Data visualization and cognitive biases in audits

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.

Unfolding irrationality: how do meaningful coincidences influence management decisions?

“…In this case, the decision-maker, because of collecting confirming cues, feels in control of the meaningful randomness (Friedland, 1998) and assigns a positive symbolic content to the occurrence of the synchronistic events, that suggests continuing on the current path (E) (Pinger et al, 2018). This has also been recently supported by the study of Chang and Luo (2019) who found, through the investigation of biases in auditing activities, that data visualization used when performing auditing activities interprets data favorably, leading auditors to the confirmation bias, and to be sure about their understanding of the accounting situation.…”

“…The elicited positive (negative) emotional answer subsequently activates a series of cognitive errors that drive the assignment of a symbolic content, i.e. continuing (or not) on the current path (Patalano et al, 2015;Chang and Luo, 2019), to the coincidences, bringing high (low) risk-oriented management decisions. The affective states perceived at the end of this process will reinforce, in a co-evolutionary way, the original affective base (Cristofaro, 2019).…”

A Qualitative Exploration of Heuristics and Cognitive Biases in Auditor Judgements

“…First, unlike previous auditor judgement studies that examine determinants and consequences of variations in auditor judgements (Choudhary et al, 2019;Kadous and Zhou, 2019;Kim et al, 2017;Lambert and Peytcheva, 2020;Sanusi et al, 2018;Yang et al, 2018), this study focuses on examining biases in the judgements of auditors that are caused by the use of mental shortcuts by auditors when making judgements. Second, this study differs from previous audit bias studies (e.g., Chang and Luo, 2019;Guiral et al, 2015) in that this study focuses on exploring the types of biases that are commonly experienced by auditors, particularly in the context of making professional judgements.…”

“…The professional judgements of auditors are prone to biases because these judgments are made subjectively by auditors in situations where the auditors have certain direct associations with their clients, and where the auditors face various limitation such as vague auditing standards and limited time (Bettinghaus et al, 2014). Because bias can impair the accuracy of auditors' professional judgments (Bettinghaus et al, 2014;Chang and Luo, 2019), it is therefore crucial to identify the range of biases that can affect the judgments of auditors and the ways through which auditors can overcome these biases.…”

“…For example, (Guiral et al, 2015) examine the impact of expertise on auditors' reporting bias in relation to the going concern of client-companies. A more recent study by Chang and Luo (2019) investigates how the use of data visualisation during an audit process can lead to cognitive biases in the part of auditors. While these studies are interesting since they provide some evidence of determinants of auditors' biases, they do not specifically identify the possible biases that can be experienced by auditors when exercising judgments.…”