Volume 31, Issue 60 (2024)
J Tax Res 2024, 31(60): 200-217 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rostam Beiggi H, Ayneband M. Detecting Tax Evasion of Legal Entities Using Artificial Inteligence. J Tax Res 2024; 31 (60) :200-217
URL: http://taxjournal.ir/article-1-2344-en.html
URL: http://taxjournal.ir/article-1-2344-en.html
1- , Me.Aynehband@iau.ac.ir
Abstract: (2143 Views)
By using artificial intelligence methods, tax evasion of legal entities which is a continuous concern of tax system, has been detected based on financial data recorded in financial transactions of collections. The phenomenon of tax evasion can be considered in the sense that it reduces the ratio of tax revenues to GDP while reducing government revenues and increasing the level of the tax gap. Since data mining technology has many predictive and classification capabilities, it can facilitate the decision-making process in financial matters. The different methods for transaction data modeling are based on matching labeled data and the reviews of legal taxpayers' files by tax experts, which are marked as tax/non-tax evasion cases. There are various methods for modeling on this method. With the increasing use of artificial intelligence modeling, this article, by using three modern methods of artificial intelligence, on the aforementioned data set, validates the use of these methods by using the parameters of accuracy, sensitivity, and precision, which determine the quality of the produced model from various aspects. It validates, the feasibility of using these models has also been done on local financial transaction data. Therefore, in this research, it has been tried to use artificial intelligence algorithms such as artificial neural network algorithms, support vector machine and artificial bee colony algorithm to detect tax evasion. The statistical population of the current research includes 3600 taxpayers in the studied years (2002-2012) of Tehran province. One of the new methods of financial data modeling is the use of hybrid methods, in which the combination of several artificial intelligence methods is used to increase the quality of the model. It is obvious that the combination of the mentioned methods has favorable results in some situations and may not be so in other situations. Therefore, in this article, this issue has been investigated in detail and the use of different methods and a combination of artificial intelligence methods has been discussed. Therefore, by using the combined algorithms in this research, we were able to with an accuracy of 85.59%; For training data and accuracy of 83.79% for test data, let's identify and classify taxpayers who are late in paying taxes and taxpayers who pay taxes on time. Therefore, according to the obtained results, we find that the proposed method in this research has a high ability to identify taxpayers who cheat on paying taxes. The use of artificial intelligence systems can be used as an economic stimulus in order to reduce tax evasion. In this regard, it is suggested to first form an integrated system as an economic data center, and this center will take on the responsibility of keeping the country's financial data safe. Then, a standard for the use of artificial intelligence and data mining of financial data should be developed. With this method of scientific and academic research, without providing sensitive data to outside the required organizations, it is possible to take a step forward from the scientific ability of experts regarding factors such as tax evasion detection, terrorism financing detection and money laundering in order to improve the country's economic system.
Type of Study: Research |
Subject:
Management
Received: 2024/02/20 | Accepted: 2024/02/29 | Published: 2024/04/20
Received: 2024/02/20 | Accepted: 2024/02/29 | Published: 2024/04/20
References
1. Aynehband, M., Hosseinzadeh, M., Zarrabi, H. et al. Accuracy and Availability Modeling of Social Networks for Internet of Things Event Detection Applications. Wireless Netw 25, 4299-4317 (2019). [DOI:10.1007/s11276-019-02093-5]
2. Khoramniya H, Fallahshams M, Zomorodian G, Asghar Anvary Rostami A. Comparison of Decision Tree (C5.0 Algorithm and Random Forest) and Support Vector Machine in the Validation of Taxpayers. J Tax Res 2023; 33 (59) :50-74 (In Persian) [DOI:10.61186/taxjournal.33.59.50]
3. Hashemi M, Etemadi H, Rezazadeh J. Modeling Tax Evasion in Value Added Tax, A Game Theory Approach. J Tax Res 2022; 30 (55) :7-51 (In Persian) [DOI:10.52547/taxjournal.30.55.2]
4. Abdul-Jabbar, H., Abuamria, F. M., Alkhatib, A. A., & Marimuthu, M. (2020). Tax evasion and the Social Influence Perspective: A conceptual model for Palestine. International Journal of Psychosocial Rehabilitation, 24(02), 4855-4866. https://doi/10.37200/V24I2/30702
5. Bani-Mustafa, A., Nimer, K., Uyar, A., & Schneider, F. (2022). Effect of Government Efficiency on Tax Evasion: The Mediating Role of Ethics and Control of Corruption. International Journal of Public Administration, 1-17. [DOI:10.1080/01900692.2022.2086262]
6. Gurney, K. (2018). An Introduction to Neural Networks. CRC Press. [DOI:10.1201/9781315273570]
7. Khademi Gerashi, M. (2021). Why Do I Have to Pay My Income Taxes? The Effects of Taxpayers' Perceived Value on Commitment to Pay in Light of the Possibility of Tax Evasion. Journal for Management and Development Process, 33(4), 97-114. https://doi/10.52547/jmdp.33.4.97 [DOI:10.52547/jmdp.33.4.97]
8. Kenno, B.G (2020), Factors Affecting Perception of Taxpayers Towards the Seriousness of Tax Evasion in Bale Robe Town Administration, Oromina, Ethiopia. International Journal of Finance and AAccounting, (2)9, 21-30.
9. M. Aynehband, A. M. Rahmani and S. Setayeshi, "COAST: Context-aware Pervasive Speech Recognition System," International Symposium on Wireless and Pervasive Computing, Hong Kong, China, 2011, pp. 1-4, doi: 10.1109/ISWPC.2011.5751306. [DOI:10.1109/ISWPC.2011.5751306]
10. McCulloch, W. S., & Pitts, W. (1943). A logical Calculus of the Ideas Immanent in Nervous Activity. The bulletin of mathematical biophysics, 5(4), 115-133. [DOI:10.1007/BF02478259]
11. Owusu, G. M. Y., Bart-Plange, M. A., Koomson, T. A. A., & Arthur, M. (2021). The Effect of Personality Traits and Tax Morale on Tax Evasion Intention. Journal of Financial Crime, 29(1), 272-292. [DOI:10.1108/JFC-02-2021-0026]
12. Parwati, N., Muslimin, M., Adam, R., Totanan, C., Yamin, N., & Din, M. (2021). The Effect of Tax Morale on Tax Evasion in the Perspective of Tri Hita Karana and Tax Framing. Accounting, 7(6), 1499-1506. https://DOI: 10.5267/j.ac.2021.3.014 [DOI:10.5267/j.ac.2021.3.014]
13. Romero, C., & Ventura, S. (2013). Data Mining in Education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. [DOI:10.1002/widm.1075]
14. Rostamy-Malkhalifeh, M., Amiri, M., & Mehrkam, M. (2021). Predicting Financial Statement Fraud Using Fuzzy Neural Networks. Advances in Mathematical Finance & Applications, 6(1), 137-145. [DOI:10.22034/ amfa.2020.1892431.1370]
15. Sadjiarto, A. susanto, A.N. yuniat, E. & Hartanto, N.G. (2019), Factors Affecting Perception of Tax Evasion Among Chindos. Advances in Economics, Business and Management Research, 144, 487-393. [DOI:10.2991/aebmr.k.200606.083]
16. Sritharan, N., Sahari, S., & Sharon, C. C. S. (2022). A Systematic Literature Review on Tax Evasion: Insights and Future Research Agenda.
https://doi.org/10.6007/IJARAFMS/v12-i2/13089 [DOI:10.6007/ijarafms/v12-i2/13089]
17. Shuid, S. H., Zazili, A. S. A., & Basri, S. A. (2021). Determinant Factors of Tax Evasion. Global Business & Management Research, 13.
18. Shmilovici, A. (2009). Support Vector Machines. In Data Mining and Knowledge Discovery Handbook (pp. 231-247). Springer, Boston, MA.
https://doi.org/10.1007/978-0-387-09823-4_12 [DOI:10.1007/978-0-387-09823-4_12]
19. Vanhoeyveld, J., Martens, D., & Peeters, B. (2020). Value-added Tax Fraud Detection with Scalable Anomaly Detection Techniques. Applied Soft Computing, 86, 1-38.
https://doi.org/10.1016/j.asoc.2019.105895 [DOI:10.1016/j.asoc. 2019.105895]
20. Fallah Shams Mirfaiz, Zamardian Gholamreza, Anvari Rostami Ali Asghar, Khorramnia Hoshang. Comparison of Neural Network Model and Logit Regression in the Validation of Taxpayers. Tax research paper. 2021; 30 (55): 108-126 (In Persian) [DOI:10.52547/taxjournal.30.55.4]
21. Manzoor D. Analyzing the Behavior of Tax Compliancy in Iran: A Game Theoretical Approach. J Tax Res 2022; 30 (55) :127-150 (In Persian) [DOI:10.52547/taxjournal.30.55.5]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
