Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretic...
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Islamic Azad University of Arak
2021-04-01
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doaj-00ec3aa6859840158029bd841aea35f52021-05-23T05:01:31ZengIslamic Azad University of ArakAdvances in Mathematical Finance and Applications2538-55692645-46102021-04-016237739210.22034/amfa.2019.1872783.1252670656Presenting a Model for Financial Reporting Fraud Detection using Genetic AlgorithmMahmood Mohammadi0Shohreh Yazdani1Mohammadhamed Khanmohammadi2Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran.both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.http://amfa.iau-arak.ac.ir/article_670656_198026aac1a3a0f6347d6d4820d8733e.pdffinancial reporting fraudfraud detectiongenetic algorithmdata mining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahmood Mohammadi Shohreh Yazdani Mohammadhamed Khanmohammadi |
spellingShingle |
Mahmood Mohammadi Shohreh Yazdani Mohammadhamed Khanmohammadi Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm Advances in Mathematical Finance and Applications financial reporting fraud fraud detection genetic algorithm data mining |
author_facet |
Mahmood Mohammadi Shohreh Yazdani Mohammadhamed Khanmohammadi |
author_sort |
Mahmood Mohammadi |
title |
Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm |
title_short |
Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm |
title_full |
Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm |
title_fullStr |
Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm |
title_full_unstemmed |
Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm |
title_sort |
presenting a model for financial reporting fraud detection using genetic algorithm |
publisher |
Islamic Azad University of Arak |
series |
Advances in Mathematical Finance and Applications |
issn |
2538-5569 2645-4610 |
publishDate |
2021-04-01 |
description |
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models. |
topic |
financial reporting fraud fraud detection genetic algorithm data mining |
url |
http://amfa.iau-arak.ac.ir/article_670656_198026aac1a3a0f6347d6d4820d8733e.pdf |
work_keys_str_mv |
AT mahmoodmohammadi presentingamodelforfinancialreportingfrauddetectionusinggeneticalgorithm AT shohrehyazdani presentingamodelforfinancialreportingfrauddetectionusinggeneticalgorithm AT mohammadhamedkhanmohammadi presentingamodelforfinancialreportingfrauddetectionusinggeneticalgorithm |
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1721430251069767680 |