The Effect of Auditor Dependence on Hybrid Models From Artificial Intelligence Tools on Enhancing Auditor Going Concern Opinion Quality: An Applied Study on a Sample of Egyptian Stock Listed Companies

Document Type : Original Article

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Accounting Department Faculty of Commerce Damnhour University Alexandria Egypt

Abstract

The Research aims to study and test The Effect of Auditor Dependence on Hybrid Models From Artificial Intelligence Tools on Enhancing Auditor Going Concern Opinion Quality: An Applied Study on a Sample of Egyptian Stock Listed (EGX) Companies during the period from 2018 to 2021.
The results of fundamental analysis concluded that there is a significant effect for using Evolutionary Search (ES) algorithms for modeling the final model for predicting auditor going concern opinion, in addition to the positive effect and increase in the final model prediction accuracy rate using different artificial intelligence tools, where the random forest algorithm achieved the highest rate, followed by K-Nearest Neighbor (KNN) and finally Artificial Neural Network Algorithm (ANN) compared to traditional analysis tools, mainly logistic regression.
According to the prediction accuracy analysis for 2021, the research found that the artificial intelligence tools achieved the highest prediction rate for the auditor.
Based on the Predication Accuracy Analysis for 2021, the research found that Auditor Dependence on Hybrid Models From Artificial Intelligence Tools achieved the highest predication rate for Auditor Going Concern Opinion for Egyptian companies for 2021 compared with Logistic Regression, which assures the positive effect of using Artificial Intelligence tools on the Auditor Going Concern Predication Quality.

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