The Impact of Artificial Intelligence on Audit Quality and Fraud Detection

Abstract

The use of Artificial Intelligence (AI) in external auditing is revolutionizing audit practices, offering greater audit quality and improved capacity to detect fraud. But the magnitude and mechanisms of such effects are not well understood. This study analyses the effects of adoption of AI on audit quality and fraud detection using survey data of 287 auditors from Big 4 and non-Big 4 firms. We theorise and test a conceptual model that suggests that professionals' judgments of the AI's use intensity relate to their perceptions of the quality of audit-related improvements and effectiveness of fraud detection, with the moderation of auditor experience, firm size, and expertise regarding AI. The measurement model has good reliability and validity. The results of the hierarchical multiple regressions show that AI adoption significantly and positively predicted audit quality (β = 0.41, p < .001) and fraud detection (β = 0.37, p < .001), accounting for 34% and 29% of the variance, respectively. Furthermore, firm size and auditors' AI training moderate the relationship between audit quality and AI adoption, as does that between AI and fraud detection for Big 4 firms. Stable results are obtained from robustness checks such as structural equation modelling and common method bias tests. The findings are among the first to show, from a large-scale empirical study, that AI is not just an efficiency tool, but is a substantive improvement to audit effectiveness, especially when it comes to complex judgmental work. Auditory practice, regulation and future research implications are discussed.

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