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

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Tashdidi Elaheh; SEPASI SAHAR; ETEMADI HOSSEIN; AZAR ADEL

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ACCOUNTING KNOWLEDGE Year:2019 | Volume:10 | Issue:3 (38) Page(s): 139-167

Download Full-Text

Persian Verion

View:

859

Download:

Cites:

Information Journal Paper

Title

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

Author(s)

Tashdidi Elaheh | SEPASI SAHAR | ETEMADI HOSSEIN | AZAR ADEL | Issue Writer Certificate

Keywords

Detecting of Fraud in Financial StatementsQ1

Bee AlgorithmQ1

Mutual EntropyQ1

Financial RatiosQ2

Abstract

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.

Cites

No record.

References

No record.

Cite

APA: Copy

Tashdidi, Elaheh, SEPASI, SAHAR, ETEMADI, HOSSEIN, & AZAR, ADEL. (2019). New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony. JOURNAL OF ACCOUNTING KNOWLEDGE, 10(3 (38) ), 139-167. SID. https://sid.ir/paper/163531/en

Vancouver: Copy

Tashdidi Elaheh, SEPASI SAHAR, ETEMADI HOSSEIN, AZAR ADEL. New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony. JOURNAL OF ACCOUNTING KNOWLEDGE[Internet]. 2019;10(3 (38) ):139-167. Available from: https://sid.ir/paper/163531/en

IEEE: Copy

Elaheh Tashdidi, SAHAR SEPASI, HOSSEIN ETEMADI, and ADEL AZAR, “New Approach to Predicting and Detecting Financial Statement Fraud, Using the Bee Colony,” JOURNAL OF ACCOUNTING KNOWLEDGE, vol. 10, no. 3 (38) , pp. 139–167, 2019, [Online]. Available: https://sid.ir/paper/163531/en

Related Journal Papers