A Hybrid Model for Supporting Auditors' Professional Judgment in Going Concern Evaluation Using Traditional Techniques and AI-Based Big Data Analytics | ||||
| المجلة العلمية للدراسات والبحوث المالية والتجارية | ||||
| Volume 6, Issue 2 - Serial Number 1, July 2025, Page 807-870 PDF (2.07 MB) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/cfdj.2025.390116.2283 | ||||
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| Authors | ||||
Mohamed Essam Tamam Osman  ; Yasser Mohamed Abdelaziz Samra
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| Faculty of Commerce, Damietta University | ||||
| Abstract | ||||
| This study proposes a hybrid model that integrate the Altman Z-score— A traditional financial distress prediction Techniques -with six AI based Big Data Analytics (Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) to enhance the professional judgment of external auditors in evaluating an entity’s going-concern status. The model was empirically tested on a sample of 144 non-financial firms listed on the Egyptian Stock Exchange from 2018 to 2023. The findings indicate that although the Altman Z-score provides valuable insights into assessing an entity’s going-concern status, the Hybrid Model consistently outperforms the predictive performance of both the standalone Altman model and individual AI-based Big Data Analytics (BDA) techniques. The traditional Altman model achieves an accuracy of 84%. All Hybrid models exceed this baseline, with the Decision Tree (DT) model performing best at 94%, followed by the Deep Neural Network (DNN) at 92%, and the Recurrent Neural Network (RNN) at 91%, indicating that Hybrid models provide more reliable overall classifications. Also, Statistical tests, including McNemar, Phi, Cramer’s V, Kappa, -2log likelihood, and Nagelkerke R Square, consistently supported the effectiveness of the Hybrid Model. These findings highlight the potential of hybrid models to significantly elevate the quality of auditors’ professional judgment and decision-making in going-concern evaluations. | ||||
| Keywords | ||||
| Traditional Techniques; AI-based Big Data Analytics; Auditor’s Professional Judgment regarding the Entity’s Going Concern | ||||
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