The use of multi-layer feedforward artificial neural network in detecting fraudulent financial reporting in companies admitted to the Tehran Stock Exchange.
Purpose: The purpose of the article is to use the multi-layer feedforward neural network to detect fraudulent financial reporting in companies listed on the Tehran Stock Exchange. According to auditing standard 240, fraud is any deliberate action by executives, employees, senior managers and third parties that causes deception in order to obtain undue benefits.
Methodology: The statistical method used in this research is multilayer feedforward neural network (sigmoid logarithm). After applying some limitations in this research, the statistical population of the research includes 520 companies admitted to the Tehran Stock Exchange during the period of 2019.
Findings: The research results are positive regarding the performance of artificial neural network in detecting fraudulent financial reporting. According to the developed ANN model, it can detect fraudulent financial reporting in financial statements.
Knowledge enhancement: The findings of this research contribute to the literature on the methods of detecting signs of fraud in financial statements, and it can also be used to help the auditor's role in detecting significant distortions attributed to fraud.
Jafari, M., rezaei, N., & solgi, M. (2023). The use of multi-layer feedforward artificial neural network in detecting fraudulent financial reporting in companies admitted to the Tehran Stock Exchange.. Scientific journal of motaher auditing studies, 1(2), 93-113.
MLA
Mohammad Jafari; neda rezaei; mohammad solgi. "The use of multi-layer feedforward artificial neural network in detecting fraudulent financial reporting in companies admitted to the Tehran Stock Exchange.". Scientific journal of motaher auditing studies, 1, 2, 2023, 93-113.
HARVARD
Jafari, M., rezaei, N., solgi, M. (2023). 'The use of multi-layer feedforward artificial neural network in detecting fraudulent financial reporting in companies admitted to the Tehran Stock Exchange.', Scientific journal of motaher auditing studies, 1(2), pp. 93-113.
VANCOUVER
Jafari, M., rezaei, N., solgi, M. The use of multi-layer feedforward artificial neural network in detecting fraudulent financial reporting in companies admitted to the Tehran Stock Exchange.. Scientific journal of motaher auditing studies, 2023; 1(2): 93-113.
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