|
Designing an Interpretable Audit Framework for Financial Statement Risk Assessment Based on Explainable Artificial Intelligence
|
Rahim Mollaei1   , Ali Jafari *2 , Asgar Pakmaram1 , Nader Rezaei1 |
1- Department of Acconting, Bon.C., Islamic Azad University, Bonab, Iran
2- Department of Acconting, Bon.C., Islamic Azad University, Bonab, Iran , alijafari1355@iau.ir |
|
|
Abstract: (11 Views) |
The purpose of this research is to design an interpretable audit framework for assessing the risk of financial statements based on explainable artificial intelligence. This research is exploratory in terms of purpose, inductive in terms of reasoning, and qualitative in terms of the nature of the data. The statistical population comprised 39 final articles, extracted through a systematic search across academic databases (focusing on international sources from 2020 and domestic sources from 1400). Data analysis was performed using the documentary content analysis technique, and to ensure the reliability of the analyses, its quality was confirmed by calculating Cohen's kappa coefficient (0.742). The key findings of the research were categorized into four overarching codes: the concept of explainable audit based on explainable AI, antecedents, consequences, and moderating factors. The resu
lts showed that explainable AI improves audit quality and professional accountability by strengthening the connection of explanations to the risk of material misstatement and improving human judgment. Achieving these positive outcomes requires the provision of prerequisites such as technical and infrastructural readiness, professional standards, and the combined skills of auditors. Also, potential negative consequences such as technical and cognitive, professional, and ethical challenges were identified. Ultimately, the effectiveness of explainable auditing is a function of moderating factors such as management attitude, environmental pressures, and the maturity of the technology ecosystem.
|
Article number: 6 |
| Keywords: Interpretable auditing, financial statement risk, explainable artificial intelligence |
|
|
Full-Text [PDF 654 kb]
(4 Downloads)
|
Article type: Research |
Subject:
Special
Received: 2026/01/30 | Accepted: 2026/05/25 | Published: 2026/08/23
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
