This research explains how machine learning works, describes its current and potential impact on the auditing profession, and presents some challenges for auditors that must be addressed for machine learning tools to reach their full capabilities. Machine learning provides the potential for significant improvements in audit speed and quality, but also entails certain risks. This research provide a general overview of machine learning, and explore current and potential future uses in the audit profession. The authors also examine the challenges that machine learning technology presents and the possible impact that machine learning will have on CPA firms and their staff.
Rather than relying primarily on representative sampling techniques, machine learning algorithms can provide firms with opportunities to review an entire population for anomalies. When audit teams can work on the entire data population, they can perform their tests in a more directed and intentional manner. In addition, machine learning algorithms can “learn” from auditors’ conclusions on specific items and apply the same logic to other items with similar characteristics.
The authors point out that while machine learning technology affords auditors a greater ability to consider internal systematic relationships and external environmental forces, auditors must also exhibit a solid understanding of the input, processing, and output of data from a broader range of sources. Although it is impossible to foretell exactly how machine learning will ultimately change the audit process, now is the time to begin contemplating its current impact and future implications.
While machine learning technology can provide significantly improved opportunities for auditors to explore their intuition, auditors must change their mode of thinking in order for these insights to be effective.
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Based upon the following peer-reviewed manuscript: Dickey, G., Blanke, S., & Seaton, L. (2019). Machine learning in auditing. The CPA Journal, 89(6), 16-21.