CURRENT MACHINE LEARNING APPLICATIONS IN ACCOUNTING AND AUDITING

Year 2020, Volume: 12 Issue: 1, 1 - 7, 31.12.2020

Derya Ucoglu

https://doi.org/10.17261/Pressacademia.2020.1337

Abstract

Purpose- Machine learning is an area of computer science that learns from large amounts of data, identifies patterns, and makes predictions about future events. In the accounting and auditing professions, machine learning has been increasingly used in the last few years. Therefore, this study aims to review the current machine learning applications in accounting and auditing with a concentration on Big Four companies.
Methodology- In this study, the machine learning tools and platforms developed by Big Four companies are examined by conducting a content analysis.
Findings- It has been identified that Big Four companies developed several machine learning tools that are used for consistent audit coordination and management, fully automated audits (only in certain areas, such as cash audit), data analysis, risk assessment, and extracting information from documents.
Conclusion- To benefit from the advantages, the Big Four companies are still expanding their portfolio of machine learning projects. On the other hand, the ethical problems and potential risks of security and violating privacy regulations by using machine learning applications in accounting and auditing should also be considered. This rapid transformation in the profession also creates a need for ethical and regulatory guidance and oversight for accounting and auditing companies.

Keywords

Machine learning, accounting, auditing, big four companies, artificial intelligence

References

• Ahmed, A. (2020). “From Data to Wisdom” Using Machine Learning Capabilities in Accounting and Finance Professionals. Talent Development & Excellence, 12(3), 2019-2036.
• Alarcon, J. L., Fine, T. & Ng, C. (2019). Accounting AI and Machine Learning: Applications and Challenges. Pennsylvania CPA Journal, 2019 Special Issue, 1-5.
• Anandarajan, M. & Anandarajan, A. (1999). A comparison of machine learning techniques with a qualitative response model for auditor’s going concern reporting. Expert Systems with Applications, (16), 385-392.
• Bao, Y., Ke, B., Li, B., Yu, J. & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199-235.
• Bhavsar, P., Safro, I., Bouaynaya, N., Polikar, R. & Dera, D. (2017). Machine Learning in Transportation Data Analytics. In M. Chowdhury, A. Apon, & K. Dey (Eds.), Data Analytics for Intelligent Transportation Systems (pp. 283–307). Elsevier Inc.
• 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.
• Davenport, T. H. (2016). Deloitte - The power of advanced audit analytics. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-da-advanced-audit-analytics.pdf (Accessed: 6 Oct. 2020).
• Deloitte (2018). 16 Artificial Intelligence projects from Deloitte - Practical cases of applied AI. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/innovatie/deloitte-nl-innovatie-artificial-intelligence-16-practical-cases.pdf (Accessed: 7 Oct. 2020).
• Deloitte, CortexAI. Retrieved from https://www2.deloitte.com/us/en/pages/consulting/topics/cortex-ai-platform.html (Accessed: 10 Oct. 2020).
• Deloitte (2017). Delivering smarter audits - Insights through innovation. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/audit/us-audit-smarter-audits-dynamic-insights-through-innovation.pdf (Accessed: 6 Oct. 2020).
• Deloitte (2018). Deloitte Signal. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/audit/deloitte-cn-audit-innovation-product-intro-deloitte-signal-en-191119.pdf (Accessed: 10 Oct. 2020).
• Deloitte, Press releases - . Deloitte Wins 2020 'Audit Innovation of the Year' at the Digital Accountancy Forum & Awards. Retrieved from https://www2.deloitte.com/us/en/pages/about-deloitte/articles/press-releases/deloitte-wins-2020-audit-innovation-of-the-year-at-digital-accountancy-forum-awards.html (Accessed: 24 Oct. 2020).
• Deng, C., Ji, X., Rainey, C., Zhang, J. & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. iScience, 1-46 (Journal Pre-proof). https://doi.org/10.1016/j.isci.2020.101656.
• Ding, K., Peng, X., & Wang, Y. (2019). A Machine Learning-Based Peer Selection Method with Financial Ratios. Accounting Horizons, 33(3), 75-87.
• Dogan, A. & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications, (166), 1-22.
• EY, Audit Innovation. Retrieved from https://www.ey.com/en_gl/audit/innovation (Accessed: 21 Oct. 2020).
• EY Canvas. Retrieved from https://www.ey.com/en_gl/audit/technology/canvas (Accessed: 21 Oct. 2020).
• EY Helix. Retrieved from https://www.ey.com/en_gl/audit/technology/helix (Accessed: 21 Oct. 2020).
• Faggella, D. (2020). AI in the Accounting Big Four – Comparing Deloitte, PwC, KPMG, and EY. Retrieved from https://emerj.com/ai-sector-overviews/ai-in-the-accounting-big-four-comparing-deloitte-pwc-kpmg-and-ey/ (Accessed: 8 Oct. 2020).
• Haq, I., Abatemarco, M. & Hoops, J. (2020). The Development of Machine Learning and its Implications for Public Accounting. CPA Journal, 90(6), 6-9.
• ICAEW (2018). Artificial intelligence and the future of accountancy. Retrieved from https://www.icaew.com/-/media/corporate/files/technical/information-technology/thought-leadership/artificial-intelligence-report.ashx?la=en (Accessed: 22 Oct. 2020).
• IIA. (2017). Küresel Bakış Açıları ve Anlayışlar: Yapay Zeka - İç Denetim Mesleğine İlişkin Dikkate Alınması Gerekenler, Kısım I. Retrieved from https://www.tide.org.tr/file/documents/pdf/GPAI-Artificial-Intelligence-Part-I-Revised.pdf (Accessed: 2 Sept. 2020).
• International Accounting Bulletin, Events Archive. Retrieved from http://www.internationalaccountingbulletin.com/events-archive (Accessed: 20 Oct. 2020).
• 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.
• Kotsiantis, S., Koumanakos, E., Tzelepis, D. & Tampakas, V. (2006). Forecasting Fraudulent Financial Statements using Data Mining, International Journal of Computational Intelligence, 3(2), 104-110.
• KPMG Clara. Retrieved from https://home.kpmg/xx/en/home/services/audit/kpmg-clara.html (Accessed: 18 Oct. 2020).
• Lahann, J., Scheid, M. & Fettke, P. (2019). Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data. 2019 IEEE 21st Conference on Business Informatics (CBI), 1-10.
• Lokanan, M. & Tran, V. (2019). Detecting anomalies in financial statements using machine learning algorithm The case of Vietnamese listed firms. Asian Journal of Accounting Research, 4(2), 181-201.
• Munoko, I., Brown‑Liburd, H. L. & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 1-26.
• PwC, Halo for Employee Expenses. Retrieved from https://www.pwc.com/us/en/services/tax/tax-innovation/halo-for-employee-expenses.html (Accessed: 15 Oct. 2020).
• PwC, Harnessing AI to pioneer new approaches to the audit. Retrieved from https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-ai-to-pioneer-new-approaches-to-the-audit.html (Accessed: 15 Oct. 2020).
• PwC, 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-to-transform-the-detection-of-fraud-and-error.html (Accessed: 15 Oct. 2020).
• PwC, Machine learning: what every risk and compliance professional needs to know. Retrieved from https://www.pwc.com/us/en/services/forensics/library/machine-learning-risk-compliance.html (Accessed: 15 Oct. 2020).
• PwC, The PwC Audit. Retrieved from https://www.pwc.com/gx/en/services/audit-assurance/the-pwc-audit.html (Accessed: 15 Oct. 2020).
• Shimamoto, D. C. (2018). Why Accountants Must Embrace Machine Learning. Retrieved from https://www.ifac.org/knowledge-gateway/preparing-future-ready-professionals/discussion/why-accountants-must-embrace-machine-learning (Accessed: 24 Sept. 2020).
• Song, X., Hu, Z., Du, J. & Sheng, Z. (2014). Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China. Journal of Forecasting, (33), 611-626.
• Taniguchi, H., Sato, H. & Shirakawa, T. (2018). A machine learning model with human cognitive biases capable of learning from small and biased datasets. Scientific Reports, (8), 1-13.
• Türegün, N. (2019). Impact of technology in financial reporting: The case of Amazon Go. Journal of Corporate Accounting & Finance, 30(3), 90-95.
• Zemankova, A. (2019). Artificial Intelligence in Audit and Accounting: Development, Current Trends, Opportunities and Threats – Literature Review. 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 148-154