EMPIRICAL INVESTIGATION INTO THE INTEGRATION OF CLOUD-BASED ARTIFICIAL INTELLIGENCE IN AUDITING

Year 2023, , 113 - 114, 15.01.2024

Yazan Abdelmajid Abu Huson Laura Sıerra-garcıa María Antonia Garcıa-benau Nader Mohammad Aljawarneh

https://doi.org/10.17261/Pressacademia.2023.1874

Abstract

Purpose- The increasing prevalence of cloud computing and the rapid proliferation of artificial intelligence technologies have opened up novel prospects for enhancing auditing methodologies. This research aims to scrutinize the consequences of incorporating cloud-based artificial intelligence (CBAI) in auditing, focusing on its implications for audit clients, auditors, and the overall audit process.
Methodology- A method of quantitative research was utilized in this study, where 322 questionnaires were distributed to external auditors in Jordan. The objective was to collect information on the potential enhancements brought about by cloud-based artificial intelligence in the auditing field. The study employed convenience random sampling, a technique involving the collection of data from readily available members of the population, which, in this context, refers to external audit offices in Jordan. Jordan has a total of 454 audit offices with a diverse range of auditors, including partner-owner auditors, assistant auditors, and certified auditors. The analysis of the gathered data was conducted using SmartPLS software, which employs structural equation modeling (SEM).
Findings- The study's outcomes reveal the potential for cost savings associated with the adoption of CBAI, as well as the streamlining of audit procedures and the enhancement of overall efficiency. Moreover, the research observes the transformation of auditors' roles, with a shift towards a greater focus on analytical and advisory responsibilities, departing from traditional manual tasks. These findings underscore the potential advantages for audit clients, auditors, and the audit process, underscoring the significance of embracing these technologies to advance the auditing profession in the digital age.
Conclusion- The study investigates how CBAI influences audit quality and efficiency through a thorough examination of data sourced from a sample of external auditors operating within the context of Jordan. Additionally, it delves into the significant advancements that CBAI can introduce to the auditing field, encompassing its potential for data storage and analysis, the improvement of communication between auditors and clients, the adoption of innovative audit techniques, and the shift from manual to digital auditing methodologies.

Keywords

Cloud-based artificial intelligence, Auditing, Auditors, Cloud computing, Artificial Intelligence, Audit quality

References

• Abu Huson, Y., Sierra-García, L., & Garcia-Benau, M. A. (2023). A bibliometric review of information technology, artificial intelligence, and blockchain on auditing. Total Quality Management & Business Excellence, 1-23. https://doi.org/10.1080/14783363.2023.2256260
• Aldossary, S., & Allen, W. (2016). Data security, privacy, availability and integrity in cloud computing: issues and current solutions. International Journal of Advanced Computer Science and Applications, 7(4). http://dx.doi.org/10.14569/IJACSA.2016.070464
• Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting and Organizational Change, 17(2), 164-196. https://doi.org/10.1108/JAOC-09-2019-0098
• Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2019). Big Data and changes in audit technology: contemplating a research agenda. Accounting and business research, 49(1), 95-119. https://doi.org/10.1080/00014788.2018.1459458
• Seethamraju, R., & Hecimovic, A. (2022). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 03128962221108440. https://doi.org/10.1177/03128962221108440
• Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from Artificial‐Intelligence‐based systems in professional service operations. Journal of Operations Management, 68(6-7), 592-618. https://doi.org/10.1002/joom.1215
• Sun, J., Gan, W., Chao, H. C., Philip, S. Y., & Ding, W. (2023). Internet of behaviors: A survey. IEEE Internet of Things Journal. doi: 10.1109/JIOT.2023.3247594.
• Tiron-Tudor, A., Deliu, D., Farcane, N., & Dontu, A. (2021). Managing change with and through blockchain in accountancy organizations: A systematic literature review. Journal of Organizational Change Management, 34(2), 477-506.https://doi.org/10.1108/JOCM-10-2020-0302
• Türegün, N. (2019). Impact of technology in financial reporting: The case of Amazon Go. Journal of Corporate Accounting and Finance, 30(3), 90-95. https://doi.org/10.1002/jcaf.22394
• Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data: A roadmap from models to technologies, 3-20. https://doi.org/10.1007/978-3-319-67925-9_1
• Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. Journal of analysis and testing, 2(3), 249-262. https://doi.org/10.1007/s41664-018-0068-2