YAPAY ZEKÂ ALGORİTMALARI İLE DÖNÜŞEN DENETİM ARAÇLARI ÜZERİNE BİR DEĞERLENDİRME

Yıl 2023, , 72 - 102, 31.01.2023

Ahmet Efe , Merve Tunçbilek

https://doi.org/10.58348/denetisim.1195294

Cited By: 6

Öz

Yapay zekâ (YZ) uygulamalarıyla birlikte gelişen yenilikçi teknolojiler nedeniyle Sayıştay ve teftiş mekanizmaları dahil olmak üzere tüm iç ve dış denetim mesleğinin bir bütün olarak bir zorluk ile karşı karşıya olduğu söylenebilir. Bu zorlukların bir kısmı yeni fırsatlarla birlikte üstesinden gelinmesi gereken engelleri ve riskleri barındırabilmektedir. Veri analitiği araç ve tekniklerini denetim otomasyon yazılımlarıyla birlikte kullanmak dahil olmak üzere iç denetimde teknolojiden yararlanmada daha etkin ve verimli bir iş yapma ihtiyacı vardır. Büyük veri zorluğundan dolayı yavaş işleyen denetim, örneklemeye dayalı denetim planlamasına dayanan denetimin maliyet ve risklerinin artması denetim görevlerini hızlandırmak için otomasyonun gerekli olduğunun birer göstergeleridirler. Bu çalışma, YZ ile birlikte gelişen riskleri ve fırsatları dengeleyecek şekilde denetim süreçleri bağlamındaki otomasyon çözümlerini incelemektedir. Genel olarak piyasada kullanılan akıllı denetim uygulamaları ve özelde ise AuditMap.ai örneği üzerinden YZ tabanlı denetim otomasyon uygulamalarının denetçinin yerine geçerek değil, aslında insan merkezli denetim planlama, programlama, yürütme, test, raporlama ve izleme süreçlerine değer katarak denetim sürecine yardımcı olunduğu ortaya konulmaktadır.

Anahtar Kelimeler

Yapay Zekâ, İç Denetim, Denetim Otomasyon Sistemleri, e-Denetim

Kaynakça

• 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.
• Appelbaum, D.A., Kogan, A. & Vasarhelyi, M.A. (2018). Analytical procedures in external auditing: A comprehensive literature survey and framework for external audit analytics. Journal of Accounting Literature 40, 83–101.
• Atakan, M. (2021). Siber güvenlik ve COVID 19 salgının uzaktan denetim üzerinde etkileri. Denetişim, (22), 27-39. https://dergipark.org.tr/tr/ pub/denetisim/issue/60158/758709.
• Bazı Cumhurbaşkanlığı Kararnamelerinde Değişiklik Yapılması Hakkında Cumhurbaşkanlığı Kararnamesi (2021). T.C. Resmi Gazete (rega.gov.tr). Tarih/Sayı: 14.07.2021/31541.
• Bhattacharya, U. & Rahut, A., De, S. (2013). Audit maturity model. Computer Science Information Technology 4.
• Blackline (2019). Mistrust In The Numbers, BlackLine Study into the Potential Global Scale of Financial Data Inaccuracies, https://www.blackline.com/assets/docs/uploads/Mistrust_in_the_Numbers_Feb_2019.pdf. adresinden alındı.
• Boskou, G., Kirkos, E. & Spathis, C. (2018). Assessing internal audit with text mining. Journal of Information & Knowledge Management 17(02) 1850020.
• Bowen, P., Hash, J. & Wilson, M. (2007). Information security handbook: a guide for managers. In: NIST Special Publication 800–100, National Institute of Standards and Technology.
• Boxwala, A.A., Kim, J., Grillo, J.M. & Ohno-Machado, L. (2011). Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal of the American Medical Informatics Association 18(4) 498–505.
• Brennan B, Baccala M., Flynn M., (2017). Artificial Intelligence Comes to Financial Statement Audits, CFO.com, Feb. 2, https://bit.ly/2Jx3CYO. adresinden alındı.
• CFR, (1996). United States Public Law: Quality System Regulation. 21 CFR part 820.
• CFR, (2011). United States Public Law: Prospectus summary, risk factors, and ratio of earnings to fixed charges (Item 503). 17 CFR part 229.503.
• COSO, (2013). Committee of Sponsoring Organizations of the Treadway Commission and others: Internal Control — Integrated Framework.
• Cowle, E. N., & Rowe, S. P. (2019). Don't make me look bad: How the audit market penalizes auditors for doing their job. https://ssrn.com /abstract=3228321 adresinden alındı.
• Devlin, J., Chang, M., Lee, K. & Toutanova, K. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805.
• Endler, D. (1998). Intrusion detection. applying machine learning to Solaris audit data. In: Proceedings 14th Annual Computer Security Applications Conference (Cat.No98EX217), IEEE 268–279.
• Eulerich, M. & Masli, A. (2019). The use of technology-based audit techniques in the internal audit function–is there an improvement in efficiency and effectiveness? Available at SSRN 3444119.
• Fan, J., Cohen, K., Shekhtman, L.M., Liu, S., Meng, J., Louzoun, Y. & Havlin, S. (2018). A combined network and machine learning approaches for product market forecasting. arXiv preprint arXiv:1811.10273. Fathi, E., (2020). AI in finance: Helping professionals shift from hindsight to insight to foresight, Mind Bridge, https://www.mindbridge.ai/blog/ai-finance-professional-insight/
• Fay R., Montague N. R., (2015). "I'm Not Biased, Am I?" Journal of Accountancy, Feb. 1, https://bit.ly/ 2JBjM3f
• Goodwin, S. (1996). Data rich, information poor (drip) syndrome: is there a treatment? Radiology management 18(3) 45–49.
• Greiner P., Bogatsch T., Jahn N., Martins L., Linß G., Notni G., (2019). "Remote-audit and VR support in precision and mechanical engineering,". Photonics and Education in Measurement Science 2019, 111440C (17 September); https://doi. org/10.1117/12.2533016. Gunderson, C., (2019). Artificial Intelligence and Machine Learning, https://www.protiviti.com/sites/default/ files/united_states/insights/ai-ml-global-study-protiviti.pdf adresinden alındı.
• Hashimoto, K., (2020). What to expect from audit software in 2021 to 2022, MindBridge, https://www.mindbridge.ai/blog/audit-software-2021-2022-trends/ adresinden alındı.
• IIA, (2017). The Institute of Internal Auditors, International Standards for the Professional Practice of Internal Auditing (Standards).
• ISACA, (2012). Information Systems Audit and Control Association: Cobit 5: Implementation. ISACA
• ISO, (2018). International Organization for Standardization: Risk management -Guidelines. Standard, ISO 31000, Geneva, CH (February)
• 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.
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
• Jorgensen, B.N., Kirschenheiter, M.T. (2003). Discretionary risk disclosures. The Accounting Review 78(2) 449–469.
• Joshi N., (2019). Robotic Process Automation Just Got' Intelligent' Thanks to Machine Learning. Forbes, Jan, 29, https://bit.ly/2JLadPh
• Kantar, L. (2020). BİST 100 Endeksinin Yapay Sinir Ağlari ve Arma Modeli ile Tahmini. Muhasebe ve Finans İncelemeleri Dergisi, 3 (2) , 121-131. Kepes B., (2016). Big Four Accounting Firms Delve into Artificial Intelligence. Computerworld, Mar.16, https://bit.ly/30jYmxo
• Kokina, J., Davenport, T.H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting 14(1) 115–122.
• Kravet, T., Muslu, V. (2013). Textual risk disclosures and investors' risk perceptions. Review of Accounting Studies 18(4) 1088–1122.
• Kuenkaikaew, S., Vasarhelyi, M.A. (2013). The predictive audit framework. The International Journal of Digital Accounting Research 13(19) 37–71.
• Lucky, N., (2020). Can Human Auditors be Replaced by Artificial Intelligence (AI). https://international.binus.ac.id/ finance/can-human-auditors-be-replaced-by-artificial-intelligence-ai/
• Mogg T., (2019). McDonald's to Use AI to Tempt You into Extra Purchases at the Drive-thru. Digital Trends, Mar. 26, https://bit.ly/2w43 BDF Newmark R., Dickey G., and Wilcox W., (2018). Agility in Audit: Could Scrum Improve the Audit Process? Current Issues in Auditing, Spring, https://bit.ly/2HlcnUt
• PwC (2017). Robotic process automation: A primer for internal audit professionals. https://www.pwc.com /us/en/risk-assurance/publications/assets/pwc-robotics-process-automation-a-primer-for-internal-audit-professionals-october-2017.pdf adresinden alındı.
• Sapphiro, D., (2020). Artificial Intelligence for Internal Audit and Risk Management Dragging Assessments Into the Modern Era, Towards Data Science, https:// towardsdatascience.com/artificial-intelligence-for-internal-audit-and-risk-management-94e509129d49#2402.
• Schrand, C. M., & Elliott, J. A. (1998). Risk and financial reporting: A summary of the discussion at the 1997 AAA/FASB conference. Accounting Horizons, 12(3), 271.
• Shabbir, J., Anwer, T. (2018). Artificial intelligence and its role in near future. arXiv preprint arXiv:1804.01396.
• Softwareworld, (2021). Top Audit Management Software of 2021, https://www.softwareworld.co/ best-audit-management-software/ adresinden alındı.
• Struthers-Kennedy, A., (2019). Protivity- IT Audit Benchmarking Study, https://www.protiviti.com/ US-en/insights/it-audit-benchmarking-survey adresinden alındı. Sun, T., Vasarhelyi, M.A. (2017). Deep learning and the future of auditing: How an evolving technology could transform analysis and improve judgment. CPA Journal 87(6).
• Sun, T., Vasarhelyi, M.A., et al. (2018). Embracing textual data analytics in auditing with deep learning. The International Journal of Digital Accounting Research Vol. 18, 49-67 ISSN: 2340-5058, Universidad de Huelva.
• Sutton S., Holt M., Arnold V., (2016). The Reports of My Death Are Greatly Exaggerated: Artificial Intelligence Research in Accounting. International Journal of Accounting Information Systems, September, https://bit.ly/2JCgnBu.
• Thabit, T. (2019). Determining the effectiveness of internal controls in enterprise risk management based on COSO recommendations. In: International Conference on Accounting, Business Economics, and Politics.
• Vasarhelyi M., Rozario A., (2018). How Robotic Process Automation Is Transforming Accounting and Auditing. CPA Journal, June, https://bit.ly /2F7t5Ae. Wyatt, J., (2019). The Next Generation of Internal Auditing- Are you ready? https://www.protiviti. com/sites/default/files/united_states/insights/next-generation-internal-audit.pdf adresinden alındı.
• Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V. (2019). Xlnet: Generalized autoregressive pre-training for language understanding. arXiv preprint arXiv:1906.08237.
• Yoon, K. (2014). Convolutional Neural Networks for Sentence Classification [OL]. arXiv Preprint.
• Yoon K., (2016). Three Essays on Unorthodox Audit Evidence. Doctoral dissertation, Rutgers University, https://bit.ly/2VmN4VJ.
• İnternet Kaynakları
• ACFE - Association of Certified Fraud Examiners (2020). Report to The Nation on Occupational Fraud and Abuse. https://www.acfe.com/report-to-the-nations/2020/ adresinden alındı.
• AICPA - American Institute of Certified Public Accountants (2017). Trust Services Criteria.