Deep Learning Algorithms and Audit Risk Analysis: A Novel Approach to Supporting Auditors’ Professional Judgment

Subject Areas : Behavioral reactions in the capital market

Rasha Mhmood Ali ¹ , Hamidreza Azizi ^{2 *} , Siraj Razooqi Abbas ³ , rahman saedi ⁴

1 - PhD. student, Department of Accounting, Faculty of International Studies, Isfahan(Khorasgan) Branch , Islamic Azad University, Isfahan, Iran.
2 - Department of Accounting, Ard.C., Islamic Azad University, Ardebil, Iran
3 - Department of Accounting, Faculty of Finance and Accounting, Wasit Provincial University, Iraq
4 - Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

Received: 2025-06-29 Accepted : 2025-07-29 Published : 2025-08-11

Keywords: Audit Risk Prediction, Deep Learning, Perfesional Behaviore, Support Vector Machine, Convolutional Neural Network, Recurrent Neural Network,

Abstract :

This study aims to develop an effective model for predicting audit risk using deep learning algorithms and to examine its implications for enhancing auditors’ professional judgment. To achieve this, three advanced algorithms—Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)—were applied to financial and non-financial data from 150 firms listed on the Tehran Stock Exchange over the period 2013–2023. Audit risk was operationalized as a binary variable, reflecting the occurrence of Type I and Type II audit errors. Significant predictors of audit risk were selected using a two-sample mean comparison test. The predictive performance of the deep learning models was then evaluated and compared. The results indicate that the RNN model achieved the highest accuracy (96.4%), followed by SVM (89.6%) and CNN (85.8%). These findings suggest that deep learning algorithms—particularly RNNs—can effectively detect complex patterns and temporal dependencies in financial data, thereby serving as powerful tools to support auditors in making informed and ethical judgments. This study contributes to the integration of artificial intelligence into the audit domain and offers practical insights for improving audit risk assessment, reducing judgmental errors, and strengthening stakeholder trust in financial reporting

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