Yapay Zekâ ve Vergi Uygulamaları: ChatGPT’nin Kullanım Alanları, Etkileri ve Etik Zorluklar

Öz

Bu çalışma, yapay zekâ teknolojilerinden biri olan ChatGPT’nin kamu maliyesi alanındaki potansiyel kullanım alanlarını ve etkilerini incelemektedir. ChatGPT’nin vergi yönetimi, denetim süreçleri, büyük veri analizi ve mali politika geliştirme gibi süreçlerde sunabileceği katkılar ele alınmakta; vergi uyumunu artırma, denetimlerin otomasyonu ve karar destek sistemlerinin güçlendirilmesi gibi işlevleri değerlendirilmektedir. Çalışma ayrıca veri gizliliği, önyargı ve yapay zekâ sistemlerinin güvenilirliği gibi etik sorunlara da dikkat çekmekte ve bu risklerin yönetilmesine yönelik çözüm önerileri sunmaktadır. Yapılan analizler, ChatGPT’nin kamu maliyesinde önemli bir dönüşüm potansiyeline sahip olduğunu ve devletlerin mali kaynaklarını daha verimli kullanmasına katkı sağlayabileceğini ortaya koymaktadır. Bununla birlikte, yapay zekâ teknolojilerinin kamu yönetiminde güvenli ve adil bir şekilde kullanılabilmesi için güçlü etik çerçevelerin ve yasal düzenlemelerin geliştirilmesi gerekmektedir. Çalışma, gelecekte yapılacak araştırmaların, ChatGPT’nin kamu maliyesindeki kullanımını daha derinlemesine incelemesi gerektiğini vurgulamaktadır.

Anahtar Kelimeler

• ChatGPT
• Yapay Zekâ
• Vergi Uyumu

Artificial Intelligence and Public Finance: The Potential Applications, Impacts, and Ethical Challenges of ChatGPT

Abstract

This study examines the potential application areas and impacts of ChatGPT, a type of artificial intelligence, within the field of public finance. The study explores how ChatGPT can contribute to processes such as tax administration, audit procedures, big data analysis, and fiscal policy development. Including enhancing tax compliance, automating audits, and strengthening decision support systems, its functions are evaluated in detail. The study also addresses ethical concerns such as data privacy, bias, and the reliability of AI systems, offering solutions to manage these risks. The findings suggest that ChatGPT has significant potential to transform public finance operations and help governments use financial resources more efficiently. However, strong ethical frameworks and legal regulations must be developed to ensure the safe and fair use of AI technologies in public administration. The study emphasizes the need for future research to explore the application of ChatGPT in public finance more comprehensively.

Keywords

• ChatGPT
• Artificial Intelligence
• Tax Compliance

Kaynakça

1. Addington, S. (2023). Chatgpt: Cyber security threats and countermeasures. Available at SSRN 4425678.
2. Alice, S., Marzullo, A., Francesco, C., & Momi, E. D. (2020). Artificial intelligence for brain diseases: A systematic review. APL Bioengineering, 4. https://doi.org/10.1063/5.0011697
3. Aljanabi, M., Ghazi, M., Ali, A. H., & Abed, S. A. (2023). Chatgpt: Open possibilities. Iraqi Journal for Computer Science and Mathematics, 4(1), 62-64.
4. Baarez. (2025). Retrieved 11/02/2025 from https://www.baarez.com/tax-automation/
5. Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R. J., Esmaeili, M., Majdabadkohne, R. M., & Pasehvar, M. (2023). Chatgpt: Applications, opportunities, and threats. 2023 Systems and Information Engineering Design Symposium (SIEDS).
6. Bhattamisra, S. K., Banerjee, P., Gupta, P., Mayuren, J., Patra, S., & Candasamy, M. (2023). Artificial intelligence in pharmaceutical and healthcare research. Big Data and Cognitive Computing, 7(1), 10.
7. Bhattamisra, S., Priyanka, B., Pratibha, G., Jayashree, M., Patra, S., & Mayuren, C. (2023). Artificial intelligence in pharmaceutical and healthcare research. Big Data Cogn. Comput., 7, 10. https://doi.org/10.3390/bdcc7010010
8. Bielza, C., & Larrañaga, P. (2020). Data-driven computational neuroscience: Machine learning and statistical models. Cambridge University Press.

9. BoombergTax. (2025). Retrieved 11/02/2025 from https://pro.bloombergtax.com/products/ai-and-bloomberg-tax/
10. Carda, H., Bozdoğanoğlu, B., & Biyan, Ö. (2023). Vergi denetiminin dijitalleşmesinde yapay zekânin rolü ve mükellef haklarına olası etkileri. Maliye Araştırmaları-6, 84.
11. Chén, O. Y. (2019). The roles of statistics in human neuroscience. Brain Sciences, 9(8), 194.
12. Chen, R. H., & Chen, C. C. (2022). Artificial intelligence: An introduction for the inquisitive reader. Chapman and Hall/CRC.
13. Christian, J., Patrick, Z., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685-695. https://doi.org/10.1007/s12525-021-00475-2
14. Deng, J., & Lin, Y. (2022). The benefits and challenges of chatgpt: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81-83.
15. Derner, E., & Batistič, K. (2023). Beyond the safeguards: Exploring the security risks of chatgpt. arXiv preprint arXiv:2305.08005.
16. Dore, K. (2024). ‘Proceed with caution’ before tapping ai chatbots to file your tax return, experts warn. Retrieved 09/10/2024 from https://www.cnbc.com/2024/04/06/heres-what-to-know-before-using-ai-chatbots-to-file-your-taxes.html
17. Falade, P. V. (2024). Investigating the security and privacy issues in chatgpt usage and their impact on organisational and individual security. Int. J. Sci. Res. in Multidisciplinary Studies, 10(3).
18. Ferreira, F. G. D. C., Gandomi, A., & Cardoso, R. N. (2021). Artificial intelligence applied to stock market trading: A review. IEEE Access, 9, 30898-30917. https://doi.org/10.1109/ACCESS.2021.3058133
19. Financial Cents. (2024). Ai in accounting: How accountants and bookkeepers can use chatgpt in their firms. Retrieved 10/10/2024 from https://financial-cents.com/resources/articles/ai-in-accounting-how-accountants-and-bookkeepers-can-use-chatgpt-in-their-firms/
20. Garcia-Macia, D., Hanappi, T., Liu, L., & Minh, A. D. (2024). Broadening the gains from generative AI: The role of fiscal policies.
21. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
22. Halpern, M. (2006). The trouble with the turing test. The New Atlantis(11), 42-63.
23. Harada, Y., Suzuki, T., Harada, T., Sakamoto, T., Ishizuka, K., Miyagami, T., Kawamura, R., Kunitomo, K., Nagano, H., & Shimizu, T. (2024). Performance evaluation of chatgpt in detecting diagnostic errors and their contributing factors: An analysis of 545 case reports of diagnostic errors. BMJ Open Quality, 13(2), e002654.
24. Hassani, H., & Silva, E. S. (2023). The role of chatgpt in data science: How ai-assisted conversational interfaces are revolutionizing the field. Big Data and Cognitive Computing, 7(2), 62.
25. Ida Merete, E., Emmanouil, P., Patrick, M., & Krogstie, J. (2021). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24, 1709-1734. https://doi.org/10.1007/s10796-021-10186-w
26. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and vascular neurology, 2(4).
27. Jiang, Z. (2024). Transforming the finance industry in china with chatgpt. Frontiers in Business, Economics and Management, 13(1), 80-83. https://doi.org/10.54097/5p6fk846
28. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349, 255-260. https://doi.org/10.1126/science.aaa8415
29. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia tools and applications, 82(3), 3713-3744.
30. Ko, H., & Lee, J. (2023). Can chatgpt improve investment decision? From a portfolio management perspective. SSRN Electronic Journal.
31. Langley, P. (2004). Machine learning as an experimental science. Machine Learning, 3, 5-8. https://doi.org/10.1007/BF00115008
32. Lauriola, I., Lavelli, A., & Aiolli, F. (2022). An introduction to deep learning in natural language processing: Models, techniques, and tools. Neurocomputing, 470, 443-456.
33. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
34. Lee, M., Hayes, D., & Maher, C. (2024). AI as a budgeting tool: Panacea or pandora’s box?. Public Finance Journal, 1(1), 49–65. https://doi.org/10.59469/pfj.2024.6
35. Lee, M., Hayes, D., & Maher, C. (2024). Ai as a budgeting tool: Panacea or pandora’s box?. Public Finance Journal, 1(1), 49-65.
36. Liang, S. (2024). Opportunities and problems presented by chatgpt to the financial industry. Highlights in Business, Economics and Management, 24, 1284-1289. https://doi.org/10.54097/4jqmm245
37. Lopez‐Jimenez, F., Attia, Z., Adelaide, M. A.-O., Carter, R., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C., Medina-Inojosa, J., Noseworthy, P., Pellikka, P., Redfield, M., Roger, V., Sandhu, G., Conor, S., & Friedman, P. (2020). Artificial intelligence in cardiology: Present and future. Mayo Clinic Proceedings, 95 5, 1015-1039. https://doi.org/10.1016/j.mayocp.2020.01.038
38. Martinez, A. (2024). Unleashing the power of ai in public finance. Retrieved 11/02/2025 from https://blog-pfm.imf.org/en/pfmblog/2024/06/unleashing-the-power-of-ai-in-public-finance
39. McCarthy, J. (2007). What is artificial intelligence.
40. Mishra, R. K., Reddy, G. Y. S., & Pathak, H. (2021). The understanding of deep learning: A comprehensive review. Mathematical Problems in Engineering, 2021(1), 5548884. https://doi.org/https://doi.org/10.1155/2021/5548884
41. Mohammad, B., Turjana, S., Alzubaidi, M., Shah, H., Alam, T., Zubair, S., & Mowafa, J. H. (2023). The pros and cons of using chatgpt in medical education: A scoping review. Studies in health technology and informatics, 305, 644-647. https://doi.org/10.3233/SHTI230580
42. Morandín-Ahuerma, F. (2022). What is artificial intelligence?. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.2022.31261
43. Nafea, I. (2018). Machine learning in educational technology. Machine Learning-Advanced Techniques and Emerging Applications. https://doi.org/10.5772/INTECHOPEN.72906
44. Nazir, A., & Wang, Z. (2023). A comprehensive survey of chatgpt: Advancements, applications, prospects, and challenges. Meta-Radiology, 1(2), 100022. https://doi.org/https://doi.org/10.1016/j.metrad.2023.100022
45. Numiro. Retrieved 11/02/2025 from https://www.numiro.ai/
46. OECD. (2024). Using artificial intelligence in public financial management. Retrieved 11/02/2025 from https://one.oecd.org/document/GOV/SBO(2024)14/en/pdf
47. OECD/UNESCO. (2024). G7 toolkit for artificial intelligence in the public sector. O. Publsihing.
48. OpenAI. (2023). Gpt-4 technical report. Retrieved 10/09/2024 from https://cdn.openai.com/papers/gpt-4.pdf
49. Otter, D. W., Medina, J. R., & Kalita, J. K. (2020). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 604-624.
50. Panagopoulou, F., Parpoula, C., & Karpouzis, K. (2023). Legal and ethical considerations regarding the use of chatgpt in education. arXiv preprint arXiv:2306.10037.
51. Pongsakorn, L., Tanpat, K., Kris, J., & Yarnaphat, S. (2023). Applying chatgpt as a new business strategy: A great power comes with great responsibility. Corporate and Business Strategy Review. https://doi.org/10.22495/cbsrv4i4siart2
52. Qu, Y., Bai, B., & Zhang, Z. (2023). The new generation of artificial intelligence technology chatgpt causes: Potential legal risks and regulatory countermeasures. 2023 8th International Conference on Computer and Communication Systems (ICCCS).
53. Rice, S., Crouse, S. R., Winter, S. R., & Rice, C. (2024). The advantages and limitations of using chatgpt to enhance technological research. Technology in Society, 76, 102426.
54. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
55. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229.
56. Segato, A., Marzullo, A., Calimeri, F., & De Momi, E. (2020). Artificial intelligence for brain diseases: A systematic review. APL Bioengineering, 4(4).
57. Silva, A. d. O., & Janes, D. d. S. (2022). The emergence of chatgpt and its implications for education and academic research in the 21st century. Review of Artificial Intelligence in Education, 3, e6. https://doi.org/10.37497/rev.artif.intell.educ.v3i00.6
58. Som, S. B. (2023). The function of chat gpt in social media: According to chat gpt. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4405389
59. Srivastav, S., Chandrakar, R., Gupta, S., Babhulkar, V., Agrawal, S., Jaiswal, A., Prasad, R., & Wanjari, M. B. (2023). Chatgpt in radiology: The advantages and limitations of artificial intelligence for medical imaging diagnosis. Cureus, 15(7).
60. Su, S.-Y., Huang, C.-W., & Chen, Y.-N. (2020). Towards unsupervised language understanding and generation by joint dual learning. arXiv preprint arXiv:2004.14710.
61. Tarca, A., Carey, V., Xue-wen, C., Romero, R., & Drăghici, S. (2007). Machine learning and its applications to biology. PLoS Computational Biology, 3. https://doi.org/10.1371/journal.pcbi.0030116
62. Tarran, B. (2023). Real talk? The future of chatgpt. Significance, 20(2), 4-5.
63. TaxGPT. (2024). Ai tax co-pilot for accountants and tax professionals. Retrieved 01/10/2024 from https://www.taxgpt.com/taxgpt-insights/taxgpt-use-cases-for-financial-advisors
64. Tecuci, G. (2012). Artificial intelligence. Wiley Interdisciplinary Reviews: Computational Statistics, 4. https://doi.org/10.1002/wics.200
65. ThomsonReuters. (2025). Retrieved 11/02/2025 from https://www.thomsonreuters.com/en
66. Tipalti. (2024). 12 best ways to use chatgpt in accounting. Retrieved 11/10/2024 from https://tipalti.com/accounting-hub/chatgpt-accounting/
67. TraceCore. (2025). Retrieved 11/02/2025 from https://tracecore.solutions/blog/how-to-transform-tax-compliance-using-ai-powered-tax-software
68. Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433
69. Umer, H., & Khan, M. S. (2023). Chatgpt in finance: Addressing ethical challenges. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4433171
70. Valencia, O., & Diaz, J. C. (2024). Leveraging ai to transform macroeconomic and fiscal policymaking in latin america and the caribbean. Retrieved 11/02/2025 from https://blogs.iadb.org/gestion-fiscal/en/ai-to-transform-macroeconomic-and-fiscal-policymaking/
71. WB. (2021). Artificial intelligence in the public sector. Retrieved 11/02/2025 from https://documents1.worldbank.org/curated/en/746721616045333426/pdf/Artificial-Intelligence-in-the-Public-Sector-Summary-Note.pdf
72. Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A brief overview of chatgpt: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
73. Zaremba, A., & Demir, E. (2023). Chatgpt: Unlocking the future of nlp in finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4323643
74. Zhao, J., & Wang, X. (2023). Unleashing efficiency and insights: Exploring the potential applications and challenges of chatgpt in accounting. Journal of Corporate Accounting & Finance, 35(1), 269-276. https://doi.org/10.1002/jcaf.22663
75. Zhou, W. (2023). Chatgpt legal risk and regulation study. Journal of Education, Humanities and Social Sciences, 19, 104-106.