New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony

Objective: Considering complex financial plans to conceal fraud in financial statements, the development of fraud detection methods can be regarded as solution for this problem. The present study uses the bee algorithm to develop methods for fraud detection in financial statements. Method: Three met...

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Autores principales:	Elaheh Tashdidi, Sahar Sepasi (Ph.D), Hossein Etemadi (Ph.D), Adel Azar (Ph.D)
Formato:	article
Lenguaje:	FA
Publicado:	Shahid Bahonar University of Kerman 2019
Materias:	detecting of fraud in financial statements bee algorithm mutual entropy financial ratios Accounting. Bookkeeping HF5601-5689
Acceso en línea:	https://doaj.org/article/21372014cd4749318123e55d51343af7
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spelling	oai:doaj.org-article:21372014cd4749318123e55d51343af72021-11-04T19:54:57ZNew Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony2008-89142476-292X10.22103/jak.2019.13616.2927https://doaj.org/article/21372014cd4749318123e55d51343af72019-11-01T00:00:00Zhttps://jak.uk.ac.ir/article_2378_816c0f68474642d16a4051d2c963582a.pdfhttps://doaj.org/toc/2008-8914https://doaj.org/toc/2476-292XObjective: Considering complex financial plans to conceal fraud in financial statements, the development of fraud detection methods can be regarded as solution for this problem. The present study uses the bee algorithm to develop methods for fraud detection in financial statements. Method: Three methods of bee algorithm, genetic algorithm and logistic regression have been used to study the subject. The statistical sample consists of 120 companies accepted in the Tehran Stock Exchange (60 companies are suspected of fraud and 60 ones are not suspected) for the period 1396-1385. The companies were suspected of fraud, based on 1) revised audit opinion after unacceptable expression, 2) existence of significant annual revisions, and revised financial statements for inventories and other assets; 3) existence of tax disputes with the tax area, according to notes on income tax filing, general tax filings and conditioned clauses in audit reports. Following the use of cross-entropy, 16 financial ratios were introduced as the potential predictors of fraudulent financial reporting. Result: The results showed that the bee algorithm method with prediction accuracy of 82.5% has better performance in identifying suspicious companies in fraudulent financial statements than the other two methods. Conclusion: The results of the research indicate that the proposed method of this study compared to other methods has higher rate of prediction accuracy, lower error rate and relatively good speed.Elaheh TashdidiSahar Sepasi (Ph.D)Hossein Etemadi (Ph.D)Adel Azar (Ph.D)Shahid Bahonar University of Kermanarticledetecting of fraud in financial statementsbee algorithmmutual entropyfinancial ratiosAccounting. BookkeepingHF5601-5689FAمجله دانش حسابداری, Vol 10, Iss 3, Pp 139-167 (2019)
institution	DOAJ
collection	DOAJ
language	FA
topic	detecting of fraud in financial statements bee algorithm mutual entropy financial ratios Accounting. Bookkeeping HF5601-5689
spellingShingle	detecting of fraud in financial statements bee algorithm mutual entropy financial ratios Accounting. Bookkeeping HF5601-5689 Elaheh Tashdidi Sahar Sepasi (Ph.D) Hossein Etemadi (Ph.D) Adel Azar (Ph.D) New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
description	Objective: Considering complex financial plans to conceal fraud in financial statements, the development of fraud detection methods can be regarded as solution for this problem. The present study uses the bee algorithm to develop methods for fraud detection in financial statements. Method: Three methods of bee algorithm, genetic algorithm and logistic regression have been used to study the subject. The statistical sample consists of 120 companies accepted in the Tehran Stock Exchange (60 companies are suspected of fraud and 60 ones are not suspected) for the period 1396-1385. The companies were suspected of fraud, based on 1) revised audit opinion after unacceptable expression, 2) existence of significant annual revisions, and revised financial statements for inventories and other assets; 3) existence of tax disputes with the tax area, according to notes on income tax filing, general tax filings and conditioned clauses in audit reports. Following the use of cross-entropy, 16 financial ratios were introduced as the potential predictors of fraudulent financial reporting. Result: The results showed that the bee algorithm method with prediction accuracy of 82.5% has better performance in identifying suspicious companies in fraudulent financial statements than the other two methods. Conclusion: The results of the research indicate that the proposed method of this study compared to other methods has higher rate of prediction accuracy, lower error rate and relatively good speed.
format	article
author	Elaheh Tashdidi Sahar Sepasi (Ph.D) Hossein Etemadi (Ph.D) Adel Azar (Ph.D)
author_facet	Elaheh Tashdidi Sahar Sepasi (Ph.D) Hossein Etemadi (Ph.D) Adel Azar (Ph.D)
author_sort	Elaheh Tashdidi
title	New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
title_short	New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
title_full	New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
title_fullStr	New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
title_full_unstemmed	New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony
title_sort	new approach to predicting and detecting financial statement fraud, using the bee colony
publisher	Shahid Bahonar University of Kerman
publishDate	2019
url	https://doaj.org/article/21372014cd4749318123e55d51343af7
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New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony

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