Designing a Model for Tax Policy Implementation Based on the Role of Artificial Intelligence

Motahhareh  Karami; Mojtaba Ranjbar; Parviz  Saeedi

Authors

Motahhareh Karami Department of Management and Accounting, Go.C., Islamic Azad University, Gorgan, Iran Author https://orcid.org/0009-0000-6629-015X
Mojtaba Ranjbar Department of Management and Accounting, Go.C., Islamic Azad University, Gorgan, Iran Author https://orcid.org/0000-0002-1614-2359
Parviz Saeedi Department of Management and Accounting, Ali.C., Islamic Azad University, Aliabad Katoul, Iran Author https://orcid.org/0000-0003-2960-3457

Keywords:

policy, taxation, tax policy, artificial intelligence

Abstract

The irregular and nontransparent structure of the economy, together with financial and institutional constraints, has made it difficult for tax and statistical authorities to generate accurate and reliable data. The adoption and implementation of effective tax policies capable of contributing to a more equitable distribution of wealth among different segments of society are therefore of considerable importance. The purpose of this study was to develop a model for tax policy implementation based on the role of artificial intelligence (AI). The study employed a qualitative-inductive approach and the Strauss–Corbin grounded theory method. Data were collected through semi-structured interviews. Using the grounded theory method, data obtained from interviews with 22 managers, experts, and key specialists in the National Tax Administration were analyzed through three stages of open, axial, and selective coding. Seven overarching categories were identified within a paradigmatic model. These factors included causal conditions (the increasing complexity of the tax system; transition from traditional methods toward the sustainable financing of public revenues; the need for fairness and accuracy in tax collection; and increasing tax evasion and avoidance), the central phenomenon (AI-enabled tax policy implementation), contextual conditions (digital transformation in public governance; access to extensive and granular data; technological innovation and transformation in tax governance; and interorganizational communication and data exchange), intervening conditions (data quality and integration; transparency and explainability; information-security constraints; organizational and human resistance; and digital skills and literacy), strategies (a risk-based audit system; optimization of operational and training processes; enhancement of institutional accountability; development of a hybrid human–AI model; and informational synergy in detecting tax evasion), and consequences (a fairer tax system with greater compliance; operational efficiency accompanied by transparency; and the linkage between public trust and tax governance). The model demonstrates how environmental imperatives lead to the selection of a technological solution and identifies the challenges and outcomes associated with its implementation.

Downloads

Download data is not yet available.

References

[1] M. Howlett, M. Ramesh, and A. Perl, Studying Public Policy: Policy Cycles and Policy Subsystems, 3rd ed. Toronto: Oxford University Press, 2009.

[2] B. Bozeman, "What Organization Theorists and Public Policy Researchers Can Learn from One Another: Publicness Theory as a Case-in-Point," Organization Studies, vol. 34, no. 2, pp. 169-188, 2013, doi: 10.1177/0170840612473549.

[3] G. Bazzan, P. Álamos-Concha, and B. Rihoux, "Identifying Diverse Paths Toward Successful Policy Performance in Organisation for Economic Co-operation and Development (OECD) and European Union Countries: A Qualitative Comparative Analysis (QCA) Exploitation of the Sustainable Governance Indicators (SGI) Data," European Policy Analysis, vol. 8, no. 2, pp. 178-208, 2022, doi: 10.1002/epa2.1145.

[4] M. B. Akitoby, M. A. Baum, C. Hackney, O. Harrison, K. Primus, and M. V. Salins, "Tax Revenue Mobilization Episodes in Emerging Markets and Low-Income Countries: Lessons from a New Dataset," International Monetary Fund, 2018. [Online]. Available: https://doi.org/10.5089/9781484361535.001

[5] H. Ataollahi, Z. Amini Sabegh, E. Sadeh, and H. Kalantari Khalilabad, "Identifying and Analyzing Factors Affecting Tax Justice Policymaking Toward Development," Islamic Lifestyle with a Focus on Health, vol. 5, no. Special Issue on Management and Education, pp. 416-428, 2021.

[6] A. Sheikh Hassani, A. Faghihi, and K. Daneshfard, "Analysis of Tax Policymaking in Iran's Value-Added Tax System," Public Administration, vol. 10, no. 1, pp. 137-160, 2018.

[7] N. Darmayasa and D. M. Partika, "Reconstruction Concept of the Meaning of Permanent Establishment Physical Presence for Tax Purposes," Journal of Tax Reform, vol. 10, no. 1, pp. 38-50, 2024.

[8] B. Peci and F. Morina, "The Legal Framework for Harmonization of Value Added Tax (VAT) in European Union," Acta Universitatis Danubius. Juridica, vol. 13, no. 1, 2017.

[9] M. Moghari Garderoudbari, I. Dadashi, B. Mohseni Maleki, and A. Zabihi, "Predicting Tax Evasion of Corporate Taxpayers with Emphasis on Economic Components, Taxpayers, and Tax Auditors Using Artificial Intelligence," Tax Research Journal, vol. 31, no. 58, pp. 131-164, 2023.

[10] H. Rostambeygi and M. Ayneband, "Tax Evasion by Legal Persons Using Artificial Intelligence," Tax Research Journal, vol. 31, no. 60, pp. 200-217, 2024.

[11] A. Hajivand, S. Z. Khoshmanzar, and Saber, "A Brief History of Artificial Intelligence: The Past, Present, and Future of Artificial Intelligence," Legal Civilization Quarterly, vol. 6, no. 18, pp. 73-90, 2024.

[12] C. Wiggins and M. L. Jones, How Data Happened: A History from the Age of Reason to the Age of Algorithms. Norton and Company, 2023.

[13] B. W. Wirtz, J. C. Weyerer, and C. Geyer, "Artificial Intelligence and the Public Sector," International Journal of Public Administration, vol. 42, no. 7, pp. 596-615, 2019, doi: 10.1080/01900692.2018.1498103.

[14] H. Margetts and C. Dorobantu, "Rethink Government with AI," Nature, vol. 568, no. 7751, pp. 163-165, 2019, doi: 10.1038/d41586-019-01099-5.

[15] A. H. Saragih, Q. Reyhani, M. S. Setyowati, and A. Hendrawan, "The Potential of an Artificial Intelligence (AI) Application for the Tax Administration System's Modernization: The Case of Indonesia," Artificial Intelligence and Law, vol. 31, no. 3, pp. 491-514, 2023, doi: 10.1007/s10506-022-09321-y.

[16] A. Angelov, "Artificial Intelligence in European Union Tax Administrations: A Comparative Assessment," Journal of Risk and Financial Management, vol. 19, no. 4, p. 295, 2026, doi: 10.3390/jrfm19040295.

[17] T. Aladebumoye, "The Role of AI in Enhancing Tax Transparency and Reducing Evasion," World Journal of Advanced Research and Reviews, vol. 25, no. 1, pp. 206-217, 2025, doi: 10.30574/wjarr.2025.25.1.0023.

[18] J. Alahira, N. Z. Mhlongo, T. Falaiye, O. Olubusola, A. I. Daraojimba, and B. B. Oguejiofor, "The Role of Artificial Intelligence in Enhancing Tax Compliance and Financial Regulation," Finance & Accounting Research Journal, vol. 10, pp. 241-251, 2024, doi: 10.51594/farj.v6i2.822.

[19] I. Ben-alla and M. Nmili, "Artificial Intelligence and Corporate Tax Risk Management: A Systematic Literature Review," International Journal of Research in Economics and Finance, vol. 3, no. 1, pp. 75-96, 2026, doi: 10.71420/ijref.v3i1.246.

[20] H. Ravanpak Nodezh and M. Gholami, "The Role of Artificial Intelligence in Enhancing Tax Revenues, Institutional Quality, and Economic Growth," Accounting and Management Outlook, vol. 8, no. 108, pp. 124-138, 2025.

[21] H. Ravanpak Nodezh and N. Hajami Koushalshahi, "The Impact of Modern Financial Technologies (FinTech) and Artificial Intelligence on the Transparency and Efficiency of Iran's Tax System," Accounting and Management Outlook, vol. 8, no. 108, pp. 1-16, 2025.

[22] S. H. Shojaei, M. Ebrahimi, and H. Zare, "Examining the Effect of Artificial Intelligence Use on Reforming Iran's Macroeconomic Structure," Journal of Economics and Business Research, no. 33, pp. 89-107, 2024.

[23] V. Bezditnyi, "Use of Artificial Intelligence for Tax Planning Optimization and Regulatory Compliance," Research Corridor Journal of Engineering Science, vol. 1, no. 1, pp. 103-142, 2024.

[24] M. Bowler-Smith, "A New-Knowledge Approach to AI and Tax Policy," presented at the Australian Tax Forum, Sydney, NSW, 2021. [Online]. Available: https://doi.org/10.66320/8qe5f566.

[25] A. L. Martinez, "Artificial Intelligence in Tax Administration: Enhancing Compliance, Transparency, and Ethical Governance," ed, 2025.

[26] S. Chatterjee, R. Chaudhuri, and D. Vrontis, "Does Remote Work Flexibility Enhance Organization Performance? Moderating Role of Organization Policy and Top Management Support," Journal of Business Research, vol. 139, pp. 1501-1512, 2022, doi: 10.1016/j.jbusres.2021.10.069.

[27] E. Shahnazari and M. Motamednia, "Opportunities and Challenges of Artificial Intelligence in Financial Markets," presented at the Ninth International Conference on Management Sciences and Accounting, Tehran, 2023.

[28] S. Kapoor and A. Narayanan, "Leakage and the Reproducibility Crisis in Machine-Learning-Based Science," Patterns, vol. 4, no. 9, 2023.

[29] Oecd, "OECD Framework for the Classification of AI Systems," OECD, 2022. [Online]. Available: https://www.oecd-library.org/docserver/cb6d9eca-en.pdf

[30] T. Zarsky, "The Trouble with Algorithmic Decisions," Science, Technology, & Human Values, vol. 41, no. 1, 2016, doi: 10.1177/0162243915605575.

[31] A. Tsamados et al., "The Ethics of Algorithms: Key Problems and Solutions," in AI and Society, 2021, pp. 1-16.

[32] M. van Bekkum and F. Z. Borgesius, "Digital Welfare Fraud Detection and the Dutch SyRI Judgment," European Journal of Social Security, vol. 23, no. 4, pp. 323-340, 2021, doi: 10.1177/13882627211031257.

[33] A. Fidelangeli and F. Galli, "Artificial Intelligence and Tax Law: Perspectives and Challenges," CERIDAP, vol. 4, no. Ottobre-Dicembre, pp. 24-58, 2021.

[34] M. Taddeo and L. Floridi, "How AI Can Be a Force for Good," Science, vol. 361, no. 6404, pp. 751-752, 2018, doi: 10.1126/science.aat5991.

[35] J. Mökander and R. Schroeder, "Artificial Intelligence, Rationalization, and the Limits of Control in the Public Sector: The Case of Tax Policy Optimization," Social Science Computer Review, vol. 42, no. 6, pp. 1359-1378, 2024.