Auditing with Data and Analytics: External Reviewers' Judgments of Audit Quality and Effort

Abstract

Audit firms increasingly rely on audit approaches using data and analytics (D&A) tools but express concern that external reviewers will excessively scrutinize such approaches. We conduct two experiments in which experienced external reviewers participate in an engagement review. We manipulate whether the audit team employed D&A or traditional audit procedures, while holding constant the procedures’ level of assurance. Our first experiment provides evidence that external reviewers judge D&A audit procedures as lower in quality than traditional audit procedures. Further analyses suggest external reviewers rely on the effort heuristic, judging D&A procedures as lower in quality because they entail less effort. Our second experiment evaluates a theory-based intervention that reduces reviewers’ reliance on the effort heuristic, causing them to judge audit quality similarly across D&A and traditional audit procedures. Overall, our evidence substantiates auditors’ concerns, identifies a specific cause for the concern, and introduces a theory-based intervention that addresses the concern.

Keywords: data and analytics, external reviewer, audit quality; effort heuristic

JEL Classification: M41; M42