The application of Artificial Intelligence in external auditing and its implications on audit quality? A review of the ongoing debates

Favourate y Mpofu

doi:10.20525/ijrbs.v12i9.2737

Authors

Favourate y Mpofu University of Johannesburg https://orcid.org/0000-0002-6309-1697

DOI:

https://doi.org/10.20525/ijrbs.v12i9.2737

Keywords:

Auditing, Artificial Intelligence, ethical, implications, audit quality

Abstract

With the intensity of the Fourth Industrial Revolution, Artificial Intelligence (AI) is being widely adopted to perform key tasks in economic activities. The audit profession has also embraced AI in the performance of its function in carrying out activities like audits, oversight, and advisory functions. The application of AI has been met with acceptance, given its advantages in some quarters and with resistance/scepticism in some. Proponents table benefits such as improved sampling procedures, reduced labour, and time in performing audits, increased efficiency, and effectiveness (due to increased audit coverage) including improved audit quality. Opponents raise pragmatic concerns such as the violation of ethical principles governing the audit profession, potential biases (loss of employment) as well as the challenges of coordinating machine and human activities. The study has two objectives. Firstly, to explore the role of AI in the external audit function. Secondly, to evaluate the ongoing debates on artificial intelligence and external auditing and assess the implications of using AI in the external audit function. The study adopts a qualitative research approach, employing a critical literature review. The study will accentuate the controversies and convergences among researchers on the role and implications of applying AI in external auditing to bring to light possible research gaps that can be explored by future researchers on the subject area. Highlighting the potential externalities of using AI has both theoretical and practical implications.

Downloads

Download data is not yet available.

References

Agnew, W., Lee, H., Jang, M., Gavrilovska, A., & Schwan, K. (2016). Drawing Insights from" Big Data" Machine Learning Literature using Textual Features Why are some novels considered to be literature? A. predictive model of literary judgments shows that textual features are important. Andreas van Cranenburgh: Reducing Mobile Throttling from Temperature by Offloading Apps. Tiny Trans. Comput. Sci., 4.

Agustí, M. A., & Orta-Pérez, M. (2022). Big data and artificial intelligence in the fields of accounting and auditing: a bibliometric analysis. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 1-27. DOI: https://doi.org/10.1080/02102412.2022.2099675

Aksoy, T., & Gurol, B. (2021). Artificial intelligence in computer-aided auditing techniques and technologies (CAATTs) and an application proposal for auditors. In Auditing ecosystem and strategic accounting in the digital era (pp. 361-384). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-72628-7_17

Alaba, F., & Ghanoum, S. (2020). Integration of Artificial Intelligence in Auditing: the Effect on Auditing Process.

Al-Aroud, S. F. (2020). The Impact of artificial intelligence technologies on audit evidence. Academy of Accounting and Financial Studies Journal, 24, 1-11.

Albawwat, I., & Frijat, Y. (2021). An analysis of auditors’ perceptions towards artificial intelligence and its contribution to audit quality. Accounting, 7(4), 755-762. DOI: https://doi.org/10.5267/j.ac.2021.2.009

Al Frijat, Y. S. (2020). Do International Financial Reporting Standards aid in Providing High-Quality Data and Contribute to Open and Precise Accounting Statements?. International Journal of Business, Accounting and Finance, 14(1), 82-103.

Alipourfallahpasand, A., & Asgari, S. (2020). Emerging Technologies in The Audit Environment: Use and Perceived Importance Among Independent Auditors. Iranian Journal of Accounting, Auditing and Finance, 4(2), 103-119.

Allami, F. A. J., Nabhan, S. H., & Jabbar, A. K. (2022). A Comparative Study of Measuring the Accuracy of Using Artificial Intelligence Methods as an Alternative to Traditional Methods of Auditing. World Economics and Finance Bulletin, 9, 90-99.

Al-Sayyed, S., Al-Aroud, S., & Zayed, L. (2021). The effect of artificial intelligence technologies on audit evidence. Accounting, 7(2), 281-288. DOI: https://doi.org/10.5267/j.ac.2020.12.003

Ardichvili, A. (2022). The Impact of Artificial Intelligence on Expertise Development: Implications for HRD. Advances in Developing Human Resources, 24(2), 78-98. DOI: https://doi.org/10.1177/15234223221077304

Bai, G. H. (2017). Research on the Application and Influence of Auditing Artificial Intelligence. DEStech Transactions on Social Science, Education and Human Science,(eiem). DOI: https://doi.org/10.12783/dtssehs/eiem2017/16097

Bahrami, F., Rezazadeh, J., & Sarraf, F. (2020). Forecasting audit opinion based on multilevel perceptron neural network model using one-goal particle swarm optimisation. International Journal of Management Practice, 13(1), 86-102. DOI: https://doi.org/10.1504/IJMP.2020.10025712

Bakarich, K. M., & O'Brien, P. E. (2021). The robots are coming… but aren't here yet: The use of artificial intelligence technologies in the public accounting profession. Journal of Emerging Technologies in Accounting, 18(1), 27-43. DOI: https://doi.org/10.2308/JETA-19-11-20-47

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. DOI: https://doi.org/10.1002/isaf.277

Balios, D., Kotsilaras, P., Eriotis, N., & Vasiliou, D. (2020). Big data, data analytics and external auditing. Journal of Modern Accounting and Auditing, 16(5), 211-219. DOI: https://doi.org/10.17265/1548-6583/2020.05.002

Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235. DOI: https://doi.org/10.1111/1475-679X.12292

Bozhinovska Lazarevska, Z., Tocev, T., & Dionisijev, I. (2022). How to improve performance in public sector auditing through the power of big data and data analytics?: the case of The Republic of North Macedonia. DOI: https://doi.org/10.32602/jafas.2022.023

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. DOI: https://doi.org/10.1191/1478088706qp063oa

Braun, V., & Clarke, V. (2014). What can “thematic analysis” offer health and wellbeing researchers?. International journal of qualitative studies on health and well-being, 9(1), 26152. DOI: https://doi.org/10.3402/qhw.v9.26152

Braun, V., & Clarke, V. (2020). Thematic analysis. Analysing qualitative data in psychology. London: Sage Publications Ltd, 128-47.

Braun, V., & Clarke, V. (2021). Thematic analysis. A practical guide. London: Sage Publications Ltd.

Brennan, B., Baccala, M., & Flynn, M. (2017). Artificial Intelligence Comes to Financial Statement Audits. 2017. CFO. URL: https://www. cfo. com/auditing/2017/02/artificial-intelligence-audits/(accessed: 04.08. 2019).

Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research, 58(1), 237-291. DOI: https://doi.org/10.1111/1475-679X.12294

Brunskill, E. (2019). “Computers that learn to help”. Human Centred Artificial Intelligence Symposium, Stanford

Calderon, T. G., & Gao, L. (2021). Cybersecurity risks disclosure and implied audit risks: Evidence from audit fees. International Journal of Auditing, 25(1), 24-39. DOI: https://doi.org/10.1111/ijau.12209

Castelo, N., Bos, M. W., & Lehmann, D. (2019). Let the Machine Decide: When Consumers Trust or Distrust Algorithms. NIM Marketing Intelligence Review, 11(2). DOI: https://doi.org/10.2478/nimmir-2019-0012

Cho, S., Vasarhelyi, M. A., Sun, T., & Zhang, C. (2020). Learning from machine learning in accounting and assurance. Journal of Emerging Technologies in Accounting, 17(1), 1-10. DOI: https://doi.org/10.2308/jeta-10718

Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., & Cagan, J. (2022). Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior, 127, 107018. DOI: https://doi.org/10.1016/j.chb.2021.107018

Commerford, B. P., Dennis, S. A., Joe, J. R., & Ulla, J. W. (2022). Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research, 60(1), 171-201. DOI: https://doi.org/10.1111/1475-679X.12407

CPA (2017). Deep Learning and the Future of Auditing : How an Elvoving Technology Could Transform Analysis and Improve Judgement. CPA Journal

Deloitte (2016). “Innovation in audit takes analytics, AI route. Audit analytics, cognitive technologies to set accountants free from grunt work

Deloitte (2020). Technology Industry Outlook: As cloud and AIdeployments rise, edge computing is poised to join the mix. Available at www.deloitte.com/content

Dickey, G, Blanke, S, & Seaton, L. "Machine Learning in Auditing." The CPA Journal (2019): 16-21.

Egiyi, M. A. (2022). The Benefits of Augmented and Virtual Reality in the Accounting Field. Journal of Management, 4(1), 15-21.

Eltweri, A. (2021). The Artificial Intelligence Ethical Implications in Auditing Public Sector.

Ernst & Young (2018a). “Why AI is both a risk and a way to manage risk”. Available www.ey.com/en-gl/assurance/why-ai-is-both-a-risk-and-a-way-to-manage-risk

Ernst & Young (2018b). “How artificial intelligence will transform the audit”. Available at www.ey.com/en-gl/assurance/how-artificial-intelligence-will-transform-the-audit

Ernst & Young (2021). “Why transformation initiatives should be people centric”. Available at www.ey.com/en-gl/consulting/why-intelligent-automation-transformation-iniatives-should-be-people-centric

Eulerich, M., Pawlowski, J., Waddoups, N. J., & Wood, D. A. (2022). A framework for using robotic process automation for audit tasks. Contemporary Accounting Research, 39(1), 691-720. DOI: https://doi.org/10.1111/1911-3846.12723

Fay R. & Montague, N.R. (2015). Im not biased. Am I? Journal of Accountancy

Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research. DOI: https://doi.org/10.1287/isre.2021.1079

Fukas, P., Rebstadt, J., Remark, F., & Thomas, O. (2021). Developing an Artificial Intelligence Maturity Model for Auditing. In 29th European Conference on Information Systems Research Papers.

Gao, Y., & Han, L. (2021). Implications of Artificial Intelligence on the Objectives of Auditing Financial Statements and Ways to Achieve Them. Microprocessors and Microsystems, 104036. DOI: https://doi.org/10.1016/j.micpro.2021.104036

Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): what is the future?. Ai, 1(2), 143-155. DOI: https://doi.org/10.3390/ai1020008

Hematfar, M. (2018). Relationship between auditor switching and the inclination of audit opinion report toward unqualified mode in the companies listed in Tehran Stock Exchange market. Journal of Humanities Insights, 2(04), 199-201.

Huang, F., & Vasarhelyi, M. A. (2019). Applying robotic process automation (RPA) in auditing: A framework. International Journal of Accounting Information Systems, 35, 100433. DOI: https://doi.org/10.1016/j.accinf.2019.100433

IFAC (2015). “Exploring artificial intelligence and the accounting profession. Opportunity, threat, both, neither?”. Available at www.ifac.org/knowledge-gateway-/preparing-future-ready-preofessionals/discussion/exploring-artificial-intelligence-accounting-profession-opportunity-threat-both-neither

IFAC (2019). From algorithms to deep learning. What you need to know about AI”. Available at www.ifac.org/knowledge-gateway/preparing-future-ready-professionals/discussions/algorithms-deep-learning-what-you-need-know about-ai

Jinadu, O., Oluwafemi, S., Soyinka, M., & Akanfe, K. (2017). Effects of International Financial Reporting Standards (IFRS) on Financial Statements Comparability of Companies. Journal of Humanities Insights, 1(01), 12-16.

Joe, J., Commerford, B., Dennis, S., & Wang, J. (2019). Complex Estimates and Auditor Reliance on Artificial Intelligence. DOI: https://doi.org/10.2139/ssrn.3422591

Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 71(1), 91-134. DOI: https://doi.org/10.1007/s11301-020-00181-x

Kend, M., & Nguyen, L. A. (2020). Big data analytics and other emerging technologies: the impact on the Australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282. DOI: https://doi.org/10.1111/auar.12305

Kepes, B. (2016). Big four accounting firms delve into artificial intelligence. Computerworld, 16.

Kokina, J., & Blanchette, S. (2019). Early evidence of digital labour in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems, 35, 100431. DOI: https://doi.org/10.1016/j.accinf.2019.100431

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. DOI: https://doi.org/10.2308/jeta-51730

Kokina, J., Gilleran, R., Blanchette, S., & Stoddard, D. (2021). Accountant as digital innovator: Roles and competencies in the age of automation. Accounting Horizons, 35(1), 153-184. DOI: https://doi.org/10.2308/HORIZONS-19-145

Kommunuri, J. (2022). Artificial intelligence and the changing landscape of accounting: a viewpoint. Pacific Accounting Review, (ahead-of-print). DOI: https://doi.org/10.1108/PAR-06-2021-0107

Koshiyama, A., Kazim, E., & Treleaven, P. (2022). Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms. Computer, 55(4), 40-50. DOI: https://doi.org/10.1109/MC.2021.3067225

Koshiyama, A., Kazim, E., Treleaven, P., Rai, P., Szpruch, L., Pavey, G., ... & Lomas, E. (2021). Towards algorithm auditing: A survey on managing legal, ethical and technological risks of AI, ML and associated algorithms. DOI: https://doi.org/10.2139/ssrn.3778998

KPMG (2019a). Digital labour in audit”. Available at https://home.kpmg/au/en/home/insights/2019/03/audit-technology-digital-labour.

KPMG (2019b). Learning and the audit: rise of machines. Available at https://home.kpmg/au/en/home/insights/2019/04/audit-technology-machine-learning

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. DOI: https://doi.org/10.2308/jeta-10511

Lacity, M., & Willcocks, L. P. (2018). Robotic process and cognitive automation: The next phase. SB Publishing.

Lacity, M., Willcocks, L., & Gozman, D. (2021). Influencing information systems practice: The action principles approach applied to robotic process and cognitive automation. Journal of Information Technology, 36(3), 216-240. DOI: https://doi.org/10.1177/0268396221990778

Lamboglia, R., Lavorato, D., Scornavacca, E., & Za, S. (2020). Exploring the relationship between audit and technology. A bibliometric analysis. Meditari Accountancy Research. DOI: https://doi.org/10.1108/MEDAR-03-2020-0836

Landers, R. N., & Behrend, T. S. (2022). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist. DOI: https://doi.org/10.1037/amp0000972

Law, K., & Shen, M. (2020). How does artificial intelligence shape the audit industry?. Nanyang Business School Research Paper, (20-31). DOI: https://doi.org/10.2139/ssrn.3718343

Lehner, O. M., Ittonen, K., Silvola, H., Ström, E., & Wührleitner, A. (2022). Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking. Accounting, Auditing & Accountability Journal, 35(9), 109-135. DOI: https://doi.org/10.1108/AAAJ-09-2020-4934

Leyer, M., & Schneider, S. (2021). Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?. Business Horizons, 64(5), 711-724. DOI: https://doi.org/10.1016/j.bushor.2021.02.026

Li, Q., & Liu, J. (2020). Development of an intelligent NLP-based audit plan knowledge discovery system. Journal of Emerging Technologies in Accounting, 17(1), 89-97. DOI: https://doi.org/10.2308/jeta-52665

Losbichler, H., & Lehner, O. M. (2021). Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research. Journal of Applied Accounting Research. DOI: https://doi.org/10.1108/JAAR-10-2020-0207

Malone, T. W. (2018). Superminds: The surprising power of people and computers thinking together. Little, Brown Spark.

Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), 5788. DOI: https://doi.org/10.3390/su13115788

Mhlanga, D. (2022). The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals. International Journal of Environmental Research and Public Health, 19(3), 1879. DOI: https://doi.org/10.3390/ijerph19031879

Minh, T. V. H. Legal Issues Facing Artificial Intelligence-Utilizing Audit Firms.

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. DOI: https://doi.org/10.2308/jeta-10589

Moloi, T., & Marwala, T. (2021a). Robotic Process Automation in Strategy and Strategy Implementation. In Artificial Intelligence and the Changing Nature of Corporations (pp. 97-104). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-76313-8_7

Moloi, T., & Marwala, T. (2021b). Natural Language Processing in Strategy and Implementation. In Artificial Intelligence and the Changing Nature of Corporations (pp. 77-95). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-76313-8_6

Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833. DOI: https://doi.org/10.1016/j.bar.2019.04.002

Mpofu, F. Y. (2021). Review Articles: A Critical Review of the Pitfalls and Guidelines to effectively conducting and reporting reviews. Technium Soc. Sci. J., 18, 550.

Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. DOI: https://doi.org/10.1007/s10551-019-04407-1

Muslihatun, F. A. N., Hantono, B. S., & Fauziati, S. (2021, November). Using Artificial Intelligence Technology for Decision Support System in Audit Risk Assessment: A Review Paper. In 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 326-331). IEEE. DOI: https://doi.org/10.1109/ICITISEE53823.2021.9655975

Oldhouser, M. C. (2016). The effects of emerging technologies on data in auditing.

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

Pishgar, M., Issa, S. F., Sietsema, M., Pratap, P., & Darabi, H. (2021). REDECA: a novel framework to review artificial intelligence and its applications in occupational safety and health. International journal of environmental research and public health, 18(13), 6705. DOI: https://doi.org/10.3390/ijerph18136705

Philbeck, T., & Davis, N. (2018). The fourth industrial revolution. Journal of International Affairs, 72(1), 17-22.

Price Water House Coopers (PWC) (2018). Confidence in the future: Human and machine coll.

Puthukulam, M. G., Anitha Ravikumar, D., Cabral, D. H., & Al Balushi, M. F. A measure of Internal Auditors’ perception on Accounting Information Systems, Professional Skepticism and Judgment and Audit efficiency.

Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman. DOI: https://doi.org/10.13189/ujaf.2021.090527

Rapoport, R. S. (2016). The iterative frame: algorithmic video editing, participant observation & the black box (Doctoral dissertation, University of Oxford).

Rebstadt, J., Remark, F., Fukas, P., Meier, P., & Thomas, O. (2022). Towards Personalized Explanations for AI Systems: Designing a Role Model for Explainable AI in Auditing.

Rozario, A. M., & Vasarhelyi, M. A. (2018). How robotic process automation is transforming accounting and auditing. The CPA Journal, 88(6), 46-49.

Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260-278. DOI: https://doi.org/10.1080/12460125.2020.1819094

Sebele-Mpofu, F. Y. (2020). Saturation controversy in qualitative research: Complexities and underlying assumptions. A literature review. Cogent Social Sciences, 6(1), 1838706. DOI: https://doi.org/10.1080/23311886.2020.1838706

Secinaro, S., Dal Mas, F., Brescia, V., & Calandra, D. (2021). Blockchain in the accounting, auditing and accountability fields: a bibliometric and coding analysis. Accounting, Auditing & Accountability Journal. DOI: https://doi.org/10.1108/AAAJ-10-2020-4987

Seethamraju, R. C., & Hecimovic, A. (2020). Impact of Artificial Intelligence on Auditing-An Exploratory Study.

Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89-109. DOI: https://doi.org/10.2308/acch-52455

Sun, T., & Vasarhelyi, M. A. (2018). Embracing textual data analytics in auditing with deep learning. International Journal of Digital Accounting Research, 18. DOI: https://doi.org/10.4192/1577-8517-v18_3

Sutton, S. G., Holt, M., & Arnold, V. (2016). “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60-73. DOI: https://doi.org/10.1016/j.accinf.2016.07.005

Tiron-Tudor, A., & Deliu, D. (2021a). Big data’s disruptive effect on job profiles: management accountants’ case study. Journal of Risk and Financial Management, 14(8), 376. DOI: https://doi.org/10.3390/jrfm14080376

Tiron-Tudor, A., & Deliu, D. (2021b). Reflections on the human-algorithm complex duality perspectives in the auditing process. Qualitative Research in Accounting & Management.

Tysiac, K. (2017). How to enable audit innovation. Journal of Accountancy, 223(4), 33.

Uglum, M. K. (2021). Consideration of the ethical implications of artificial intelligence in the audit profession.

Wang, Z., & Srinivasan, R. S. (2017). A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, 75, 796-808. DOI: https://doi.org/10.1016/j.rser.2016.10.079

World Economic Forum (2015). Deep Shift: Technology Tipping Points and Societal Impact.

Zhang, Z., Chen, Z., & Xu, L. (2022). Artificial intelligence and moral dilemmas: Perception of ethical decision-making in AI. Journal of Experimental Social Psychology, 101, 104327. DOI: https://doi.org/10.1016/j.jesp.2022.104327