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 study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors' decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly impact budgeted audit hours. Specifically, 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 nonfinancial 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' judgments.

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SYNOPSIS The sentinel effect posits that the perception of increased oversight is associated with improved behavior. We consider the association between enhanced government oversight and financial reporting aggressiveness in the healthcare industry. Specifically, we examine the association between criminal cases (pending cases and successful cases) against healthcare providers and the quality of subjective accounts that require significant judgment and have been shown to be linked to healthcare earnings management—revenue accruals and the allowance for doubtful accounts. We find that heightened government oversight is associated with lower financial reporting aggressiveness.

The rise of technology-enabled data analytic tools creates opportunities for firms to improve audit quality related to complex estimates. To combat auditors’ resistance to using technology-enabled tools, firms may promote the sophistication of such tools to their audit staff. However, there is a paucity of research that has examined how auditors’ perceived sophistication of an analytic tool impacts judgments about audit evidence. We conduct an experiment and find that, holding all other information constant, the preferences of an audit supervisor interact with the perceived sophistication of an analytic tool to jointly impact auditors’ anticipated evaluation from a supervisor and, in turn, their evidence assessment decisions when auditing a complex estimate. As such, the promotion of tool sophistication by audit firms can significantly affect the audit of complex estimates to a greater degree than what would be expected. Implications for audit theory and practice are discussed.

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 vs. predictive) and data analyzed (financial vs. 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 is analyzed than when non-financial data is 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.