Data visualization and cognitive biases in audits

Chengyee Janie Chang and Yan Luo

Managerial Auditing Journal, 2019, vol. 36, issue 1, 1-16

Abstract: Purpose - This paper aims to examine major cognitive biases in auditors’ analyses involving visualization, as well as proposes practical approaches to address such biases in data visualization. Design/methodology/approach - Using the professional judgment framework of KPMG (2011), this study performs an analysis of whether and how five major types of cognitive biases (framing, availability, overconfidence, anchoring and confirmation) may occur in an auditor’s data visualization and how such biases potentially compromise audit quality. Findings - The analysis suggests that data visualization can trigger and/or aggravate the common cognitive biases in audit. If not properly addressed, such biases may adversely affect auditors' judgment and decision-making. Practical implications - To ensure that data visualization improves audit efficiency and effectiveness, it is essential that auditors are aware of and successfully address cognitive biases in data visualization. Six practical approaches to debias cognitive biases in auditors’ visualization are proposed: using data visualization to complement rather than supplement traditional audit evidence; positioning data visualization to support rather than replace sophisticated analytics tools; using a dashboard with multiple dimensions; using both visualized and tabular data in analyses; assigning experienced audit staff; and providing pre-audit tutorials on cognitive bias and visualization. Originality/value - The study raises awareness of psychological issues in an audit setting.

Keywords: Cognitive bias; Audit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:majpps:maj-08-2017-1637

DOI: 10.1108/MAJ-08-2017-1637

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