The Use of Data Analytic Visualizations to Inform the Audit Risk Assessment: The Impact of Initial Visualization Form and Documentation Focus

• In Event: Auditor Reliance on Advanced Technologies, Specialists, or Both

Abstract

Public accounting firms are increasingly emphasizing the use of data analytic techniques and data visualizations for identifying anomalies in client data and assessing risk (Austin, Carpenter, Christ, and Nielson 2021; Brown-Liburd, Issa, and Lombardi 2015)). Default visualizations provided to auditors could be suboptimal in relation to the underlying data, which could limit auditors’ ability to identify anomalies without some intervention to reconfigure the visualization. We examine the effects of the initial form of a data visualization (whether optimal or suboptimal) and a documentation focus intervention on anomaly identification performance, risk assessments, and audit planning decisions. Findings indicate that auditors anchor on initially provided data visualizations even when they are in less-than-optimal form. When the initial visualization is suboptimal, anomaly identification performance suffers and leads to lower assessed risk. We find that the supporting focus documentation encourages auditors to search for and document a greater number of high-risk evidence items, spend more time interacting with the data visualizations, and more accurately identify anomalies when provided an initially less-than-optimal visualization. Overall, this study should be of interest to public accounting firms and standard setters seeking to improve audit quality and efficiency, particularly when using data analytic visualizations during risk assessment.

Authors

• Rebecca Baaske, University of Tampa

• Uday S Murthy, University of South Florida