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摘要
En 中文
SUMMARY
Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not use this information to its full potential, resulting in a missed opportunity for possible improvements to audit quality. This article summarizes a study by Koreff (2022) that examines whether conclusions from different types of data analytical models (anomaly versus predictive) and data analyzed (financial versus non-financial) result in different auditor decisions. Findings suggest that when predictive models are used and identify a risk of misstatement, auditors increase budgeted audit hours more when financial data are analyzed than when non-financial data are analyzed. However, when anomaly models are used and identify a risk of misstatement, auditors' budgeted hours do not differ based on the type of data analyzed. These findings provide evidence that different data analytics do not uniformly impact auditors' decisions.
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