دوره 31، شماره 59 - ( 1402 )
جلد 31 شماره 59 صفحات 25-8 |
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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; 31 (59) :8-25
URL: http://taxjournal.ir/article-1-2319-fa.html
URL: http://taxjournal.ir/article-1-2319-fa.html
برزگری دهج محمد، یعقوب نژاد احمد، کیقبادی امیررضا، جهانشاد آزیتا. انتخاب برای حسابرسی مالیاتی با استفاده از الگوریتم‌های دادهکاوی. پژوهشنامه مالیات. 1402; 31 (59) :8-25
1- ، yaghoobacc@gmail.com
چکیده: (1094 مشاهده)
با تصویب قانون مالیاتهای مستقیم در سال 1394 و اصلاح ماده 97 آن، سازمان امور مالیاتی کشور مکلف است اظهارنامه مالیاتی تسلیمی اشخاصی که شروع سال مالی آنها از 27/05/1397 و به بعد میباشد را بپذیرد و صرفاً تعدادی از آنها را براساس شاخصهای ریسک انتخاب و مورد حسابرسی قرار دهد. یکی از روشهای تعیین مؤدیان پرریسک مالیاتی استفاده از روشهای دادهکاوی میباشد که به موجب آن میتوان براساس اطلاعات هر مؤدی، مؤدیان پرریسک را تعیین نمود. در این تحقیق، اطلاعات اظهارنامه‌های مالیاتی اشخاص حقوقی از سال 1393 تا 1395 برای ارزیابی ریسک مورداستفاده قرار گرفتهاست. الگوریتم‌های مورد استفاده در این پژوهش، روشهای دسته‌بندی ماشین بردار پشتیبان، شبکه عصبی، درخت تصمیم و نزدیک‌ترین همسایه بوده است. نتایج پژوهش مؤید آن است که الگوریتم شبکه عصبی به عنوان بهترین الگوریتم برای برآورد ریسک اظهارنامه، معرفی می‌شود.
واژه‌های کلیدی: ماده 97 قانون مالیاتهای مستقیم، ریسک اظهارنامه مالیاتی، روش های دادهکاوی، مؤدیان پرریسک مالیاتی
فهرست منابع
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).
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