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Autor
Karmańska Anna (University of Economics in Katowice, Poland)
Tytuł
Artificial Intelligence in Audit
Sztuczna inteligencja w audycie
Źródło
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 2022, nr 4 (66), s. 87-99, tab., bibliogr. 36 poz.
Research Papers of Wrocław University of Economics
Słowa kluczowe
Audyt, Sztuczna inteligencja, Uczenie maszynowe
Audit, Artificial intelligence, Machine learning
Uwagi
Klasyfikacja JEL: O33, M42, J24
streszcz., summ.
Abstrakt
Celem artykułu jest wskazanie korzyści płynących z zastosowania sztucznej inteligencji (AI) w badaniu sprawozdań finansowych. Posłużono się kwestionariuszem ankiety. Próbą badawczą objęto 206 praktyków i studentów audytu i rachunkowości. Zastosowano analizę czynnikową metodą głównych składowych z rotacją Promax. Wyniki wskazują, że w opinii respondentów zastosowanie sztucznej inteligencji zwiększa efektywność audytu. Sztuczna inteligencja usprawnia komunikację i obsługę klienta. Ponadto AI może zautomatyzować czasochłonne i rutynowe zadania. Powyższe trzy czynniki odpowiadają za 62,223% wariancji. Wyniki badania wskazują na korzyści płynące z implementacji sztucznej inteligencji w audycie i mogą wspierać menedżerów we wdrażaniu nowych technologii w ich organizacjach. Ograniczeniem badawczym jest fakt, że badanie koncentruje się na respondentach jedynie z Polski.(abstrakt oryginalny)

The main objective of this paper is to identify the benefits of applying the Artificial Intelligence (AI) in the audit sector. The study employed a questionnaire for a research sample including 206 auditing and accounting practitioners and students. Data were collected via an online survey. A principal axis factor analysis with the Promax rotation was conducted to assess the underlying structure for the points of the questionnaire. The research outcomes indicate that, in the opinion of the respondents, AI adoption increases audit efficiency, and enhances client communication and service. Finally, AI can also automate time-consuming and routine tasks. The three indicated factors account for 62.223% variance. The findings reveal the advantages of AI adoption and could support managers in deploying new technology in their organizations. The research limitation concerns the fact that this study focused only on respondents from Poland.(original abstract)
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Bibliografia
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  1. Aksoy, T., and Gurol, B. (2021). Artificial Intelligence in Computer-Aided Auditing Techniques and Technologies (CAATTs) and an application proposal for auditors (pp. 361-384). https://doi. org/10.1007/978-3-030-72628-7_17
  2. Albawwat, I., and Frijat, Y. Al. (2021). An analysis of auditors' perceptions towards artificial intelligence and its contribution to audit quality. Accounting, 7(4), 755-762. https://doi.org/10.5267/j.ac. 2021.2.009
  3. Albitar, K., Gerged, A. M., Kikhia, H., and Hussainey, K. (2021). Auditing in times of social distancing: The effect of COVID-19 on auditing quality. International Journal of Accounting and Information Management, 29(1), 169-178. https://doi.org/10.1108/IJAIM-08-2020-0128
  4. Bertomeu, J., Cheynel, E., Floyd, E., and Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468-519. https://doi.org/10.1007/s11142-020-09563-8
  5. Bérubé, M., Giannelia, T., and Vial, G. (2021). Barriers to the implementation of AI in organizations: Findings from a Delphi study (Proceedings of the Annual Hawaii International Conference on System Sciences, 2020-Janua, pp. 6702-6711). https://doi.org/10.24251/hicss.2021.805
  6. Bhalerao, K., Kumar, A., Kumar, A., and Pujari, P. (2022). A study of the barriers and benefits of artificial intelligence adoption in small and medium enterprise. Academy of Marketing Studies Journal, 26(1), 1-6.
  7. Christ, M. H., Emett, S. A., Summers, S. L., and Wood, D. A. (2021). Prepare for takeoff: improving asset measurement and audit quality with drone-enabled inventory audit procedures. Review of Accounting Studies, 26(4), 1323-1343. https://doi.org/10.1007/s11142-020-09574-5
  8. Commerford, B. P., Dennis, S. A., Joe, J. R., and Ulla, J. (2022). Man versus machine: Complex estimates and auditor reliance on Artificial Intelligence. Journal of Accounting Research, 60(1). https://doi.org/10.1111/1475-679X.12407
  9. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Management, Ph.D., (291). https://doi.org/oclc/56932490
  10. Deloitte. (2018). Cognitive technology to comply with new accounting standards. Retrieved from https:// www2.deloitte.com/za/en/pages/audit/articles/cognitive-technology-expedites-new-accountingstandards-compliance.html
  11. EY. (2019). Audit innovation. Retrieved from https://www.ey.com/en_gl/audit/innovation
  12. Hasan, A. R. (2022). Artificial Intelligence (AI) in accounting and auditing: A literature review. Open Journal of Business and Management, 10(01), 440-465. https://doi.org/10.4236/ojbm.2022.101026
  13. Hołda, A. (2010). Istota badań próbkowych w rewizji finansowej i dobór prób badawczych. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, (829). https://r.uek.krakow.pl/bitstream/123456789/2711/1/170626644.pdf
  14. Hoogduin, L. A. (2019). Using machine learning in a financial statement audit. Compact, (4), 4-8.
  15. IAASB. (2020). Non-authoritative support material related to technology: frequently asked questions (faq) the use of automated tools and techniques when identifying and assessing risks of material - misstatement in accordance with ISA 315 (revised 2019). Retrieved from https://www.ifac.org/ system/files/publications/files/IAASB-Technology-FAQ-Automated-Tools-Techniques.pdf
  16. International Federation of Accountants [IFAC]. (2009). ISA 200, overall objectives of the independent auditor and the conduct of an audit in accordance with international standards on auditing (Issue January 2009, pp. 72-100). Retrieved from http://www.ifac.org/sites/default/files/publications/ files/2012 IAASB Handbook Part I_Web.pdf
  17. International Federation of Accountants [IFAC]. (2019). ISA 315 (Revised), identifying and assessing the risks of material misstatement. Retrieved from https://www.ifac.org/system/files/publications/ files/ISA-315-Full-Standard-and-Conforming-Amendments-2019-.pdf
  18. International Federation of Accountants [IFAC]. (2009). ISA 500, audit evidence. Retrieved from https://www.ifac.org/system/files/downloads/a022-2010-iaasb-handbook-isa-500.pdf
  19. International Federation of Accountants [IFAC]. (2009). ISA 530, audit sampling. Retrieved from https://www.ifac.org/system/files/downloads/a027-2010-iaasb-handbook-isa-530.pdf
  20. Karmańska, A. (2020). The determinants of key audit matters in listed companies in Poland, in accounting, reporting and auditing. Meeting the needs of the information providers and users.
  21. Kokina, J., and Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115-122. https://doi. org/10.2308/jeta-51730
  22. Law, K., and Shen, M. (2021). How does artificial intelligence shape audit firms? (Nanyang Business School Research Paper, 20-31). https://doi.org/http://dx.doi.org/10.2139/ssrn.3718343
  23. Maurer, M. (2021). PwC to Spend $12 Billion on Hiring, Expanding Expertise in AI, Cybersecurity. The Wall Street Journal. Retrieved from https://www.wsj.com/articles/pwc-to-spend-12-billionon-hiring-expanding-expertise-in-ai-cybersecurity-11623758400
  24. Munoko, I., Brown-Liburd, H. L., and Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. J Bus Ethics, (167), 209-234. https://doi.org/https://doi.org/10.1007/ s10551-019-04407-1
  25. Panetta, K. (2019). The CIO's guide to Artificial Intelligence. Retrieved from https://www.gartner.com/ smarterwithgartner/the-cios-guide-to-artificial-intelligence
  26. Pedrosa, I., Costa, C. J., and Aparicio, M. (2020). Determinants adoption of computer-assisted auditing tools (CAATs). Cognition, Technology and Work, 22(3), 565-583. https://doi.org/10.1007/s10111019-00581-4
  27. Polska Agencja Nadzoru Audytowego. (2021). Badanie sprawozdań finansowych przez firmy audytorskie na podstawie sprawozdań rocznych za rok 2020 oraz wyników kontroli. Retrieved from https://pana.gov.pl/wp-content/uploads/2021/10/badanie-sprawozdan-finansowych-przezfirmy-audytorskie-na-podstawie-sprawozdan-rocznych-za-rok-2020-oraz-wynikow-kontroli.pdf
  28. Puthukulam, G., Ravikumar, A., Sharma, R. V. K., and Meesaala, K. M. (2021). Auditors' perception on the impact of artificial intelligence on professional skepticism and judgment in Oman. Universal Journal of Accounting and Finance, 9(5), 1184-1190. https://doi.org/10.13189/ujaf.2021.090527
  29. PwC. (2017). Harnessing the power of AI to transform the detection of fraud and error. Retrieved from https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-the-power-of-ai-totransform-the-detection-of-fraud-and-error.html
  30. Serpeninova, Y., Makarenko, S., and Litvinova, M. (2020). Computer-assisted audit techniques: classification and implementation by auditor. Public Policy and Accounting, 1(1), 44-49. https:// doi.org/10.26642/ppa-2020-1-44-49
  31. Tiron-Tudor, A., and Deliu, D. (2021). Reflections on the human-algorithm complex duality perspectives in the auditing process. Qualitative Research in Accounting and Management. https:// doi.org/10.1108/QRAM-04-2021-0059
  32. Ucoglu, D. (2020). Current machine learning applications in accounting and auditing. Pressacademia, 12(1), 1-7. https://doi.org/10.17261/pressacademia.2020.1337
  33. Vein, C., and Sidhu, H. (2018). Using drones to enhance audits. School of Accountancy. Retrieved from https://www.journalofaccountancy.com/podcast/using-drones-to-enhance-audits.html
  34. Zemankova, A. (2019). (Proceedings - 2019 3rd International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2019, pp. 148-154). https://doi.org/10.1109/ ICCAIRO47923.2019.00031
  35. Zhang, A. (Chanyuan). (2019). Intelligent process automation in audit. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3448091
  36. Zhang, Y., Xiong, F., Xie, Y., Fan, X., and Gu, H. (2020). The impact of Artificial Intelligence and blockchain on the accounting profession. IEEE Access, (8), 110461-110477. https://doi. org/10.1109/ACCESS.2020.3000505
Cytowane przez
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ISSN
1899-3192
Język
eng
URI / DOI
http://dx.doi.org/10.15611/pn.2022.4.06
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