The Future of the Audit Related Professions in the Light of Risk and Benefits of Artificial Intelligence Advancements

Öz

The study analyzes the potential impacts of super AI on traditional auditing and investigative practices, ethical considerations, and the role of auditors and investigators in the face of increasing automation and dependence on AI systems. The study uses a deductive approach to analyze data collected from secondary sources, including academic journals, business reports, and government publications Using literature knowledge and business reports, it is tried to make analysis and assessments on the risks and benefits of usage of super AI over audit and investigating professionals. The study identifies several risks and problems associated with super AI, including automation of auditing and investigation processes, dependence on AI systems, ethical considerations, lack of technical expertise, and security concerns. The study concludes that while AI can provide significant benefits to the audit-related professions, there are also critical risks that need to be addressed to ensure ethical and effective use of AI in auditing practices. It is important for these professions to carefully assess the benefits and risks of AI and take appropriate measures to minimize the risks and maximize the benefits. With a problem-solving approach, it is aimed to produce applicable suggestions for audit authorities and professional in Türkiye to help provide reasonable mitigating of its upcoming risks to reasonable level and increase its benefits to the upmost. The study suggests various policies, strategies, and projects for improvement, requiring a concerted effort from all stakeholders.

Anahtar Kelimeler

• AI
• Audit
• Investigation
• Inspection
• Process Automation
• Machine Learning
• Smart MIS.

Yapay Zekâ Ilerlemelerinin Riskleri Ve Faydaları Işığında Denetimle Ilgili Mesleklerin Geleceği

Öz

Bu çalışma, süper yapay zekânın (YZ) geleneksel denetim ve soruşturma uygulamaları, etik hususlar ve artan otomasyon ve YZ sistemlerine bağımlılık karşısında denetçilerin ve araştırmacıların rolü üzerindeki potansiyel etkilerini analiz etmektedir. Çalışmada, akademik dergiler, iş raporları ve hükümet yayınları dâhil olmak üzere ikincil kaynaklardan toplanan verileri analiz etmek için tümdengelimli bir yaklaşım kullanarak süper YZ kullanımının denetim ve soruşturma profesyonelleri üzerindeki riskleri ve faydaları hakkında analizler ve değerlendirmeler yapılmaktadır. Çalışma, denetim ve soruşturma süreçlerinin otomasyonu, YZ sistemlerine bağımlılık, etik hususlar, teknik uzmanlık eksikliği ve güvenlik endişeleri konusunda süper YZ ile ilişkili çeşitli riskleri ve sorunları tartışmaktadır. Çalışma, YZ'nin denetimle ilgili mesleklere önemli faydalar sağlayabilse de denetim uygulamalarında etik ve etkili kullanımını sağlamak için ele alınması gereken bazı kritik riskler olduğu sonucuna varmaktadır. Bu meslekler için YZ’nin faydalarını ve risklerini dikkatli bir şekilde değerlendirmek ve riskleri en aza indirmek ve faydaları en üst düzeye çıkarmak için uygun önlemleri almak gerekir. Sorun çözücü bir yaklaşımla, Türkiye'deki denetim otoriteleri ve uzmanları için uygulanabilir öneriler üreterek, yaklaşmakta olan risklerin makul bir şekilde yönetilmesine ve faydalarını en üst düzeye çıkarmaya yardımcı olunmaktadır. Çalışma, iyileştirme için tüm paydaşların ortak çabasını gerektiren çeşitli politikalar, stratejiler ve projeler önermektedir.

Anahtar Kelimeler

• YZ
• Denetim
• Soruşturma
• Teftiş
• Süreç Otomasyonu
• Makine Öğrenimi
• Akıllı YBS.

Kaynakça

1. Accenture. (2016). Artificial Intelligence is a Growth Engine for the World Economy. Retrieved from https://www.accenture.com/us-en/insights/strategy/artificial-intelligence-growth-engine-world-economy.
2. AEye. (n.d.). About AEye. Retrieved June 3, 2021, from https://www.aeye.ai/.
3. Allied Market Research. (2019). Artificial Intelligence Market by Component, Technology, and Application - Global Opportunity Analysis and Industry Forecast, 2018-2025. Retrieved from https://www.alliedmarketresearch.com/artificial-intelligence-market.
4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
5. Bryson, J. J. (2018). Future-proofing governance: Creating a framework to regulate artificial intelligence. Philosophy & Technology, 31(4), 557-579.
6. Bundesrechnungshof. (2020). AI in auditing: Opportunities and challenges. Retrieved from https://www.bundesrechnungshof.de/EN/Themen/Verwaltungsmodernisierung/Artificial-Intelligence/Artificial-Intelligence_node.html.
7. Cosmides, L., & Tooby, J. (2000). Evolutionary psychology and the emotions. In M. Lewis & J. Haviland-Jones (Eds.), Handbook of emotions (pp. 91-115). New York: Guilford Press.
8. de la Torre, J. (2021). The Future of Auditing: How Artificial Intelligence is Transforming the Profession. Harvard Business Review. https://hbr.org/2021/06/the-future-of-auditing-how-artificial-intelligence-is-transforming-the-profession.

9. Deloitte. (2018). The future of internal audit: The impact of technology. Deloitte Insights.
10. Deloitte. (2019). The future of the audit in a world of artificial intelligence. https://www2.deloitte.com/uk/en/pages/audit/articles/the-future-of-the-audit-in-a-world-of-artificial-intelligence.html.
11. Efe A., (2021). Yapay zeka risklerinin etik yönünden değerlendirilmesi. Bilgi ve İletişim Teknolojileri Dergisi, 3(1), 1-24.
12. Efe, A., & Isık, A. (2020). A general view of industry 4.0 revolution from cybersecurity perspective. International Journal of Intelligent Systems and Applications in Engineering, 8(1), 11-20.
13. Efe, A. & Tunçbilek, M. (2023). Yapay Zekâ Algoritmalari ile Dönüşen Denetim Araçlari Üzerine Bir Değerlendirme . Denetişim, 0 (27), 72-102. DOI: 10.58348/denetisim.1195294
14. Enderlein, H. (2020). The Impact of Artificial Intelligence on Auditing. Journal of Accounting and Taxation. https://www.sciencedirect.com/science/article/pii/S2212567120300454.
15. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Luetge, C. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
16. Fodor, J. A. (1980). Methodological solipsism considered as a research strategy in cognitive psychology. Behavioral and Brain Sciences, 3(1), 63-73.
17. Frey, C. B., & Osborne, M. A. (2017). The Future of Employment: How Susceptible are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254-280.
18. Gartner. (2021). Gartner Forecasts Worldwide Artificial Intelligence Market to Grow 17.5% in 2021. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2021-03-08-gartner-forecasts-worldwide-artificial-intelligence-market-to-grow-17-5-percent-in-2021.
19. Gibbs, S. (2018). The impact of artificial intelligence on the workplace. Wired, 26(10), 92-96.
20. Goodman, B., & Flaxman, S. (2016). European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine, 38(3), 50-57.
21. HizliAktarim. (n.d.). About HizliAktarim. Retrieved June 3, 2021, from https://www.hizliaktarim.com/.
22. Institute of Internal Auditors. (2019). Artificial intelligence: A potential game changer for internal audit. The IIA Research Foundation.
23. Isaak, J., & Hanna, M. J. (2018). User acceptance of computer technology: A comparison of two theoretical models. Management Research Review, 41(6), 667-680.
24. KPMG. (2019). Artificial intelligence in auditing. KPMG International.
25. Kshetri, N. (2018). Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organisations. Journal of Management Analytics, 5(4), 247–265. https://doi.org/10.1080/23270012.2018.1527875.
26. Kshetri, N. (2020). Ethics of artificial intelligence. Journal of Business Research, 112, 556-566.
27. Lu, W., & Ye, L. (2021). The effect of artificial intelligence on auditing: A systematic literature review. Journal of International Accounting, Auditing and Taxation, 42, 100365.
28. Ministry of Industry and Technology. (2019). Artificial Intelligence in Turkey. Retrieved from https://www.sanayi.gov.tr/en/ai-in-turkey.
29. Nauto. (n.d.). About Nauto. Retrieved June 3, 2021, from https://nauto.com/.
30. Öğütücü H. (2021) Türkiye Yapay Zeka İnisiyatifi Girişimler Haritası’nın Ekim 2021 versiyonunda yer alan 208 girişim, E-girişim, https://egirisim.com/2021/10/26/turkiye-yapay-zeka-inisiyatifi-girisimler-haritasinin-ekim-2021-versiyonunda-yer-alan-208-girisim/.
31. OtoSense. (n.d.). Our Solution. Retrieved June 3, 2021, from https://otosense.com/.
32. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.
33. PwC. (2018). Artificial Intelligence in Audit. Retrieved from https://www.pwc.com/gx/en/services/audit/insights/artificial-intelligence-in-audit.html.
34. PWC. (2021). The future of audit: How technology, including AI, is changing the audit profession. Retrieved from https://www.pwc.com/gx/en/audit-services/future-of-audit.html.
35. SEC. (2019). SEC uses technology to enhance market surveillance and enforce the law. Retrieved from https://www.sec.gov/news/press-release/2019-144.
36. Vatsana. (n.d.). About Vatsana. Retrieved June 3, 2021, from https://vatsana.com/.
37. Waldner, M. (2019). Turkey's AI ecosystem: challenges and opportunities. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2019/11/turkeys-ai-ecosystem-challenges-and-opportunities/.
38. Yampolskiy, R. V. (2018). Artificial intelligence safety and cybersecurity: a timeline of AI failures. Journal of Cybersecurity, 4(1), 1-12. arXiv preprint arXiv:1806.02193.
39. Zendesk. (2020). The State of Chatbots 2020. Retrieved from https://www.zendesk.com/resources/the-state-of-chatbots-2020/.