The Role and Application Areas of Artificial Intelligence in Tax Auditing

Öz

The acceleration of digitalisation on a global scale has brought about a transformative process that necessitates the integration of artificial intelligence (AI) technologies into tax auditing procedures. AI-driven audit applications make a significant contribution to reducing tax losses and curbing the shadow economy. In this study, the concepts of digitalisation and artificial intelligence are first examined within a theoretical framework, followed by an evaluation of the main areas of application of these technologies in tax audit processes and international examples. Subsequently, it is demonstrated that digitalisation has become not merely a technical modernisation element for the Turkish tax administration, but also a strategic management tool. Through the integration of AI technologies, tax auditing has evolved into a more holistic, proactive, and data-driven structure. An important finding revealed by the study is that, despite the positive effects of digitalisation on audit quality, there remain areas in which operational efficiency has not yet been fully optimised.

Anahtar Kelimeler

• Artificial Intelligence
• Digitalization
• Tax Audit
• Tax Administration 3.0

Vergi Denetiminde Yapay Zekânın Rolü ve Uygulama Alanları

Öz

Dijitalleşmenin küresel ölçekte ivme kazanması, vergi denetim süreçlerinde yapay zekâ teknolojilerinin entegrasyonunu zorunlu kılan bir dönüşüm sürecini beraberinde getirmiştir. Yapay zekâ destekli denetim uygulamaları, vergi kayıplarının ve kayıt dışı ekonominin azaltılmasına önemli ölçüde katkı sağlamaktadır. Çalışmada öncelikle kuramsal çerçevede dijitalleşme ve yapay zekâ kavramları incelendikten sonra bu teknolojilerin vergi denetim süreçlerindeki temel uygulama alanları ile uluslararası düzeydeki örnekler değerlendirilmiştir. Daha sonra dijitalleşmenin Türk vergi idaresi açısından yalnızca teknik bir modernizasyon unsuru değil, aynı zamanda stratejik bir yönetim aracı hâline geldiği ortaya konulmaktadır. Vergi denetimi, yapay zekâ teknolojilerinin entegrasyonu sayesinde daha bütüncül, proaktif ve veri odaklı bir yapıya dönüşmüştür. Çalışmanın ortaya koyduğu önemli bir gerçeklik, dijitalleşmenin denetim kalitesi üzerindeki olumlu etkilerine rağmen, operasyonel verimlilikte henüz tam anlamıyla optimize edilememiş alanların bulunduğudur.

Anahtar Kelimeler

• Yapay Zeka
• Dijitalleşme
• Vergi Denetimi
• Vergi İdaresi 3.0

Kaynakça

1. Akdoğan, T., & Yavuz, H. (2022). Dijitalleşme Perspektifinden Vergi Uyumu. Ankara: İksad Publishing House.
2. Alina, C. M., Cerasela, S. E., & Gabriela, G. (2018). Internal Audit Role in Artificial Intelligence. Ovidius University Annals, Economic Sciences Series, 18(1), 441-445.
3. Arslan, Y. & Yiğit, S. (2024), Vergi İnceleme Sürecinde Kullanılan Bilgi Teknolojileri ve Dijital Dönüşüm. Vergi Raporu Dergisi, 301, 7-19.
4. Asquith, R. (2025, New Models of Artificial Intelligence (AI) and Machine Learning (ML) are Being Evaluated by Global Tax Authorities to Tackle Fraud. Vat Calc. (Erişim: 11.07.2025), https://www.vatcalc.com/artificial-intelligence/tax-authorities-adopt-ai-for-tax-fraud-and-efficiencies/
5. Avcı, O. (2021). Vergi Tahsilatında Yapay Zekânın Kullanımı ve Önemi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 22(1), 51-63.
6. B1 Analytics. (2025). Tax Monitoring: changes in AIS “Nalog‑3” services. Retrieved from article on B1 Group news portal. (Erişim:11.07.2025), https://b1.ru/en/tax-messenger/
7. Beebe, J. (2023). AI and Taxes - A Work in Progress: Part 1. Rice University's Baker Institute for Public Policy. (Erişim:09.06.2025), https://www.bakerinstitute.org/research/ai-and-taxes-work-progress-part-1
8. Belahouaoui, R. ve Attak, E.H. (2024). Digital Taxation, Artificial Intelligence and Tax Administration 3.0: Improving Tax Compliance Behavior-A Systematic Literature Review Using Textometry (2016-2023). Accounting Research Journal, 37(2), 172-191.

9. Bird, R. M., & Zolt, E. M. (2008). Technology and Taxation in Developing Countries: From Hand to Mouse. UCLA School of Law, Law-Econ Research, 8(7), 1-57.
10. Bizarro, P. A., & Dorian, M. (2017). Artificial Intelligence: The Future of Auditing. Internal Auditing, 5, 21-26.
11. Bozdoğanoğlu, B. & Yücel, A. (2025). Use of Artificial Intelligence Technology in Tax Management: Evaluation of the Principle of Transparency Within the Framework of International Legislation Provisions. International Journal of Public Finance, 10, 1-20.
12. Bozdoğanoğlu, B. (2023). Yapay Zekâ ChatGPT’nin Vergi Sistemlerinde Kullanımı ve Uyuşmazlıkları Önlemedeki Rolünün Değerlendirilmesi. Mali Hukuk Dergisi, 19(224), 1545-1588.
13. Collosa, A., & Vasco, D. C. (2023). Can ChatGPT be Used in Tax Administrations? (Part 1). (Erişim:11.05.2025)https://www.ciat.org/ciatblog-podra-utilizarse-chat-gpt-en-las-administraciones-tributarias/?lang=en
14. Costa, P., & Krivinskas, M. (2021). The Digital Journey of The Tax Funtion. (Erişim: 22.06.2025), https://www.itrworldtax.com/
15. Çakmak, E. K. (2025). Dijital Çağda Vergi İdareleri: Türk Vergi İdaresinin Dijital Dönüşüm Olgunluğu. Maliye Dergisi, 188, 215-250.
16. Çolak, M. (2023). Mali Hukuk ve Dijitalleşme. Ankara: Adalet Yayınevi.
17. Ding, F. (2017). AI Tax Will Become the Black Technology of the Tax System, (Erişim:11.05.2025), http://www.shui5.cn/article/ef/113244.html
18. Dommel, P., & Chenok, D. (2023). How Can AI Improve Performance in Tax Administration? IBM Center of The Business of Government. (Erişim: 09.08.2025), https://www.businessofgovernment.org/blog/how-can-ai-drive-better-taxpayer-experience-and-improved-tax-agency-operations
19. Erdoğan, M. (2019). Denetim 4.0 ve Ötesi. Journal of Accounting and Taxation Studies, 12(3), 809-834.
20. Erkan, S. (2019). Yapay Zekâ ve Makine Öğreniminin Vergi Uygulamalarında Kullanımı ve Karar Almaya Etkisi. Vergi Dünyası Dergisi, 1(457), 59-66.
21. European Commission. (2020). The Use of Artificial Intelligence in Tax Administration: A Study of Member States, (Erişim:07.9.2025), https://ec.europa.eu/taxation_customs/sites/taxation/files/2020_ai-in-taxation_study.pdf
22. Federal Tax Service of Russia. (2025). Capacity Building & Digital Services Highlights. Retrieved From Official Site, (Erişim: 01.05.2025), https://www.nalog.gov.ru/eng/
23. Fidelangeli, A. & Galli, F. (2021). Artificial Intelligence and Tax Law: Perspectives and Challenges. CERIDAP, 24-58.
24. GİB. (2023). Gelir İdaresi Başkanlığı 2023.Faaliyet Raporu. (Erişim:11.05.2025), https://cdn.gib.gov.tr/api/gibportal-file/file/getFileResources?objectKey=arsiv/fileadmin/faaliyetraporlari/2023/2023_faaliyet_raporu.pdf
25. Global VAT Compliance. (2024a). UK Tax Authority builds AI team and plans new compliance tools. (Erişim:01.05.2025), https://www.globalvatcompliance.com/technology/mytaxflow/
26. Grönkvist, O. & Ogenborg, M. (2025). From Paper Flips to Data Scripts: A Case Study on Public Sector AI Implementation. BUSN79 - Degree Project in Accounting andFinance. (Erişim:11.05.2025), https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9203495&fileOId=9203501
27. Guarino, G. (2024). Artificial intelligence will be an integral part of the Italian tax system. The Conservative. (Erişim:11.07.2025), https://www.theconservative. online/en/artificial-intelligence-integral-part-italian-tax-system
28. Günay, H. F. (2025), Çeşitli Faktörler Işığında Türkiye’de Vergi Denetim Etkinliğinin İncelenmesi. The Journal of International Scientific Researches, 10(1), 101-117.
29. HMRC. (2024a). New HMRC service announced for workers to take control of their tax affairs. (Erişim:18.05.2025), https://www.gov.uk/government/news/new-hmrc-service-announced-for-workers-to-take-control-of-their-tax-affairs
30. HMRC. (2024b). HMRC’s Transformation Roadmap. (Erişim:17.05.2025), https://www.gov.uk/government/publications/hmrc-transformation-roadmap/hmrcs-transformation-roadmap
31. Houser, K., & Sanders, D. (2018). The Use of Big Data Analytics by the IRS: What Tax Practitioners Need to Know. Journal of Taxation, 128(2), 1-15.
32. Huang, M. H., & Rust, R. (2018). Artificial Intelligence in Servise. Journal of Servise Research, 21(2), 155-172.
33. Huang, Z. W. (2018). Discussion on the Development of Artificial Taxation. American Journal of Industrial and Business Management, 8(8), 1817-1824.
34. İlgün, M. F. (2020). Vergi Denetim Sürecinde Büyük Veri Analitiği. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 8(1), 1-24.
35. Jing-Yi, L. (2019). Research on the Reform of Tax Collection and Management System by Artificial Intelligence Under the Background of “Intelligence” Development Strategy. Advances in Economics, Business and Management, 109, 1029-1034.
36. Kanca, O. C., & Yamak, R. (2023). Türkiye Ekonomisi İçin Olivera-Tanzi Etkisinin Sınanması: ARDL Metodu. Maliye Araştırmaları. Bursa: Ekin Yayınevi.
37. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, In My Hand: Who’s The Fairest In The Land? On The Interpretations, Illustrations, And Implications Of Artificial Intelligence. Business Horizon Science Direct, 62(1), 15-25.
38. Karataş Durmuş, N., & Arıtı Erdem, İ. (2023). Vergi İdaresi 3.0: Yapay Zekâ Perspektifinden Bir İnceleme. Maliye Dergisi, 184, 225-253.
39. Kreston Reeves. (2024). Taxpayer data now on HMRC AI system. (Erişim:19.06.2025), https://www.krestonreeves.com/news/taxpayer-data-now-on-hmrc-ai-system/
40. Marinello, A. (2024). L’utilizzo dell’intelligenza artificiale da parte del Fisco: limiti e prospettive, anche alla luce dell’Artificial Intelligence Act dell’Unione Europea, Rivista di diritto tributario. (Erişim:03.05.2025), https://www.rivistadirittotributario.it/2024/12/05/lutilizzo-dellintelligenza
41. Mergel, I., Kattel, R., Lember, V., & Mcbride, K. (2018). Citizen-Oriented Digital Transformation in The Public Sector, Proceedings of The 19th Annual International Conference on Digital Government Research: Governance in the Data Age, 1-3. (Erişim:03.05.2025), https://doi.org/10.1145/3209281.320929
42. Misuraca, G., & Van Noordt, C. (2020). Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU. Publications Office of the European Union. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, Athens-Greece, 1-10.
43. Mittelstadt, B.D., Allo, P. Taddeo, M., Wachter, S. & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2),1-21.
44. OECD (2022a), Digital Services: Supporting SMEs to get Tax Right, OECD Forum on Tax Administration, Paris: OECD Publishing. (Erişim:19.06.2025), https://www.oecd.org
45. OECD (2023a), Tax Administration 2023: Comparative Information on OECD and other Advanced and Emerging Economies, Paris: OECD Publishing. (Erişim:20.06.2025), https://www.oecd.org
46. OECD. (2016). Advanced Analytics for better Tax Administration. OECD Publishing. (Erişim:19.06.2025), https://www.oecd.org
47. OECD. (2017a). The Changing Tax Compliance Environment and the Role of Audit. OECD Publishing. (Erişim:19.06.2025), https://www.oecd.org
48. OECD. (2017b). Tax Administration 2017: Comparative Information on OECD and other Emerging Economies. OECD Publishing, (Erişim:19.06.2025), https://www.oecd.org
49. OECD. (2018). Resumen de Los Desafíos Fiscales Derivados de la Digitalización: Informe Provisional 2018. OECD Publishing. (Erişim:19.06.2025), https://www.oecd.org
50. OECD. (2020). Tax Administration 3.0: The Digital Transformation of Tax Administration. OECD Publishing. (Erişim:19.06.2025), https://www.oecd.org
51. OECD. (2022). Tax Administration 2022: Comparative Information on OECD and Other Advanced and Emerging Economies. OECD Publishing. (Erişim: 20.06.2025), https://www.oecd.org
52. OECD. (2023). Tax Administration 2023: Comparative Information on OECD and other Advanced and Emerging Economies. OECD Publishing. (Erişim:21.06.2025), https://www.oecd.org
53. Özkaya, L., & Özkaya, L. (2019). Yapay Zekânın Muhasebe ve Vergi Denetimi Mesleğine Sunacağı Katkılar. Vergi Raporu, 236, 314-323.
54. Palabıyık, D. Ç. (2024). Elektronik Vergi Denetimi Dönemi Başlıyor. (Erişim:20.05.2025), https://www.aa.com.tr/tr/ekonomi/elektronik-vergi-denetimi-donemi-bas liyo r/3307403#
55. Pingrui, L. & Xu, Z. (2024). The Impact Of Digital Transformation On Corporate Supply Chain Management: Evidence From Listed Companies. Finance research letters. New York : Elsevier Science, 60,1-10.
56. Pires, M. (2024). Artificial Intelligence (AI) in the field of Tax Administration. Review of International and European Economic Law, 3(5). 1-9
57. Pirim, H. (2006). Yapay Zekâ. Journal of Yaşar University, 81-93.
58. Rai, Dilli Hang, (2024). Artificial Intelligence Through Time: A Comprehensive Historical Review, Tribhuvan University,1-24.(Erişim:08.06.2025) https://www.researchgate.net/profile/Dilli-Hang-Rai/publication
59. Rathi, A., Sharma, S., Lodha, G., & Srivastava, M. (2021). A Study on Application of Artificial Intelligence and Machine Learning in Indian Taxation System. Psychology and Education Journal, 58(2), 1226-1233.
60. Resmi Gazete (2025). 13.05.2025 Tarih ve 32899 Sayılı Resmi Gazete
61. Saragih, A. H., Reyhani, Q., & Setyowati, M. S. (2023). The Potential of an Artificial Intelligence (AI) Application for the Tax Administration System’s Modernization: The Case of Indonesia. Artificial Intelligence and Law, 31(3), 491-514.
62. Shakil, M. H., & Tasnia, M. (2022). Artificial Intelligence and Tax Adminisration in Asia and the Pacific. Taxation in the Digital Economy, 45-55.
63. T.C. Hazine ve Maliye Bakanlığı Gelir İdaresi Başkanlığı Strateji Geliştirme Daire Başkanlığı (2021), 2020 Yılı Faaliyet Raporu, No:387, Ankara, (Erişim:02.09.2025) https://ms.hmb.gov.tr/uploads/2021/03/2020-HMB-Idare-Faaliyet-Raporu.pdf
64. T.C. Hazine ve Maliye Bakanlığı Gelir İdaresi Başkanlığı Strateji Geliştirme Daire Başkanlığı (2022), 2021 Yılı Faaliyet Raporu, No:429, Ankara, (Erişim:02.09.2025) https://ms.hmb.gov.tr/uploads/2023/03/2022-HMB-Idare-Faaliyet-Raporu-.pdf https://ms.hmb.gov.tr/uploads/2022/03/Hazine-ve-Maliye-Bakanligi-2021-Yili-Faaliyet-Raporu.pdf
65. T.C. Hazine ve Maliye Bakanlığı Gelir İdaresi Başkanlığı Strateji Geliştirme Daire Başkanlığı (2023), 2022 Yılı Faaliyet Raporu, No:455, Ankara (Erişim:02.09.2025) https://ms.hmb.gov.tr/uploads/2024/04/Hazine-ve-Maliye-Bakanligi-2023-Yili-Faaliyet-Raporu.pdf
66. T.C. Hazine ve Maliye Bakanlığı Gelir İdaresi Başkanlığı Strateji Geliştirme Daire Başkanlığı (2024), 2023 Yılı Faaliyet Raporu, No:515, Ankara (Erişim:02.09.2025), https://ms.hmb.gov.tr/uploads/2024/04/Hazine-ve-Maliye-Bakanligi-2023-Yili-Faaliyet-Raporu.pdf
67. T.C. Hazine ve Maliye Bakanlığı Gelir İdaresi Başkanlığı Strateji Geliştirme Daire Başkanlığı (2025), 2025 Yılı Performans Programı, No:528, (Erişim:02.09.2025) https://ms.hmb.gov.tr/uploads/2025/02/HMB-2025-Yili-Performans-Programi.pdf
68. T.C. Hazine ve Maliye Bakanlığı Vergi Denetim Kurulu Başkanlığı (2024), 2024 Yılı Faaliyet Raporu, (Erişim:19.05.2025) https://vdk.hmb.gov.tr/duyuru/2024-yili-faaliyet-raporu-yayimlanmistir
69. T.C. Hazine ve Maliye Bakanlığı Vergi Denetim Kurulu Başkanlığı, (2019). Faaliyet Raporu 2018. (Erişim:08.06.2025), https://ms.hmb.gov.tr/uploads/2019/04/VDK-2018-Birim-Faaliyet-Raporu-1102019-002.pdf
70. T.C. Hazine ve Maliye Bakanlığı Vergi Denetim Kurulu Başkanlığı, (2024). Faaliyet Raporu 2023. (Erişim:02.09.2025), https://ms.hmb.gov.tr/uploads/sites/17/2024/03/VDK-2023-Yili-Faaliyet-Raporu.pdf
71. Turan, D. (2020). Yapay Zekâ ve Vergi Uygulamalarına Etkisi. Anadolu Akademi Sosyal Bilimler Dergisi, 2(1), 55-70.
72. Turing, A. M. (1995). Computing Machinery and Intelligence. Computation & Intelligence. American Association for Artificial Intelligence, 44-53.
73. VATCalc. (2025, June 13). Italy turns to AI to tackle VAT fraud.VATCalc.(Erişim:20.05.2025) https://www.vatcalc.com/italy/italy-new-consolidated-vat-code-jan-2026/
74. Warner, K. S., & Wager, M. (2019). Building Dynamic Capabilities for Digital Transformation: An Ongoing Process of Strategic Renewal. Long Range Planning, 52(3), 326-349.
75. Yayman, D. (2021). Vergi İdarelerinin Gelecek Senaryoları. International Journal of Social And Humanities Sciences Research, 8(76), 2872–2795.
76. Yereli, A. B., & Orkunoğlu Şahin, I. (2019). Yapay Zekânın Ekonomi, Toplumsal Refah ve Vergilendirme Üzerindeki Etkisi. Vergi Dünyası, 39 (459), 58-71.
77. Yıldız, F. ve Köşer, Ö. (2025). Yapay Zekâ Uygulamalarının Vergi Gelirlerine Etkisi, Vergi Raporu, 313, 7-23.
78. Yılmaz, M., & Akcan, A. T. (2022). Vergi Tahmin Sapması ve Ekonomik Büyüme İlişkisi. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(2), 430-438.
79. Zhou, L. (2019). Opportunities and Challenges of Artificial Intelligence in the Application. Advances in Economics. Business and Management, 109, 201-206.
80. Zhurenkov, D. A., Poikin, A. E., Saveliev, A. M., & Berkutova, T. A. (2021). Methods and Approaches of Artificial Intelligence in Taxation Area Post-non-classical Scientific Rationality Post-non-classical Scientific Rationality. IFAC PapersOnLine, 54(13), 194-199.