Not too long ago the audit profession used large amounts of paper and boxes to document their audit procedures, but now the profession is in the early stages of using artificial intelligence to perform certain tasks. Does this mean the end to the auditing professional?
Artificial intelligence (AI) has made its entrance into the audit profession and is expected to rapidly increase its presence as companies create tools to perform tasks which have typically been given to less experienced team members. The current application of AI is called “Machine Learning.” Machine Learning is based on the idea that audit professionals can give machines data and let them “learn” for themselves. Some of the current applications for these machines are natural language processing capabilities to interpret contracts/deeds, using drones with image recognition to analyze inventory, and the ability to evaluate credit information related to financial institutions loan portfolios. Future expected applications are financial forecasting, financial statement preparation, typical day-to-day accounting functions, and much more. Machine Learning can perform many routine tasks at a much faster rate than any accountant. As such, the whole population or dataset can be effectively analyzed and understood by the audit process which can lead to a higher quality audit instead of sampling a snapshot of data which is the current norm. Although this sounds like a perfect plan there some drawbacks. With larger amounts of information being analyzed there will be more anomalies uncovered which would lead to increased work for auditors. In addition, all of the machine learning tools used to analyze financial statements and build predictive models are based on historical data which could potentially cause large issues as the system would inherit the bias, or potentially fraud, embedded in the data and it would “learn” that these are typical transactions.
Is Machine Learning going to be the end of the audit professional?
In short, no. Machines will always be more efficient at sifting through enormous volumes of data but it takes a human to understand and correct for new circumstances. Auditors should use technology to leverage tasks that can be automated and enable them to strengthen their techniques, thereby leading to higher quality audits. It is clear that humans and machines will not be direct competitors but rather teammates working together for a more precise and efficient audit.
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