“The impact of Generative Artificial Intelligence on the Relationship between political information uncertainty and Audit Fees” | ||
| مجلة البحوث المحاسبية | ||
| Volume 12, Issue 3, September 2025, Pages 98-110 PDF (496.98 K) | ||
| DOI: 10.21608/abj.2025.447674 | ||
| Authors | ||
| Safaa Mohammed El Halawany1; Shehata Elsayed Shehata2 | ||
| 1Assistant Lecturer، Accounting and Auditing department High Institute for commercial sciences ، Abi Qir Alexandria،Egypt | ||
| 2Professor،Department of Accounting and Auditing، Faculty of Commerce،Alexandria University،Egypt | ||
| Abstract | ||
| This working paper explores the moderating role of Generative Artificial Intelligence (GAI) on the relationship between political information uncertainty and audit fees. Political shocks, characterized by sudden and significant changes in political stability, policy, or governance, have historically influenced investor behavior and market dynamics, which in turn affects audit risk and the associated fees. The integration of GAI into audit processes offers advanced analytical tools that can enhance the assessment of political risk and its impact on financial reporting. GAI introduces a new dimension to this relationship by providing advanced predictive analytics, sentiment analysis, and scenario modeling capabilities. Through a comprehensive analysis of recent political events and corresponding investment patterns, this working paper examines how GAI-driven insights can mitigate the adverse effects of political uncertainty on audit fees. It also examines how GAI-driven insights can mitigate the effects of political uncertainty on audit practices, not only improving the accuracy of risk assessments but also contributes to more stable audit fee structures. Overall, political shocks introduce additional complexities and uncertainties into the audit process, requiring auditors to adapt their practices to ensure the accuracy and reliability of financial reporting. | ||
| Keywords | ||
| Generative Artificial Intelligence; Political information; uncertainty; External audit; Audit fees | ||
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