Volume 33, Issue 59 (2023)                   J Tax Res 2023, 33(59): 8-25 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Barzegari Dehaj M B D, Ya’ghoobnejad A Y, Keighobadi A, Jahanshad A. Tax Audit Selection by Using of Data Mining Algorithms. J Tax Res 2023; 33 (59) :8-25
URL: http://taxjournal.ir/article-1-2319-en.html
1- , yaghoobacc@gmail.com
Abstract:   (813 Views)
Since the direct taxes law was approved in 2014, and its article 97 was amended, the State tax affairs organization has been required to accept tax returns from individuals whose financial year begins on 29/07/2018, and to select and audit only a few of those returns, based on risk indicators. Using data mining methods, it is possible to determine high-risk taxpayers based on their information. In this way, high-risk taxpayers can be identified. During this study, tax returns information for legal entities from 2014 to 2016 was used in order to assess the level of risk.. Finally, the success of the methods has been evaluated. The algorithms used are vector machine classification methods, neural network support, decision tree and nearest neighbor. The results of the research confirm that the neural network algorithm is introduced as the best algorithm for estimating the risk of the statement.
 
Full-Text [PDF 682 kb]   (606 Downloads)    
Type of Study: Research | Subject: Management
Received: 2023/12/20 | Accepted: 2023/12/1 | Published: 2023/12/1

References
1. رفرنس های متنی مثل خروجی کراس رف را در اینجا وارد کرده و تایید کنید
2. Fazel Yazdi, A., Moinuddin, M. (2013). Risk Analysis of Accounting Information Systems, Auditor Magazine, No. 60, pp. 106 - 111, (Persian).
3. Hajiha, Z. (2010). An Investigation on the Relationship between Inherent and Control Risks in Risk Based Audit Approach, Financial Accounting; 2(6), pp. 95-120, (Persian).
4. Masihi, M., Yaghoobnejad, A., Keyghobadi, A., Torabi, T. (2019). Using Data Mining Techniques to Measure Tax Risk of Value Added Taxes, Journal of Investment Knowledge, 8(32), pp. 347-363 (Persian).
5. Mohammadi, T., Arab Maziar Yazdi, A., Ghasemi, A, Taklif, A., jalalpanahi, R. (2020). Balance of Payment Constrained Growth in Tow Developing and Developed Oil-Exporting Economies (Case Study: Iran and Norway) qjerp, 27 (92), pp. 257-296 (Persian).
6. Namazi, M., Sadeghzadeh Maharluie, M. (2018). Predicting Tax Evasion by Decision Tree Algorithms, Financial Accounting, 9(36), pp. 76-100, (Persian).
7. Setayesh, M., Ebrahimi, F., SAIF, S., Sarikhani, M. (2013). Forecasting the Type of Audit Opinions: A Data Mining Approach, Management Accounting, 5(15), pp. 69-82, (Persian).
8. Bell, T. B., Peecher, M. E., Solomon, I. (2005). The 21st Century Public Company Audit: Conceptual Elements of KPMGs Global Audit Methodology', KPMG, LLP.
9. Ding, N., Zhang, X., Zhai, Y., & Li, C. (2021). Risk Assessment of VAT Invoice Crime Levels of Companies Based on DFPSVM: a Case Study in China', Risk Management, Palgrave Macmillan, vol. 23(1), pp. 75-96.
10. De Roux, D., Perez, B., Moreno, A., Villamil, M. D. P., and Figueroa, C. (2018). Tax Fraud Detection for Under-reporting Declarations using an Unsupervised Machine Learning Approach, In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 215-222.
11. Efstathios. K., Spathis. Ch., Nanopoulos.A and Y. Manolopoulos, (2007). Identifying Qualified Auditors Opinions: A Data Mining Approach, Journal of Emerging Technologies Accounting, Vol.4, pp 183-197.
12. Hirsh-Pasek, Kathy, et al. (2015). Putting Education in Educational' Apps: Lessons from the Science of Learning. Psychological Science in the Public Interest 16.1, pp. 3-34.
13. Karahoca, Adem, Dilek Karahoca, and Mert Şanver. (2012). Survey of Data Mining and Applications (Review from 1996 to Now), Data Mining Applications in Engineering and Medicine: 1.
14. Kiros, Efstathios, Charalambos Spathis, and Yannis Manolopoulos. (2007). Data Mining Techniques for the Detection of Fraudulent Financial Statements, Expert Systems with Applications 32.4, pp. 995-1003.
15. Murorunkwere, B., F., Haughton, H., Nzabanita, J. Kipkogei, F. & Kabano, I. (2023). Predicting Tax Fraud using Supervised Machine Learning Approach, African Journal of Science, Technology, Innovation and Development, 15:6, 731-742.
16. O,Dannell, E and J,Schults. (2003). The influence of Strategic-Systems Lens of Auditor Risk Assessments, working paper, Arizona state University.
17. Placencia, J. O., Hallo, M., & Luján-Mora, S. (2020). Detection of Taxpayers with High Probability of Non-payment: An Implementation of a Data Mining Framework, In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6. IEEE.
18. Ruzgas T, Kižauskienė L, Lukauskas M, Sinkevičius E, Frolovaitė M, Arnastauskaitė J. (2023). Tax Fraud Reduction Using Analytics in an East European Country', Axioms. 12(3).
19. Fazel Yazdi, A., Moinuddin, M. (2013). Risk Analysis of Accounting Information Systems, Auditor Magazine, No. 60, pp. 106 - 111, (Persian).
20. Hajiha, Z. (2010). An Investigation on the Relationship between Inherent and Control Risks in Risk Based Audit Approach, Financial Accounting; 2(6), pp. 95-120, (Persian).
21. Masihi, M., Yaghoobnejad, A., Keyghobadi, A., Torabi, T. (2019). Using Data Mining Techniques to Measure Tax Risk of Value Added Taxes, Journal of Investment Knowledge, 8(32), pp. 347-363 (Persian).
22. Mohammadi, T., Arab Maziar Yazdi, A., Ghasemi, A, Taklif, A., jalalpanahi, R. (2020). Balance of Payment Constrained Growth in Tow Developing and Developed Oil-Exporting Economies (Case Study: Iran and Norway) qjerp, 27 (92), pp. 257-296 (Persian).
23. Namazi, M., Sadeghzadeh Maharluie, M. (2018). Predicting Tax Evasion by Decision Tree Algorithms, Financial Accounting, 9(36), pp. 76-100, (Persian).
24. Setayesh, M., Ebrahimi, F., SAIF, S., Sarikhani, M. (2013). Forecasting the Type of Audit Opinions: A Data Mining Approach, Management Accounting, 5(15), pp. 69-82, (Persian).
25. Bell, T. B., Peecher, M. E., Solomon, I. (2005). The 21st Century Public Company Audit: Conceptual Elements of KPMGs Global Audit Methodology', KPMG, LLP.
26. Ding, N., Zhang, X., Zhai, Y., & Li, C. (2021). Risk Assessment of VAT Invoice Crime Levels of Companies Based on DFPSVM: a Case Study in China', Risk Management, Palgrave Macmillan, vol. 23(1), pp. 75-96.
27. De Roux, D., Perez, B., Moreno, A., Villamil, M. D. P., and Figueroa, C. (2018). Tax Fraud Detection for Under-reporting Declarations using an Unsupervised Machine Learning Approach, In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 215-222.
29. Efstathios. K., Spathis. Ch., Nanopoulos.A and Y. Manolopoulos, (2007). Identifying Qualified Auditors Opinions: A Data Mining Approach, Journal of Emerging Technologies Accounting, Vol.4, pp 183-197. [DOI:10.2308/jeta.2007.4.1.183 https://doi.org/10.2308/jeta.2007.4.1.183]
30. Hirsh-Pasek, Kathy, et al. (2015). Putting Education in Educational' Apps: Lessons from the Science of Learning. Psychological Science in the Public Interest 16.1, pp. 3-34. [DOI:10.1177/1529100615569721]
31. Karahoca, Adem, Dilek Karahoca, and Mert Şanver. (2012). Survey of Data Mining and Applications (Review from 1996 to Now), Data Mining Applications in Engineering and Medicine: 1.
32. Kiros, Efstathios, Charalambos Spathis, and Yannis Manolopoulos. (2007). Data Mining Techniques for the Detection of Fraudulent Financial Statements, Expert Systems with Applications 32.4, pp. 995-1003.
33. Murorunkwere, B., F., Haughton, H., Nzabanita, J. Kipkogei, F. & Kabano, I. (2023). Predicting Tax Fraud using Supervised Machine Learning Approach, African Journal of Science, Technology, Innovation and Development, 15:6, 731-742.
34. O,Dannell, E and J,Schults. (2003). The influence of Strategic-Systems Lens of Auditor Risk Assessments, working paper, Arizona state University.
35. Placencia, J. O., Hallo, M., & Luján-Mora, S. (2020). Detection of Taxpayers with High Probability of Non-payment: An Implementation of a Data Mining Framework, In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6. IEEE.
36. Ruzgas T, Kižauskienė L, Lukauskas M, Sinkevičius E, Frolovaitė M, Arnastauskaitė J. (2023). Tax Fraud Reduction Using Analytics in an East European Country', Axioms. 12(3).

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Tax Research

Designed & Developed by : Yektaweb