Anahtar Kelimeler
Nasıl Atıf Yapılır
Öz
Bu çalışma, tek bir şirkete ait 2025 hesap dönemi bağımsız denetim dosyasının genişletilmiş güvence yaklaşımı bağlamında dijitalleşme potansiyelini incelemektedir. Nitel tek örnek olay tasarımında doküman analizi kullanılmıştır. Çalışma kâğıtları, risk değerlendirme belgeleri, kontrol testleri, detay testleri, dış teyitler, finansal tablolar, bilgi teknolojileri belgeleri ve bağımsız denetçi raporu, içerik analizi, süreç haritalandırma ve analitik değerlendirme matrisiyle değerlendirilmiştir. Bulgular, dosyanın risk-prosedür-kanıt izlenebilirliği bakımından güçlü bir başlangıç veri altyapısı sunduğuna işaret etmektedir. Genişletilmiş Güvence Potansiyel Skoru 3,83 olarak hesaplanmıştır. Bu değer, dijital güvence altyapısına dönüşme potansiyelini gösteren keşifsel bir değerlendirmedir ve doğrulanmış bir denetim kalitesi ölçütü değildir. Denetim raporunun dosyada üretilen kanıt yoğunluğunu sınırlı biçimde yansıttığı belirlenmiştir. Çalışma, dijitalleşmenin denetçiyi ikame etmekten ziyade mesleki muhakemeyi ve kanıt değerlendirme kapasitesini destekleyebileceğine işaret etmektedir.
Makale Kullanım İstatistikleri
##plugins.themes.healthSciences.displayStats.downloads##
Referanslar
Abu Al Rob, M., Mohd Nor, M. N., & Salleh, Z. (2024). The influence of big data analytics adoption on auditors’ professional skepticism in risk assessment: An empirical study using the technology acceptance model. Journal of Logistics, Informatics and Service Science, 11(11), 158–177. https://doi.org/10.33168/JLISS.2024.1110
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27. https://doi.org/10.2308/ajpt-51684
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(1), 83–101. https://doi.org/10.1016/j.acclit.2018.01.001
Barr-Pulliam, D., Brown-Liburd, H. L., & Sanderson, K.-A. (2022). The effects of the internal control opinion and use of audit data analytics on perceptions of audit quality, assurance, and auditor negligence. Auditing: A Journal of Practice & Theory, 41(1), 25–48. https://doi.org/10.2308/AJPT-19-064
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2), 423–429. https://doi.org/10.2308/acch-51068
DeFond, M., & Zhang, J. (2014). A review of archival auditing research. Journal of Accounting and Economics, 58(2–3), 275–326. https://doi.org/10.1016/j.jacceco.2014.09.002
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management, 23(3), 157–214. https://doi.org/10.1002/isaf.1386
Francis, J. R. (2011). A framework for understanding and researching audit quality. Auditing: A Journal of Practice & Theory, 30(2), 125–152. https://doi.org/10.2308/ajpt-50006
Gönen, S. (2016). Bağımsız denetimde çalışma kağıtlarının 230 No’lu Türkiye Denetim Standardı açısından incelenmesi. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 5(7), 1792–1808. https://doi.org/10.15869/itobiad.259185
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. https://doi.org/10.2308/jeta-10511
Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975
Kamu Gözetimi, Muhasebe ve Denetim Standartları Kurumu. (2025). 2024 yılı yıllık inceleme raporu. https://www.kgk.gov.tr/Portalv2Uploads/files/Duyurular/v2/Diger/2024%20Yıllık%20İnceleme%20Raporu.pdf
Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187–1230. https://doi.org/10.1111/1475-679X.12123
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150, Article 119751. https://doi.org/10.1016/j.techfore.2019.119751
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), Article 2053951716679679. https://doi.org/10.1177/2053951716679679
Porter, B. (1993). An empirical study of the audit expectation-performance gap. Accounting and Business Research, 24(93), 49–68. https://doi.org/10.1080/00014788.1993.9729463
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873
Ruhnke, K., & Schmidt, M. (2014). The audit expectation gap: Existence, causes, and the impact of changes. Accounting and Business Research, 44(5), 572–601. https://doi.org/10.1080/00014788.2014.929519
Sullivan, G. M., & Artino, A. R., Jr. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/JGME-5-4-18
Şirin, M. (2006). Denetimde kanıt teorisi ve gelişimi. Sayıştay Dergisi, (61), 23–36. https://dergipark.org.tr/tr/pub/sayistay/article/918889
Wang, X., Sun, F., Kim, M. G., & Na, H. J. (2025). Developing a novel audit risk metric through sentiment analysis. Sustainability, 17(6), Article 2460. https://doi.org/10.3390/su17062460
Werner, M., & Gehrke, N. (2015). Multilevel process mining for financial audits. IEEE Transactions on Services Computing, 8(6), 820–832. https://doi.org/10.1109/TSC.2015.2457907
Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 1–18). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372833
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
Zhong, C., & Goel, S. (2024). Transparent AI in auditing through explainable AI. Current Issues in Auditing, 18(2), A1–A14. https://doi.org/10.2308/CIIA-2023-009
Bu çalışma Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanslanmıştır.
Telif Hakkı (c) 2026 Mustafa Terzioğlu