Do Different Data Analytics Impact Auditors’ Decisions?

Abstract

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.