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Journal of Social Economics Research

December 2020, Volume 7, 2, pp 51-61

Perception of Professional Accountants about the Application of Artificial Intelligence (AI) in Auditing Industry of Bangladesh

Dilruba Afroze

Anisha Aulad

Dilruba Afroze¹

Anisha Aulad²

Lecturer, Department of Accounting & Information Systems, Faculty of Business Studies, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh. ¹

Student, Department of Accounting & Information Systems, Faculty of Business Studies, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh. ²

on Google Scholar

on PubMed

Pages: 51-61

DOI: 10.18488/journal.35.2020.72.51.61

Article History:

Received: 19 March, 2020
Revised: 27 April, 2020
Accepted: 29 May, 2020
Published: 24 June, 2020

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Abstract:

Globalization and frequent evolution of technology keeps putting pressure on professionals mastered in different fields which does not leave accountants and auditing professionals behind. With frequent adoption of modern technologies like AI, it has become incumbent on every professional to adopt and regularize the use of such technologies in respective fields. Accounting and auditing, previously a tedious work, has become faster and easier through technologies like knowledge based systems. Although the concept of AI is relatively new in the world of accounting, it is being used extensively by some firms and practitioners and continues to evolve its ways. However, within the context of Bangladesh, the application of AI is still in its infant’s stage. This study aims at exploring the perceptions of accounting professionals about the application of AI in their profession. Respondents of this study are audit practitioners employed in different auditing firms across Dhaka city. We have collected data through five point Likert scale and a questionnaire survey. We have found a variety of perceptions about this technology from the practitioner’s. Result indicates that professionals in Bangladesh still are not fully aware of AI use in auditing. Certain problems like incurrence of huge cost, lack of information security and unsecured jobs create hindrance for AI to get rooted in the auditing industry of Bangladesh.

Contribution/ Originality
This study documents the perceptions of professionals about use of artificial intelligence (AI) in the accounting and auditing industry of developing country such as Bangladesh. It finds that there is an unexplored and untapped market for AI in Bangladesh, which can be utilized if proper policy is taken by authorities.

Keywords:

Artificial Intelligence, Auditing Industry, Bangladesh, Professional Accountants, Transparency, Big data, Technology.

Reference:

Askary, S., Abu-Ghazaleh, N., & Tahat, Y. (2018). Artificial intelligence and reliability of accounting information. Paper presented at the 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Kuwait City, Kuwait, October 30 – November 1, 2018, Proceedings. 10.1007/978-3-030-02131-3_28.

Ata, H. A., & Seyrek, I. H. (2009). The use of data mining techniques in detecting fraudulent financial statements: An application on manufacturing firms. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(2), 157-170.

Baldwin, A. A., Brown, C. E., & Trinkle, B. S. (2006). Opportunities for artificial intelligence development in the accounting domain: The case for auditing. Intelligent Systems in Accounting, Finance & Management: International Journal, 14(3), 77-86.Available at: https://doi.org/10.1002/isaf.277.

Brown, C. E., & Murphy, D. S. (1990). The use of auditing expert systems in public accounting. Journal of Information Systems, 4(3), 63-72.

Chan, D. Y., & Vasarhelyi, M. A. (2011). Innovation and practice of continuous auditing. International Journal of Accounting Information Systems, 12(2), 152-160.

Dörnyei, Z. (2007). Research methods in applied linguistics. New York: Oxford University Press.

Dungan, C. W., & Chandler, J. S. (1985). Auditor: A microcomputer-based expert system to support auditors in the field. Expert Systems, 2(4), 210-221.Available at: https://doi.org/10.1111/j.1468-0394.1985.tb00474.x.

Flowerday, S., Blundell, A., & Von Solms, R. (2006). Continuous auditing technologies and models: A discussion. Computers & Security, 25(5), 325-331.Available at: https://doi.org/10.1016/j.cose.2006.06.004.

Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing Journal of Practice & Theory, 16(1), 14-28.

Hunton, J. E., & Rose, J. M. (2010). 21st century auditing: Advancing decision support systems to achieve continuous auditing. Accounting Horizons, 24(2), 297–312.Available at: https://doi.org/10.2308/acch.2010.24.2.297.

Huq, S. M. U. (2014). The role of Artificial intelligence in the development of accounting systems: A review. IUP Journal of Accounting Research & Audit Practices, 13(2), 7-19.

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.Available at: https://doi.org/10.2308/jeta-10511.

Kanatov, M., Atymtayeva, L., & Yagaliyeva, B. (2014). Expert systems for information security management and audit. Implementation phase issues. Paper presented at the 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), Kitakyushu, 2014.

Lee, Y.-C., Hsiao, Y.-C., Peng, C.-F., Tsai, S.-B., Wu, C.-H., & Chen, Q. (2015). Using mahalanobis–taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(1_suppl), 3-12.

Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Paper presented at the In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers. IEEE.

Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1-10.

O'Leary, D. E., & Watkins, P. R. (1989). Review of expert systems in auditing. EXPERT System Review, 2(1), 3-22.

Omoteso, K. (2012). The application of artificial intelligence in auditing: Looking back to the future. Expert Systems with Applications, 39(9), 8490-8495.Available at: https://doi.org/10.1016/j.eswa.2012.01.098.

Saleiro, P., Kuester, B., Hinkson, L., London, J., Stevens, A., Anisfeld, A., & Ghani, R. (2018). Aequitas: A bias and fairness audit toolkit (pp. 4-14). Chicago, IL 60637, USA: Center for Data Science and Public Policy University of Chicago.

Shi, G., Ma, Z., Feng, J., Zhu, F., Bai, X., & Gui, B. (2020). The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms. PLoS ONE, 15(5), e0232658.Available at: https://doi.org/10.1371/journal.pone.0232658.

Wu, R. C. F. (1994). Integrating neurocomputing and auditing expertise. Managerial Auditing Journal, 9(3), 20-26.

Zhao, N., Yen, D. C., & Chang, I. C. (2004). Auditing in the e-commerce era. Information Management & Computer Security, 12, 389-400.