Using Artificial Neural Networks in Improving the Efficiency of External Auditors in Detecting Financial Fraud | ||
| مجلة بحوث الأعمال | ||
| Volume 2, Issue 2 - Serial Number 3, July 2025, Pages 1447-1506 PDF (1.22 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/abs.2025.402428.1051 | ||
| Authors | ||
| هدير هشام السيد إبراهيم* ; سماح طارق أحمد حافظ; دنيا سمير محمود عبدالرازق | ||
| كلية التجارة جامعة المنصورة | ||
| Abstract | ||
| With the growing sophistication of financial fraud and the limitations of traditional audit methods, there is an urgent need to adopt artificial intelligence to enhance external auditors' efficiency in detecting fraud, especially in light of the scarcity of applied evidence on this impact in the Egyptian context. The research aimed to evaluate the impact of using artificial neural networks on improving the efficiency of external auditors in detecting financial fraud, through examining the impact of adopting artificial neural networks on external auditing and the impact of artificial neural networks on the efficiency of external auditors in detecting financial statement fraud. The research relies on a sample of non-financial companies listed on the Egyptian Stock Exchange, which number 126 companies in different sectors. This study depends on 1000 firm-year observations from the Egyptian environment through the period 2012 to 2022. The researchers found that artificial neural networks adoption has a significant impact on the external auditing by the auditor's opinion, and adopting artificial neural networks has a significant impact on the efficiency of external auditors by the audit fees and audit report lag in detecting financial fraud. | ||
| Keywords | ||
| Artificial neural networks; Deep neural network; Efficiency of External Auditors; Auditors’ opinion; Detecting Financial Fraud | ||
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