerial Auditing Journal.
37. Manheim, D., Martin, S., Bailey, M., Samin, M., & Greutzmacher, R. (2024). The Necessity of AI Audit Standards Boards. https://www.aimodels.fyi/papers/ arxiv/necessity-ai-audit-standards-boards
38. Mazars. (2024). AI and Digital Transformation in Risk Management.
39. Miller, K. & Thompson, B. (2024). AI-Powered Decision Making in Audits: Risks and Opportunities. Business Intelligence Journal, 26(3), 88-105.
40. Muley, A., Muzumdar, P., Kurian, G., & Prasad Basyal, G. (2023). Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework. Asian Journal of Medicine and Health, 21(10), 276-291, 2023; Article no. AJMAH.104899 ISSN: 2456-8414
41. Nonnenmacher, J., Kruse, F., Schumann, G. & Marx Gómez, J. (2021, January). Using Autoencoders for Data-Driven Analysis in Internal Auditing. In Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 5748-5757).
42. O’Reilly, D. (2024). Big Data and Predictive Analytics in Auditing. Audit Innovation Review, 22(2), 76-95.
43. Omoteso, K. (2012). The application of artificial intelligence in auditing: Looking back to the future, Expert Systems with Applications, 39(9), 8490-8495, https://doi.org/10.1016/j.eswa.2012.01.098.
44. Ranjbar, A., Wermundsen Mork, E., Ravn, J., Brøgger, H., Myrseth, P., Peter Østrem, H., & Hallock, H. (2024). Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation?. Risk Manag Healthc Policy. 2024 Apr 10;17:877-882. doi: 10.2147/RMHP.S452337. PMID: 38617593; PMCID: PMC11016246.
45. Şahin, M. & Yıldırım, B. (2023). Denetimde Dijital Dönüşüm: Türkiye’deki Uygulamalar. Finans ve Muhasebe Araştırmaları Dergisi, 19(2), 63-81.
46. Singh, A., Dwivedi, A., Agrawal, D., & Singh, D. (2023). Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability. Oper Manag Res. 2023 Jan 13:1–17. doi: 10.1007/s12063-022-00344-x. Epub ahead of print. PMCID: PMC9838524.
47. Smith, A. & Garcia, J. (2025). Continuous Learning Algorithms in Fraud Detection. International Journal of Auditing Technology, 29(1), 97-115.
48. Smith, S. (2018). Data Analytics in an Audit: Examining Fraud Risk and Audit Quality. https://core.ac.uk/reader/231828510
49. Steinbart, P. J., Raschke, R. L., Gal, G., & Dilla, W. N. (2018). The influence of a good relationship between the internal audit and information security functions on information security outcomes. Accounting, Organizations and Society, 71, 15-29.
50. Sun, T. & Vasarhelyi, M. A. (2018). Embracing textual data analytics in auditing with deep learning, pp. 49-67.
51. Velarde, G. (2020). Artificial Intelligence and its impact on the Fourth Industrial Revolution: A Review. International Journal of Artificial Intelligence & Applications (IJAIA) 10(6), 41-48.
52. Williams, H. & Zhang, L. (2024). The Role of AI in Shaping Future Audit Practices. Journal of Emerging Technologies in Finance, 27(2), 121-140.
53. Yılmaz, A. & Korkmaz, T. (2022). İç Denetimde Yapay Zekâ Kullanımı ve Etkileri. Denetim ve Muhasebe Bilimleri Dergisi, 14(4), 39-55.
54. Zhu, W., J. Miao, J. Hu, & L. Qing. (2014). Vehicle detection in driving simulation using extreme learning machine. Neurocomputing, 128 (March 27): 160-165.