Optimizing Benish's fraud model in forecasting Restatement of Financial Statements Using a combination of neural network and Genetic Algorithm

Document Type : Original Article

Authors
Sasan Mehrani 1
akbar rahimi poor 2
1 Associate Professor of Accounting, University of Tehran, Tehran, Iran.
2 Ph.D. Student in Accounting, Tehran University Alborz Campus, Tehran, Iran.
Abstract
Restatement of Financial Statements , which means re-presenting last year's financial information in order to correct incorrect information or increase comparability, is one of the common phenomena in Iran, which causes investors to lose confidence in the credibility and competence of management and the quality of reported profits. The purpose of this research is to present the development model of Benish fraud in companies admitted to the Tehran Stock Exchange between 2008 and 2019. The data of 265 companies were extracted using the selected and final indicators of Benish's (1999) fraud model, and neural network and genetic algorithms were also used to develop the prediction model. The results of the research indicate that based on the confusion matrix, the accuracy and efficiency of the improved Benish model with the combined algorithm of neural network and genetics in predicting unrepresented companies was 78.15%, and re-presented companies was 29.63%. . Also, the overall accuracy of the model presented with the combined algorithm of neural network and genetics was 73.21%.
Keywords
Restatement of financial statements
annual adjustments
prediction
Benish model
genetic algorithm
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