Designing an Intelligent Tax System Model with Emphasis on Tax Evasion Detection, Tax Justice, and Economic Transaction Analysis
Keywords:
Intelligent tax system, tax evasion, tax justice, economic transaction analysis, data governance, machine learning, tax compliance.Abstract
This study aimed to design a qualitative model for the intelligent transformation of the tax system, focusing on tax evasion detection, tax justice, and data-driven analysis of economic transactions. This qualitative study was conducted using an interpretive phenomenological design. The participants consisted of 24 tax experts, financial managers, auditors, data specialists, and tax information technology professionals in Tehran, selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was reached. Theoretical saturation was achieved at the twenty-first interview, and three additional interviews were conducted to confirm the stability of the extracted categories. Data were analyzed through open, axial, and selective coding using NVivo software. Data analysis led to the extraction of five main categories: integration of tax and economic databases; intelligent detection of tax risk and evasion; tax justice and public trust; algorithmic governance and data protection; and organizational empowerment and phased implementation. Participants emphasized that tax intelligence is not merely a matter of software acquisition, but requires transformation in data architecture, information-exchange regulations, human-resource capabilities, algorithmic transparency, and accountability mechanisms. The findings also indicated that transaction analysis can reduce tax evasion only when it is accompanied by clear legal rules, algorithmic error control, and a non-discriminatory approach. The proposed model suggests that intelligent tax transformation becomes effective when technical, institutional, and social logics are addressed simultaneously. Technically, data integration and risk analytics are essential; institutionally, rules for access, security, and accountability must be strengthened; and socially, perceived fairness and taxpayer trust are necessary conditions for sustainable tax compliance.
Downloads
References
Alexopoulos, A., Dellaportas, P., Gyoshev, S., Kotsogiannis, C., Olhede, S. C., & Pavkov, T. (2021). A network and machine learning approach to detect value added tax fraud. arXiv. https://doi.org/10.48550/arXiv.2106.14005
Alm, J. (2019). What motivates tax compliance? Journal of Economic Surveys, 33(2), 353–388. https://doi.org/10.1111/joes.12272
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
International Monetary Fund. (2025). Generative artificial intelligence for compliance risk analysis: Applications in tax and customs administration. International Monetary Fund.
Kirchler, E., Hoelzl, E., & Wahl, I. (2008). Enforced versus voluntary tax compliance: The “slippery slope” framework. Journal of Economic Psychology, 29(2), 210–225. https://doi.org/10.1016/j.joep.2007.05.004
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
OECD. (2016). Advanced analytics for better tax administration: Putting data to work. OECD Publishing. https://doi.org/10.1787/9789264256453-en
OECD. (2019). Tax morale: What drives people and businesses to pay tax? OECD Publishing. https://doi.org/10.1787/f3d8ea10-en
OECD. (2020). Tax Administration 3.0: The digital transformation of tax administration. OECD Publishing. https://doi.org/10.1787/ca274cc5-en
OECD. (2023). Tax administration 2023: Comparative information on OECD and other advanced and emerging economies. OECD Publishing. https://doi.org/10.1787/900b6382-en
OECD. (2025). Tax administration digitalisation and digital transformation initiatives. OECD Publishing.
Okunogbe, O., & Santoro, F. (2022). The promise and limitations of information technology for tax mobilization. World Bank. https://doi.org/10.1596/1813-9450-9848
Savić, M., Atanasijević, J., Jakovetić, D., & Krejić, N. (2021). Tax evasion risk management using a hybrid unsupervised outlier detection method. arXiv. https://doi.org/10.48550/arXiv.2103.01033
Torgler, B. (2007). Tax compliance and tax morale: A theoretical and empirical analysis. Edward Elgar Publishing.
Zumaya, M., Guerrero, R., Islas, E., Pineda, O., Gershenson, C., Iñiguez, G., & Pineda, C. (2021). Identifying tax evasion in Mexico with tools from network science and machine learning. In O. M. Granados & J. R. Nicolás-Carlock (Eds.), Corruption networks: Understanding complex systems (pp. 89–113). Springer. https://doi.org/10.1007/978-3-030-81484-7_6
