İNSAN FAKTÖRÜ VE MAKİNE : YAPAY ZEKÂ İÇ DENETÇİLERİN YERİNİ ALABİLİR Mİ?

Yıl 2025, Cilt: 7 Sayı: 1, 11 - 52, 08.09.2025

Özden Şentürk

Öz

Bu çalışma, yapay zekâ teknolojilerinin iç denetim alanında meydana getirdiği paradigma değişimini kapsamlı bir şekilde inceleyerek, söz konusu teknolojilerin iç denetim süreçlerine olan etkilerini avantajlar ve sınırlılıklar bağlamında ele almaktadır. Çalışmada öncelikli olarak, büyük veri analitiği, makine öğrenmesi ve doğal dil işleme gibi gelişmiş teknolojilerin iç denetim süreçleriyle entegrasyonu üzerinde durulmaktadır. Bu teknolojiler sayesinde, özellikle veri analizi, risk değerlendirme, dolandırıcılık tespiti ve gerçek zamanlı izleme süreçlerinde otomasyonun sağlanarak verimliliğin artırıldığı ve hata oranlarının belirgin şekilde azaltıldığı tespit edilmiştir.
Bununla birlikte, çalışmada yapay zekâ teknolojilerinin mevcut kapasitelerinin bazı kritik insani yetkinlikleri karşılamakta yetersiz kaldığı belirtilmektedir. Özellikle etik değerlendirme, bağlamsal analiz, yaratıcı problem çözme ve stratejik muhakeme gibi insana özgü niteliklerin yapay zekâ tarafından tam olarak yerine getirilemediği görülmektedir. Bu bağlamda, yapay zekânın iç denetim fonksiyonlarını tamamen üstlenmesi mümkün olmayıp, daha çok denetçilerin karar alma süreçlerini destekleyici bir araç olarak kullanılmasının daha etkili sonuçlar doğuracağı sonucuna varılmaktadır.
Sonuç olarak çalışma, iç denetimde sürdürülebilir ve güvenilir bir gelecek için makinelerin otomasyon gücü ile insanın etik, sezgisel ve bağlamsal yetkinliklerini birleştiren hibrit bir yaklaşımın benimsenmesinin önemini vurgulamaktadır. Elbette yapay zekâ, iç denetim süreçlerindeki rutin ve tekrar eden işleri otomatikleştirebilir, ancak etik değerlendirmeler, sezgisel analiz ve karmaşık bağlamsal kararlar için insan muhakemesi kaçınılmazdır. Dolayısıyla iç denetçiler, insan faktörünün kritik önem taşıdığı alanlarda yapay zekânın tamamlayıcısı olmaya devam edecek ve vazgeçilmezliklerini koruyacaktır.

Anahtar Kelimeler

Yapay Zeka , İç Denetim , Etik

HUMAN FACTOR AND MACHINE: CAN ARTIFICIAL INTELLIGENCE REPLACE INTERNAL AUDITORS?

Öz

This study comprehensively examines the paradigm shift brought about by artificial intelligence technologies in the field of internal auditing, addressing both the advantages and limitations of their impact on internal audit processes. The study primarily focuses on the integration of advanced technologies such as big data analytics, machine learning, and natural language processing into internal auditing practices. It identifies significant improvements in efficiency through automation in processes such as data analytics, risk assessment, fraud detection, and real-time monitoring, resulting in reduced error rates and enhanced effectiveness.However, the study emphasizes certain limitations regarding artificial intelligence technologies, noting their inadequacies in replicating critical human competencies. Specifically, skills such as ethical judgment, contextual analysis, creative problem-solving, and strategic reasoning remain uniquely human and are not fully replaceable by AI. Consequently, the study concludes that artificial intelligence should not entirely replace internal auditors but rather serve as a supportive tool to enhance their decision-making processes.
In conclusion, the research underscores the importance of adopting a hybrid approach for a sustainable and reliable future in internal auditing, integrating the automation capabilities of AI with human ethical judgment, intuition, and contextual insights. Of course, AI can automate routine and repetitive tasks in internal audit processes, but human judgement is inevitable for ethical assessments, intuitive analysis and complex contextual decisions. Therefore, internal auditors will continue to complement AI in areas where the human factor is critical and remain indispensable.

Anahtar Kelimeler

Artificial Intelligence , Internal Auditing , ethic

Kaynakça

• Adelakun, B. O. (2022). The Impact of AI on Internal Auditing: Transforming Practices and Ensuring Compliance. Finance & Accounting Research Journal, 4(6), 350-370.
• Ağdeniz, Ş. (2024). Güvenilir Yapay Zekâ ve İç Denetim. Denetişim (29), 112-126. https://doi.org/10.58348/denetisim.1384391
• Al-sagheer, N. H. A., & Bacha, S. (2024). Internal audit, strategic risks and corporate financial distress. Journal of Ecohumanism, 3(4), 1847-1869.
• Aldemir, C., & Uçma Uysal, T. (2024). AI competencies for internal auditors in the public sector. Edpacs, 69(1), 3-21.
• 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, 6(3), 1-40.
• Altundağ, S. (2024). Artificial intelligence-based audit software: Today’s realities and future vision. Denetişim (31), 180-197.
• Amponsah, I. A., & Ali, M. O. (2023). Examining the Influence of Expert Systems and Decision Support Systems on Auditing. Albukhary Social Business Journal, 47-61.
• Aysan, H., & Fırat, Z. (2023). Yapay Zekâ Uygulamaları İç Denetim Mesleğine Neler Kazandırabilir? Kamu Denetçiliği Kurumu Yayını.
• Barros, C., & Marques, R. P. (2022). Continuous Assurance for the Digital Transformation of internal auditing. Journal of Information Systems Engineering and Management, 7(1).
• Bello, O. A., & Olufemi, K. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer science & IT research journal, 5(6), 1505-1520.
• Bozkurt, A. (2023). ChatGPT, Üretken Yapay Zekâ ve Algoritmik Paradigma Değişikliği. Alanyazın, 4(1), 63-72. https://doi.org/10.59320/alanyazin.1283282
• Bozkuş Kahyaoğlu, S., & Aksoy, Y. (2023). Denetimde Dijital Dönüşüm ve Yapay Zekâ. Sayıştay Başkanlığı Yayını.
• Chelkovan, V. (2024). The Use of Artificial Intelligence Methods in the Intelligent Decision Support System. Electronics and Control Systems, 1(79), 16-21.
• Chen, N., Hu, M., & Li, W. (2022). Algorithmic decision-making safeguarded by human knowledge. arXiv preprint arXiv:2211.11028.
• Chen, Y., & Ye, H. (2022). Integrating machine learning algorithms into audit processes: Benefits and challenges. International Journal of Auditing, 26(3), 312-327. https://doi.org/10.1111/ijau.12298
• Chen, Z., & Ye, R. (2022). Principles of Creative Problem Solving in AI Systems: Ana-Maria Oltețeanu: Cognition and Creative Machine: Cognitive AI for Creative Problem Solving. Freie Universität Berlin, Springer. https://doi.org/10.1007/978–3-030–30322-8
• Couceiro, B., Pedrosa, I., & Marini, A. (2020, June). State of the art of artificial intelligence in internal audit context. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-7). IEEE.
• Couceiro, M. S., Dias, L. A., Mendes, M. F., & Araújo, M. (2020). State of the art of artificial intelligence in internal audit context. Journal of Applied Accounting Research, 21(3), 373-394. https://doi.org/10.1108/JAAR-04-2020-0060
• Cui, C., Amiri, S., Ding, Y., Zhan, X., & Zhang, S. (2023, July). Learning to reason about contextual knowledge for planning under uncertainty. In Uncertainty in Artificial Intelligence (pp. 465-475). PMLR.
• Çelebi, V., & İnal, A. (2019). Yapay zekâ bağlamında etik problemi. Journal of International Social Research, 12(66).
• Dambe, S., Gochhait, S., & Ray, S. (2023, November). The Role of Artificial Intelligence in Enhancing Cybersecurity and Internal Audit. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 88-93). IEEE.
• Deliu, D. (2020). Elevating professional reasoning in auditing. Psycho-professional factors affecting auditor’s professional judgement and skepticism. Journal of Accounting and Auditing: Research and Practice, 20(2), 46-80.
• Deliu, D. (2024). Professional Judgment and Skepticism Amidst the Interaction of Artificial Intelligence and Human Intelligence. The Audit Financiar journal, 22(176), 724-741.
• Dünya Ekonomik Forumu. (2023). İşlerin Geleceği Raporu 2023.
• Demirbaş, D., & Dağlıoğlu, S. A. (2022). Yapay Zekâ Destekli Denetim Uygulamaları ve Etik Sınamalar. Muhasebe ve Finansman Dergisi, 95, 153-170.
• Efe, A., & Tunçbilek, M. (2023). Yapay zekâ algoritmaları ile dönüşen denetim araçları üzerine bir değerlendirme. Denetişim (27), 72-102. https://doi.org/10.58348/denetisim.1195294
• Erdoğan, E. (2019). Yapay Zekâ ve İç Denetim: Etik ve İnsan Faktörü. Kamu Denetçiliği Kurumu Yayını.
• Erişen, O., & Erer, M. (2023). Exploring the impacts of digitalization on the internal audit profession. Journal of Research in Business, 8(1), 171-190.
• Falcao Filho, H. A. (2024). Making sense of negotiation and AI: The blossoming of a new collaboration. International Journal of Commerce and Contracting, 8(1-2), 44-64.
• Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2022). On the effect of information asymmetry in human-AI teams. arXiv preprint arXiv:2205.01467.
• Henry, H., & Rafique, M. (2021). Impact of artificial intelligence (AI) on auditors: a thematic analysis. IOSR Journal of Business and Management, 23(9).
• Hilario, M., Paredes, P., Mayhuasca, J., Liendo, M., & Martínez, S. (2024). Evaluation of the Impact of Artificial Intelligence on the Systems Audit Process. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. https://doi.org/10.58346/jowua.2024.i3.013
• Holmes, W., Persson, J., Chounta, I.-A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and Education. A critical view through the lens of human rights, democracy, and the rule of law. Council of Europe. https://rm.coe.int/artificial-intelligence-andeducation-a-critical-view-through-the-lens/1680a886bd
• Ikhsan, W. M., Abdullah, Z., & Rahayu, S. (2022). Financial fraud detection using machine learning algorithms. Jurnal Riset Akuntansi dan Bisnis Airlangga, 7(2), 103–119. https://doi.org/10.31093/jraba.v7i2.103
• Ikhsan, W. M., Ednoer, E. H., Kridantika, W. S., & Firmansyah, A. (2022). Fraud detection automation through data analytics and artificial intelligence. Riset: Jurnal Aplikasi Ekonomi Akuntansi dan Bisnis, 4(2), 103-119.
• Ilmawan, F. N., & Bawono, I. R. (2024). Challenges and opportunities of digital auditing: A look beyond the year 2020. Jurnal Magister Akuntansi Trisakti, 11(2), 93-110.
• Ivakhnenkov, S. (2023). Artificial intelligence application in auditing. Scientific Papers.
• Khinvasara, T., Shankar, A., & Wong, C. (2024). Survey of Artificial Intelligence for Automated Regulatory Compliance Tracking. Journal of Engineering Research and Reports, 26(7), 390-406.
• Köse, C., & Şimşek, M. (2022). Yapay Zekâ Tabanlı Sistemlerin İç Denetimde Kullanımı: Fırsatlar ve Riskler. İşletme Araştırmaları Dergisi, 14(1), 515-530.
• Köse, U. (2018). Türkiye Yapay Zekâda Kullanıcı Değil Geliştirici. Süleyman Demirel Üniversitesi Bülteni.
• Leal, R. P., Gutiérrez, L. J., & Pérez, A. (2024). Ethical implications of artificial intelligence in auditing: A professional judgment perspective. Journal of Business Ethics, 193(1), 87-104. https://doi.org/10.1007/s10551-022-05148-7
• Leal, T. D. Z., & Macfarland, C. A. G. (2024). La responsabilidad civil y la ética en la inteligencia artificial: una revisión sistemática de las ideas del período 2018-2023. IUSTA, (60), 66-93.
• Lidiana, L. (2024). AI and auditing: enhancing audit efficiency and effectiveness with artificial intelligence. Accounting studies and tax journal (count), 1(3), 214-223.
• Lidiana, R. (2024). Enhancing internal audit efficiency through big data analytics and machine learning. International Journal of Data Science and Analytics in Audit, 5(1), 12-29. https://doi.org/10.1007/s41060-024-00375-8
• Macailao, M. C. (2020). Strategic approaches of internal auditors on occupational fraud. Journal of Critical Reviews, 7(11), 21-25.
• Mallela, I., Aravind, S., Tharan, O., Goel, P., Pal, S., & , S. (2020). Explainable AI for Compliance and Regulatory Models. International Journal for Research Publication and Seminar. https://doi.org/10.36676/jrps.v11.i4.1584
• McKinsey & Company. (2020). İşimizin Geleceği: Türkiye’de Dijitalleşme, Otomasyon ve İş Gücü Dönüşümü. McKinsey Türkiye Raporu.
• Mulyana, H., & Pasundan, U. (2023). Ethics and Law for the Use and Development of Artificial Intelligence Technology. East Asian Journal of Multidisciplinary Research. https://doi.org/10.55927/eajmr.v2i11.2947
• Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. https://doi.org/10.1007/s10551-019-04407-1
• Muslihatun, F. A. N., Hantono, B. S., & Fauziati, S. (2021, November). Using Artificial Intelligence Technology for Decision Support System in Audit Risk Assessment: A Review Paper. In 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 326-331). IEEE.
• Onwubuariri, E. R., Adelakun, B. O., Olaiya, O. P., & Ziorklui, J. E. K. (2024). AI-Driven risk assessment: Revolutionizing audit planning and execution. Finance & Accounting Research Journal, 6(6), 1069-1090.
• Özerbaş, M. A., & Kuralbayeva, A. (2018). Türkiye ve Kazakistan öğretmen adaylarının dijital okuryazarlık düzeylerinin incelenmesi. Muğla Sıtkı Koçman Üniversitesi Eğitim Fakültesi Dergisi, 16-25.
• Öztürk, H., & Dündar, S. (2021). İç Denetim Süreçlerinde Dijitalleşme ve Yapay Zekâ Kullanımı: Bir Literatür Taraması. Muhasebe Bilim Dünyası Dergisi, 23(2), 25-42.
• Pehlivanova, P. (2020). The Significance of Rationality in Reforming Ethics within Contemporary Professional Work. Business & Professional Ethics Journal, 39(1), 43-75.
• Polat, M. (2024). Yapay Zekânın Denetimde Kullanılması ve Etik Sorunlar. Sayıştay Dergisi, 35(134), 395-423.
• Ramos, S., Perez-Lopez, J., & Abreu, R. (2024). Bibliometric analysis of artificial intelligence trends in auditing and fraud detection. Corporate Governance and Organizational Behavior Review. https://doi.org/10.22495/cgobrv8i2sip8
• Rikhardsson, P., Kristinn, T., Bergthorsson, G., & Batt, C. (2022). Artificial intelligence and auditing in small-and medium-sized firms: Expectations and applications. AI Magazine, 43(3), 323-336.
• Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach (3rd Ed). Prentice Hall.
• Schleith, J., Norkute, M., Mikhail, M., & Tsar, D. (2022, June). Cognitive strategy prompts: Creativity triggers for human centered ai opportunity detection. In Proceedings of the 14th Conference on Creativity and Cognition (pp. 29-37).
• Seelaboyina, R., Gudipelly, M. R., & Vishnu, M. S. S. (2023, October). Humanizing AI: The Importance of Effective Communication Skills in the Age of Automation. In 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) (pp. 368-372). IEEE.
• Shivram, V. (2024). Auditing with AI: A Theoretical Framework for Applying Machine Learning Across the Internal Audit Lifecycle.
• Sun, Q., Chen, G., & He, S. (2022, October). Enterprise Development Decision Support System Based on Artificial Intelligence Technology. In 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs) (pp. 245-249). IEEE.
• Supriadi, I. (2024). The audit revolution: Integrating artificial intelligence in detecting accounting fraud. Akuntansi dan Teknologi Informasi, 17(1), 48-61.
• Supriadi, Y. (2024). AI and auditing: Enhancing audit efficiency and effectiveness with artificial intelligence. Journal of Emerging Technologies in Accounting, 21(1), 45-62. https://doi.org/10.2308/JETA-2024-007
• Talat, Z., Blix, H., Valvoda, J., Ganesh, M. I., Cotterell, R., & Williams, A. (2022, July). On the machine learning of ethical judgments from natural language. In 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 769-779). Association for Computational Linguistics.
• UNICEF. (2021). Policy guidance on AI for children. Paris: UNICEF. https://www.unicef.org/globalinsight/reports/policy-guidance-ai-children
• Westberg, M., & Framling, K. (2021). Cognitive perspectives on context-based decisions and explanations. arXiv preprint arXiv:2101.10179.
• Wheelen, T. L., & Hunger, J. D. (2017). Strategic Management and Business Policy: Globalization, Innovation, and Sustainability. Pearson Education.
• Ye, W., Bullo, F., Friedkin, N., & Singh, A. K. (2022). Modeling human-ai team decision making. arXiv preprint arXiv:2201.02759.
• Yontar, A. (2019). Öğretmen adaylarının dijital okuryazarlık düzeyleri. Ana Dili Eğitimi Dergisi, 7(4), 815-824.