The Detection of Taxpayers with False Invoices using Data Mining Techniques

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.

GHORBANI ABOLFAZL; LAYEGHI KAMRAN; davoudi fatemeh

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

Journal Paper

Paper Information

Journal: Journal of Tax Research Year:2017 | Volume:25 | Issue:33 (81) Page(s): 167-193

Download Full-Text

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

Persian Verion

View:

788

Download:

Cites:

Information Journal Paper

Title

The Detection of Taxpayers with False Invoices using Data Mining Techniques

Author(s)

GHORBANI ABOLFAZL | LAYEGHI KAMRAN | davoudi fatemeh | Issue Writer Certificate

Keywords

False InvoicesQ1

Fraud DetectionQ1

Data MiningQ2

ClusteringQ2

Neural NetworksQ2

Gas Neural NetworksQ1

Abstract

In this paper, we present indices by which it is possible to characterize and detect those potential users of False Invoices in a given year, depending on the information of their tax payment, their historical performance and characteristics, using different types of Data Mining techniques. In this research first, Clustering algorithms like Self-Organizing Map (SOM) and neural gas networks are used to identify groups of similar behaviors of taxpayers. Then decision trees, Neural Networks and Bayesian networks are used to identify those variables that are related to conduct of fraud and/or no fraud, detect patterns of associated behavior and establishing to what extent cases of fraud and/or no fraud can be detected with the available information. We utilize some information gained from tax auditors who are working in the Tax offices of Tehran and the informal unofficial statistics and anonymous questionnaire from some companies to gain primary data to detect fraud and compare different techniques of False Invoices. To determine the main indexes in False Invoices, we divided taxpayers to the micro and small enterprises and on the other side medium and large enterprises and examined the factors of fraud on each groups, with neural gas networks, separately. Particularly the neural gas method found that it was possible to identify some relevant variables to differentiate between good or bad behavior, not necessarily associated with the use and sale of False Invoices. Kohonen’ s method however, did not provide any behavioral patterns. In the case of micro and small businesses, the percentage of correctly detected fraud cases was 92%, while in the case of medium and large enterprises, this percentage was 89%.

Cites

No record.

References

No record.

Cite

APA: Copy

GHORBANI, ABOLFAZL, LAYEGHI, KAMRAN, & davoudi, fatemeh. (2017). The Detection of Taxpayers with False Invoices using Data Mining Techniques. TAX JOURNAL, 25(33 (81) ), 167-193. SID. https://sid.ir/paper/89746/en

Vancouver: Copy

GHORBANI ABOLFAZL, LAYEGHI KAMRAN, davoudi fatemeh. The Detection of Taxpayers with False Invoices using Data Mining Techniques. TAX JOURNAL[Internet]. 2017;25(33 (81) ):167-193. Available from: https://sid.ir/paper/89746/en

IEEE: Copy

ABOLFAZL GHORBANI, KAMRAN LAYEGHI, and fatemeh davoudi, “The Detection of Taxpayers with False Invoices using Data Mining Techniques,” TAX JOURNAL, vol. 25, no. 33 (81) , pp. 167–193, 2017, [Online]. Available: https://sid.ir/paper/89746/en

Related Journal Papers