SOLIQ MA’MURIYATCHILIGIDA SUN’IY INTELLEKT ORQALI SOLIQ XAVFINI MINIMALLASHTIRISH

Aliyarov Olim Abdugaparovich

Ushbu maqolada soliq xavfini kamaytirish va soliq to‘lashdan bo‘yin tovlashni cheklashda sun’iy intellekt (SI) texnologiyalarining roli har tomonlama tahlil qilingan. Zamonaviy soliq ma’muriyatchiligi oldida turgan asosiy muammolar - murakkab moliyaviy operatsiyalar, raqamli iqtisodiyotning tezkor rivojlanishi va an’anaviy soliq ijrosi usullarining cheklovlari hamda ularning yechimi sifatida SIni keng qo‘llash istiqbollari chuqur yoritilgan. Shuningdek, turli mamlakatlar tajribasi misolida soliq to‘lashdan bo‘yin tovlash sxemalarini erta bosqichda aniqlashda SI algoritmlarining yuqori samaradorligi, shu jarayon bilan bog‘liq huquqiy, axloqiy va texnik jihatlar ham xolis tahlil etilgan. Maqola yakunida soliqqa tortish sohasida SIdan foydalanish bo‘yicha amaliy hamda ilmiy tavsiyalar keltirilgan.

Name of journalRaqamli iqtisodiyot
Number of edition11-son
View count 13

Web Address https://infocom.uz/magazine/18

DOI

Date of creation in the UzSCI system 10-07-2025

Read count 13

Date of publication 07-07-2025

Main LanguageO'zbek

Pages89-102

Tags

muvofiqlik

raqamli iqtisodiyot

sun’iy intellekt

soliq shaffofligi

soliq to‘lashdan bo‘yin tovlash

texnik integratsiya

katta ma’lumotlar tahlili

avtomatlashtirilgan audit

huquqiy masalalar

Ўзбек

Ushbu maqolada soliq xavfini kamaytirish va soliq to‘lashdan bo‘yin tovlashni cheklashda sun’iy intellekt (SI) texnologiyalarining roli har tomonlama tahlil qilingan. Zamonaviy soliq ma’muriyatchiligi oldida turgan asosiy muammolar - murakkab moliyaviy operatsiyalar, raqamli iqtisodiyotning tezkor rivojlanishi va an’anaviy soliq ijrosi usullarining cheklovlari hamda ularning yechimi sifatida SIni keng qo‘llash istiqbollari chuqur yoritilgan. Shuningdek, turli mamlakatlar tajribasi misolida soliq to‘lashdan bo‘yin tovlash sxemalarini erta bosqichda aniqlashda SI algoritmlarining yuqori samaradorligi, shu jarayon bilan bog‘liq huquqiy, axloqiy va texnik jihatlar ham xolis tahlil etilgan. Maqola yakunida soliqqa tortish sohasida SIdan foydalanish bo‘yicha amaliy hamda ilmiy tavsiyalar keltirilgan.

Tags

muvofiqlik

raqamli iqtisodiyot

sun’iy intellekt

soliq shaffofligi

soliq to‘lashdan bo‘yin tovlash

texnik integratsiya

katta ma’lumotlar tahlili

avtomatlashtirilgan audit

huquqiy masalalar

№ Author name position Name of organisation

1 Aliyarov O.A. Iqtisodiyot kafedrasi o‘qituvchisi Termiz davlat universiteti

№ Name of reference

1 Aberbax, J. D., & Kristensen, T. (2007). Challenges in modernizing tax administration: Putting customers first in compulsory public organizations. Public Policy and Administration, 22(2), 155–182.

2 Adelkan, O. A., Adisa, O., Ilugbusi, B. S., Obi, O. C., Awonuga, K. F., Asuzu, O. F., & Ndbuisi, N. L. (2024). The evolution of tax compliance in the digital era: A comparative analysis of AI-based models and blockchain technology in U.S. tax administration. Journal of Computer Science and IT Research, 5(2), 311–335.

3 Adie, F. U., Oduokpaha, G. E., Oworah, S. G., & Joseph, O. J. Evaluation of antibiotic resistance patterns of bacteria obtained from Automated Teller Machine surfaces in Calabar South, Cross River State, Nigeria.

4 Avofala, T. B., & Godwin, O. S. (2024). Data-driven strategies for combating chronic diseases worldwide. GSC Advanced Research and Reviews, 21(03), 235–240.

5 Awofala, T. B., & Bilikis, L. A. (2024). Parental health education and its influence on early childhood education. World Journal of Biology Pharmacy and Health Sciences, 20(03), 241–247.

6 Awofala, T. B., Bilikis, L. A., Imohimi, E., & Demilade, S. (2024). Data-driven approaches to reducing academic stress and improving students’ mental health. World Journal of Advanced Research and Reviews, 24(03), 2201–2206.

7 Axila, N., Bansal, A., Muhammad, N. Q., Al-Xuzay, M. Y., Almulla, A. A., & Agarwal, S. (2024, May). An automated instinctive optimization technique for filing taxes via an AI-based compilation system. In 4th International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE 2024) (pp. 973–977).

8 Bajpai, D. A. (2024). Assessing the impact of artificial intelligence on improving tax compliance and financial regulation. Available at SSRN 4922459.

9 Ballamudi, V. K. R. (2019). Artificial intelligence: Its influence on governance. Global Disclosure of Economics and Business, 8(2), 105–118.

10 Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence, and Tax Administration 3.0: Improving tax compliance behavior – A systematic literature review (2016–2023) using textometry. Journal of Accounting Research, 37(2), 172–191.

11 Blanco, G., de Coninck, H. C., Agbemabiese, L., Anadon, L. D., Lim, Y. S., Pengue, W. A., … Winkler, H. (2022). Innovation, technology development, and transfer. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 2674–2814). Cambridge University Press, 2005.

12 Ebers, M., & Tupay, P. K. (2023). Artificial intelligence and machine learning in delivering efficient public services in Estonia: Opportunities and legal challenges. Springer.

13 Faúndez-Ugalde, A., Mellado-Silva, R., & Aldunate-Lizana, E. (2020). The use of artificial intelligence by tax administrations: An analysis of taxpayers’ rights in Latin American countries. Computer Law & Security Review, 38, 105441.

14 Fidelangeli, A., & Galli, F. (2021). Artificial intelligence and tax law: Perspectives and challenges. CERIDAP, 4(October–December), 24–58.

15 Huang, Z. (2018). Discussion on issues in developing artificial intelligence in taxation. American Journal of Industrial and Business Management, 8(08), 1817–1825.

16 International Monetary Fund, Fiscal Affairs Dept. (2019). Fiscal Monitor, April 2019: Curbing Corruption. International Monetary Fund.

17 Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is transforming audit. Journal of Emerging Technologies in Accounting, 14(1), 115–122.

18 Milner, C., & Berg, B. (2017). Tax analytics – Artificial intelligence and machine learning – Level 5. PwC Advanced Tax Analytics & Innovation.

19 Nembe, J. K., Atadoga, J. O., Mhlongo, N. Z., Falaiye, T., Olubusola, O., Daraojimba, A. I., & Oguejiofor, B. B. (2024). The role of artificial intelligence in enhancing tax compliance and financial regulation. Journal of Finance and Accounting Research, 6(2), 241–251.

20 Nembe, J. K., Atadoga, J. O., Mhlongo, N. Z., Falaiye, T., Olubusola, O., Daraojimba, A. I., & Oguejiofor, B. B. (2024). The role of artificial intelligence in enhancing tax compliance and financial regulation. Journal of Finance and Accounting Research, 6(2), 241–251.

21 O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktyabrdagi “Sun’iy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash to‘g‘risida”gi PQ-358-son qarori.

22 Ojo, A. Integrating Result-Based Education into the Bachelor’s Mathematics Curriculum.

23 Omoyemi, O. E. (2024). Automated detection and correction of errors in Augmentative and Alternative Communication (AAC) systems using NLP. World Journal of Advanced Research and Reviews, 24(2), 520–525.

24 Omoyemi, O. E. (2024). Machine learning for predictive AAC: Enhancing speech and gesture-based communication systems. World Journal of Advanced Research and Reviews, 24(01), 2569–2575.

25 Organisation for Economic Co-operation and Development (OECD). (2020). Tax Administration 3.0: The Digital Transformation of Tax Administration. OECD.

26 Ovor, S. G., & Awofala, T. B. (2024). Evaluating the effectiveness of health policy through big data analytics. International Research Journal of Modernization in Engineering, Technology and Science, 6(12), 4126–4130.

27 Rathi, A., Sharma, S., Lodha, G., & Srivastava, M. (2021). A study on the application of artificial intelligence and machine learning in the Indian tax system. Journal of Psychology and Education, 58(2), 1226–1233.

28 Said, M. (2024). Artificial intelligence in transfer pricing: Opportunities and challenges for tax authorities. Journal of Economics and Business Research, 6(2).

29 Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2023). The potential of artificial intelligence (AI) applications for modernizing the tax administration system: A case study from Indonesia. Artificial Intelligence and Law, 31(3), 491–514.

30 Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2023). The potential of artificial intelligence (AI) applications for modernizing the tax administration system: A case study from Indonesia. Artificial Intelligence and Law, 31(3), 491–514.

31 Shakil, M. H., & Tasnia, M. (2022). Artificial intelligence and tax administration in the Asia-Pacific. In Taxation in the Digital Economy (pp. 45–55). Routledge.

32 Toʻlakov, U.T. (2024). Raqamli iqtisodiyot sharoitida soliq maʼmuriyatchiligini raqamlashtirishning xorij tajribasi. Raqamli iqtisodiyot va axborot texnologiyalari, 4(03), 3-12

33 Toʻlakov, U.T. (2025). Soliq maʼmuriyatchiligini takomillashtirishda innovatsion yondashuvlar. Ijtimoiy-gumanitar fanlarning dolzarb muammolari, 5(1), 91–105.

34 Yalamati, S. (2023). Detecting corporate tax fraud using advances in artificial intelligence. International Journal of Machine Learning for Sustainable Development, 5(2), 1–15.

Waiting

№	Name of reference
1	Aberbax, J. D., & Kristensen, T. (2007). Challenges in modernizing tax administration: Putting customers first in compulsory public organizations. Public Policy and Administration, 22(2), 155–182.
2	Adelkan, O. A., Adisa, O., Ilugbusi, B. S., Obi, O. C., Awonuga, K. F., Asuzu, O. F., & Ndbuisi, N. L. (2024). The evolution of tax compliance in the digital era: A comparative analysis of AI-based models and blockchain technology in U.S. tax administration. Journal of Computer Science and IT Research, 5(2), 311–335.
3	Adie, F. U., Oduokpaha, G. E., Oworah, S. G., & Joseph, O. J. Evaluation of antibiotic resistance patterns of bacteria obtained from Automated Teller Machine surfaces in Calabar South, Cross River State, Nigeria.
4	Avofala, T. B., & Godwin, O. S. (2024). Data-driven strategies for combating chronic diseases worldwide. GSC Advanced Research and Reviews, 21(03), 235–240.
5	Awofala, T. B., & Bilikis, L. A. (2024). Parental health education and its influence on early childhood education. World Journal of Biology Pharmacy and Health Sciences, 20(03), 241–247.
6	Awofala, T. B., Bilikis, L. A., Imohimi, E., & Demilade, S. (2024). Data-driven approaches to reducing academic stress and improving students’ mental health. World Journal of Advanced Research and Reviews, 24(03), 2201–2206.
7	Axila, N., Bansal, A., Muhammad, N. Q., Al-Xuzay, M. Y., Almulla, A. A., & Agarwal, S. (2024, May). An automated instinctive optimization technique for filing taxes via an AI-based compilation system. In 4th International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE 2024) (pp. 973–977).
8	Bajpai, D. A. (2024). Assessing the impact of artificial intelligence on improving tax compliance and financial regulation. Available at SSRN 4922459.
9	Ballamudi, V. K. R. (2019). Artificial intelligence: Its influence on governance. Global Disclosure of Economics and Business, 8(2), 105–118.
10	Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence, and Tax Administration 3.0: Improving tax compliance behavior – A systematic literature review (2016–2023) using textometry. Journal of Accounting Research, 37(2), 172–191.
11	Blanco, G., de Coninck, H. C., Agbemabiese, L., Anadon, L. D., Lim, Y. S., Pengue, W. A., … Winkler, H. (2022). Innovation, technology development, and transfer. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 2674–2814). Cambridge University Press, 2005.
12	Ebers, M., & Tupay, P. K. (2023). Artificial intelligence and machine learning in delivering efficient public services in Estonia: Opportunities and legal challenges. Springer.
13	Faúndez-Ugalde, A., Mellado-Silva, R., & Aldunate-Lizana, E. (2020). The use of artificial intelligence by tax administrations: An analysis of taxpayers’ rights in Latin American countries. Computer Law & Security Review, 38, 105441.
14	Fidelangeli, A., & Galli, F. (2021). Artificial intelligence and tax law: Perspectives and challenges. CERIDAP, 4(October–December), 24–58.
15	Huang, Z. (2018). Discussion on issues in developing artificial intelligence in taxation. American Journal of Industrial and Business Management, 8(08), 1817–1825.
16	International Monetary Fund, Fiscal Affairs Dept. (2019). Fiscal Monitor, April 2019: Curbing Corruption. International Monetary Fund.
17	Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is transforming audit. Journal of Emerging Technologies in Accounting, 14(1), 115–122.
18	Milner, C., & Berg, B. (2017). Tax analytics – Artificial intelligence and machine learning – Level 5. PwC Advanced Tax Analytics & Innovation.
19	Nembe, J. K., Atadoga, J. O., Mhlongo, N. Z., Falaiye, T., Olubusola, O., Daraojimba, A. I., & Oguejiofor, B. B. (2024). The role of artificial intelligence in enhancing tax compliance and financial regulation. Journal of Finance and Accounting Research, 6(2), 241–251.
20	Nembe, J. K., Atadoga, J. O., Mhlongo, N. Z., Falaiye, T., Olubusola, O., Daraojimba, A. I., & Oguejiofor, B. B. (2024). The role of artificial intelligence in enhancing tax compliance and financial regulation. Journal of Finance and Accounting Research, 6(2), 241–251.
21	O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktyabrdagi “Sun’iy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash to‘g‘risida”gi PQ-358-son qarori.
22	Ojo, A. Integrating Result-Based Education into the Bachelor’s Mathematics Curriculum.
23	Omoyemi, O. E. (2024). Automated detection and correction of errors in Augmentative and Alternative Communication (AAC) systems using NLP. World Journal of Advanced Research and Reviews, 24(2), 520–525.
24	Omoyemi, O. E. (2024). Machine learning for predictive AAC: Enhancing speech and gesture-based communication systems. World Journal of Advanced Research and Reviews, 24(01), 2569–2575.
25	Organisation for Economic Co-operation and Development (OECD). (2020). Tax Administration 3.0: The Digital Transformation of Tax Administration. OECD.
26	Ovor, S. G., & Awofala, T. B. (2024). Evaluating the effectiveness of health policy through big data analytics. International Research Journal of Modernization in Engineering, Technology and Science, 6(12), 4126–4130.
27	Rathi, A., Sharma, S., Lodha, G., & Srivastava, M. (2021). A study on the application of artificial intelligence and machine learning in the Indian tax system. Journal of Psychology and Education, 58(2), 1226–1233.
28	Said, M. (2024). Artificial intelligence in transfer pricing: Opportunities and challenges for tax authorities. Journal of Economics and Business Research, 6(2).
29	Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2023). The potential of artificial intelligence (AI) applications for modernizing the tax administration system: A case study from Indonesia. Artificial Intelligence and Law, 31(3), 491–514.
30	Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2023). The potential of artificial intelligence (AI) applications for modernizing the tax administration system: A case study from Indonesia. Artificial Intelligence and Law, 31(3), 491–514.
31	Shakil, M. H., & Tasnia, M. (2022). Artificial intelligence and tax administration in the Asia-Pacific. In Taxation in the Digital Economy (pp. 45–55). Routledge.
32	Toʻlakov, U.T. (2024). Raqamli iqtisodiyot sharoitida soliq maʼmuriyatchiligini raqamlashtirishning xorij tajribasi. Raqamli iqtisodiyot va axborot texnologiyalari, 4(03), 3-12
33	Toʻlakov, U.T. (2025). Soliq maʼmuriyatchiligini takomillashtirishda innovatsion yondashuvlar. Ijtimoiy-gumanitar fanlarning dolzarb muammolari, 5(1), 91–105.
34	Yalamati, S. (2023). Detecting corporate tax fraud using advances in artificial intelligence. International Journal of Machine Learning for Sustainable Development, 5(2), 1–15.