Practitioner Summary : Do Different Data Analytic Inputs Impact Auditors’ Decisions?

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 uniformly incorporate this information into their decision making. This article summarizes a recent study by Koreff (2021) that examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. non-financial), result in different auditor decisions. Findings suggest that, when financial data is analyzed, auditors increase budgeted audit hours more when predictive models are used than when anomaly models are used. The opposite occurs when non-financial data is analyzed. Auditors increase budgeted audit hours more when anomaly models are used compared to predictive models. These findings provide initial evidence that data analytics with different inputs do not uniformly impact auditors’ decisions