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Machine Learning in Auditing – Current and Future Applications

Originally published in The CPA Journal, June, 2019.

Machine learning is a key subset of artificial intelligence (AI), which originated with the idea that machines could be taught to learn in ways similar to how humans learn. While humans are just beginning to comprehend the dynamic capabilities of machine learning, the concept has been around for decades. The proliferation of data, primarily due to the rise of the Internet and advances in computer processing speed and data storage, has now made machine learning a significant component of modern life. Common examples of machine learning can be found in e-mail spam filters and credit monitoring software, as well as the news feed and targeted advertising functions of technology companies such as Facebook and Google.

Machine learning has the potential to disrupt nearly every industry during the next several years, and the auditing profession is no exception (Julia Kokina and Thomas H. Davenport, “The Emergence of Artificial Intelligence: How Automation Is Changing Auditing,” Journal of Emerging Technologies in Accounting, Spring 2017, http://bit.ly/2Heshyk). Jon Raphael, chief innovation officer at Deloitte, expects machine learning to significantly change the way audits are performed, as it enables auditors to largely “avoid the tradeoff between speed and quality” (“Rethinking the Audit,” Journal of Accountancy, Apr. 1, 2017, http://bit.ly/2Vxx7RB). 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.

Machine learning technology for auditing is still primarily in the research and development phase. Several of the larger CPA firms have machine learning systems under development, and smaller firms should begin to benefit as the viability of the technology improves, auditing standards adapt, and educational programs evolve. This article 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.

What is Machine Learning?

Machine learning is a subset of artificial intelligence that automates analytical model building. Machine learning uses these models to perform data analysis in order to understand patterns and make predictions. The machines are programmed to use an iterative approach to learn from the analyzed data, making the learning automated and continuous; as the machine is exposed to increasing amounts of data, robust patterns are recognized, and the feedback is used to alter actions. Machine learning and traditional statistical analysis are similar in many regards, but different in execution. While statistical analysis is based on probability theory and probability distributions, machine learning is designed to find the combination of mathematical equations that best predict an outcome. Thus, machine learning is well suited for a broad range of problems that involve classification, linear regression, and cluster analysis.

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About the Authors:

Gabe Dickey, CPA is an assistant professor of accounting at the University of Northern Iowa, Cedar Falls, Iowa.

Sandra Blanke, PhD is an associate professor of management–cybersecurity at the University of Dallas, Irving, Tex.

Lloyd Seaton, PhD, CPA, CMA is an associate professor of accounting at the University of Northern Colorado, Greeley, Colo.