Developing a Comprehensive Model of AI-Based Audit Report Accuracy

Mohammad Amin  Ghanbari Tafreshi; Mohsen Hamidian; Seyedeh Mahboobeh  Jafari

Authors

Mohammad Amin Ghanbari Tafreshi Department of Accounting, ST.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0001-8420-2130
Mohsen Hamidian Department of Accounting, ST.C., Islamic Azad University, Tehran, Iran https://orcid.org/0000-0001-8993-4552
Seyedeh Mahboobeh Jafari Department of Accounting, ST.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0002-1748-852X

Keywords:

Artificial intelligence, audit report accuracy, audit quality, professional judgment, human oversight, digital auditing, organizational readiness, AI governance

Abstract

This study aimed to develop and empirically validate a comprehensive model of artificial intelligence-based audit report accuracy by integrating technological, informational, human, organizational, governance, ethical, and professional determinants. An exploratory sequential mixed-methods design was employed in Tehran, Iran. In the qualitative phase, 18 experts in auditing, accounting, financial reporting, accounting information systems, and artificial intelligence were purposively selected and interviewed using semi-structured interviews until theoretical saturation was achieved. Qualitative data were analyzed through systematic coding and thematic categorization to identify the principal dimensions of AI-based audit report accuracy. In the quantitative phase, a researcher-developed questionnaire derived from the qualitative findings was administered to auditing, accounting, and financial professionals. Of 420 distributed questionnaires, 384 valid responses were retained for analysis. Reliability and validity of the measurement model were assessed using factor loadings, Cronbach's alpha, rho_A, composite reliability, average variance extracted, and the heterotrait-monotrait ratio. Partial least squares structural equation modeling with bootstrapping was used to test the proposed structural relationships. AI technological reliability (β = 0.267, p < 0.001), data quality and integrity (β = 0.241, p < 0.001), organizational readiness (β = 0.156, p < 0.001), explainability and transparency (β = 0.121, p = 0.005), AI governance and ethical-regulatory compliance (β = 0.118, p = 0.007), and information security and system controls (β = 0.103, p = 0.012) significantly predicted AI-assisted audit process quality. Auditor digital competence (β = 0.286, p < 0.001), AI governance (β = 0.249, p < 0.001), and explainability (β = 0.203, p < 0.001) significantly predicted human oversight and professional judgment. AI-assisted audit process quality (β = 0.468, p < 0.001) and human oversight (β = 0.329, p < 0.001) significantly predicted audit report accuracy. The interaction between these two factors was also significant (β = 0.097, p = 0.015). The final model explained 72.4% of the variance in audit report accuracy. Accurate AI-based audit reporting depends on the coordinated functioning of reliable technology, high-quality data, organizational readiness, governance safeguards, auditor digital competence, and sustained professional human oversight rather than on technological automation alone.

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