Source : CPA Journal (https://www.cpajournal.com/2020/07/16/icymi-machine-learning-in-auditing/)

Machine learning provides the potential for significant improvements in audit speed and quality. 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. 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 technology for auditing is still primarily in the research and development phase. Although there are limitations to the current capabilities of machine learning, it excels at performing repetitive tasks. Because an audit requires a vast amount of data and has a significant number of task-related components, machine learning has the potential to increase both the speed and quality of audits. The machine-based performance of redundant tasks should allow auditors more time for review and analysis, which would give them a greater ability to focus on the areas of greatest risk, as well as a better understanding of the larger picture.

Among the functions of Machine Learning technology that can be applied in auditing are:-

• The use of Machine Learning in the management of contract documents to analyze the format, terms of the contract and identify the classification of capital or operational expenditure which allows auditors to focus on high-risk contracts;
• The use of Machine Learning in the analysis of accounting journals to identify errors or accounting manipulations that allow auditors to test every journal entry made by the company in a particular year; by subjecting all journal entries to the test and focusing only on high-risk transactions;
• The use of Machine Learning in fraud interview process to analyze the accuracy of the information conveyed based on the use of words, facial reactions and others; and
• The use of Machine Learning in identifying potential revenue generation by analyzing customer, product and sales information to ensure there is no fraud in financial reporting.

Date of Input: 25/07/2022 | Updated: 25/07/2022 | nurmiera