Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025)

Houda Zaim; Siham Sahbani

doi:10.29333/ejosdr/19260

Full Text (PDF)

Houda Zaim ¹ ^* , Siham Sahbani ¹

More Detail

¹ Sidi Mohamed Ben Abdellah University, National School of Commerce and Management, MOROCCO^* Corresponding Author

Abstract

While AI is integral to the tax compliance ecosystem, academic research is scattered across tax enforcement, governance, and sustainability. This bibliometric analysis of 1,803 Scopus-indexed articles from 2015 to 2025 shows that the field is not yet mature, but is undergoing rapid thematic change, with three waves, enforcement, governance, and sustainability, compressed into a decade. The central efficacy-legitimacy challenge is clear: AI improves anomaly detection and audit targeting, but algorithmic opacity can undermine taxpayer trust. The geographic inversion is also telling: Ukraine, Jordan, and Malaysia have more studies than OECD operational centers, suggesting reform urgency rather than administrative maturity. The study introduces the ATCGM, combining effectiveness, legitimacy, and sustainability, and identifies five empirically grounded research gaps. The practical implications are real: Peru’s e-invoicing rollout increased declared VAT liabilities by over 5% in its first year, but the governance conditions behind such gains remain under-researched.

Keywords

artificial intelligence
tax compliance
bibliometric analysis
digital taxation
algorithmic governance
tax modernization

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

EUR J SUSTAIN DEV RES, Volume 10, Issue 4, 2026, Article No: em0436

https://doi.org/10.29333/ejosdr/19260

Publication date: 01 Oct 2026

Online publication date: 16 Sep 2026

Article Views: 10

Article Downloads: 5

Open Access References How to cite this article

References

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M. ... Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous distributed systems [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1603.04467
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
Adandohoin, K. (2021). Tax transition in developing countries: Do value added tax and excises really work? International Economics and Economic Policy, 18(2), 379-424. https://doi.org/10.1007/s10368-021-00492-8
Aggarwal, S. (2024). The role of artificial intelligence in tax administration and compliance: A new era of digital taxation. Educational Administration: Theory and Practice, 30(1), 3831-3837. https://doi.org/10.53555/kuey.v30i1.7581
Alles, M., Brennan, G., Kogan, A., & Vasarhelyi, M. A. (2006). Continuous monitoring of business process controls. International Journal of Accounting Information Systems, 7(2), 137-161. https://doi.org/10.1016/j.accinf.2005.10.004
Allingham, M. G., & Sandmo, A. (1972). Income tax evasion: A theoretical analysis. Journal of Public Economics, 1(3-4), 323-338. https://doi.org/10.1016/0047-2727(72)90010-2
Alm, J., Kirchler, E., & Muehlbacher, S. (2012). Combining psychology and economics in the analysis of compliance. Economic Analysis and Policy, 42(2), 133-151. https://doi.org/10.1016/S0313-5926(12)50016-0
Anjarwi, A. W., T, S., Baridwan, Z., & Iqbal, S. (2025). The deterrent role of social norms on tax evasion in Indonesia. Deviant Behavior, 46(7), 927-946. https://doi.org/10.1080/01639625.2024.2375017
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Aslett, J., Hamilton, S., Gonzalez, I., Hadwick, D., & Hardy, M. A. (2024). Understanding artificial intelligence in tax and customs administration (Technical Notes and Manuals No. 2024/006). International Monetary Fund. https://doi.org/10.5089/9798400290435.005
Belahouaoui, R., & Alm, J. (2025). Tax fraud detection using artificial intelligence-based technologies: Trends and implications. Journal of Risk and Financial Management, 18(9), Article 502. https://doi.org/10.3390/jrfm18090502
Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence and Tax Administration 3.0: Improving tax compliance behavior, a systematic literature review using textometry (2016–2023). Accounting Research Journal, 37(2), 172-191. https://doi.org/10.1108/ARJ-12-2023-0372
Bellon, M., Dabla-Norris, E., Khalid, S., & Lima, F. (2022). Digitalization to improve tax compliance: Evidence from VAT e-invoicing in Peru. Journal of Public Economics, 210, Article 104661. https://doi.org/10.1016/j.jpubeco.2022.104661
Bellon, M., Dabla-Norris, E., & Khalid, S. (2023). Technology and tax compliance spillovers: Evidence from a VAT e-invoicing reform in Peru. Journal of Economic Behavior & Organization, 212, 756-777. https://doi.org/10.1016/j.jebo.2023.06.004
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50
Bird, R. M., & Zolt, E. M. (2008). Technology and taxation in developing countries: From hand to mouse. National Tax Journal, 61(4), 791-821.
Bojanc, R., Pucihar, A., & Lenart, G. (2024). E-invoicing: A catalyst for digitalization and sustainability. Organizacija, 57(1), 3-19. https://doi.org/10.2478/orga-2024-0001
Braithwaite, V. (Ed.). (2003). Taxing democracy: Understanding tax avoidance and evasion. Ashgate.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Chen, Y., & Lei, S. (2025). Tax avoidance opportunity for multinational enterprises: Effects of digitalized tax administration in China. Journal of International Financial Markets, Institutions and Money, 102, Article 102177. https://doi.org/10.1016/j.intfin.2025.102177
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Feld, L. P., & Frey, B. S. (2002). Trust breeds trust: How taxpayers are treated. Economics of Governance, 3(2), 87-99. https://doi.org/10.1007/s101010100032
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Grana, J., Sarzosa, M., Plumley, A., & Rodriguez, D. (2025). The comprehensive general indirect effect of auditing individual income tax returns (IRS SOI Working Paper). U.S. Department of the Treasury, Internal Revenue Service.
Gu, Y., Dai, J., & Vasarhelyi, M. A. (2023). Audit 4.0-based ESG assurance: An example of using satellite images on GHG emissions. International Journal of Accounting Information Systems, 50, Article 100625. https://doi.org/10.1016/j.accinf.2023.100625
Hai, B., Shi, H., Piao, T., & Dou, Z. (2024). Digitalization of tax administration and its impact on corporate ESG performance. Finance Research Letters, 68, Article 105791. https://doi.org/10.1016/j.frl.2024.105791
Hasan, A., Sheikh, N., & Farooq, M. B. (2024). Exploring stakeholder perceptions of tax reform failures and their proposed solutions: A developing country perspective. Meditari Accountancy Research, 32(3), 721-755. https://doi.org/10.1108/MEDAR-03-2023-1961
Kabir, M. R. (2021). Behavioral intention to adopt blockchain for a transparent and effective taxing system. Journal of Global Operations and Strategic Sourcing, 14(1), 170-201. https://doi.org/10.1108/JGOSS-08-2020-0050
Kieslich, K., Keller, B., & Starke, C. (2022). Artificial intelligence ethics by design: Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data & Society, 9(1). https://doi.org/10.1177/20539517221092956
Kirchler, E. (2007). The economic psychology of tax behaviour. Cambridge University Press. https://doi.org/10.1017/CBO9780511628238
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
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422). IEEE. https://doi.org/10.1109/ICDM.2008.17
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in neural information processing systems (Vol. 30, pp. 4765-4774).
McKinsey & Company. (2021). The future of tax: How AI and automation are transforming tax functions.
Murphy, K. (2005). Regulating more effectively: The relationship between procedural justice, legitimacy, and tax non-compliance. Journal of Law and Society, 32(4), 562-589. https://doi.org/10.1111/j.1467-6478.2005.00338.x
Nannini, L., Balayn, A., & Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the EU, US, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1198-1212). Association for Computing Machinery. https://doi.org/10.1145/3593013.3594074
Neuman, E. L., & Sheu, R. J. (2022). Big data analytics in IRS audit procedures and its effects on tax compliance: A moderated mediation analysis. Journal of the American Taxation Association, 44(2), 97-113. https://doi.org/10.2308/JATA-2020-038
OECD. (2015). Addressing the tax challenges of the digital economy, Action 1: 2015 final report (OECD/G20 Base Erosion and Profit Shifting Project). OECD Publishing. https://doi.org/10.1787/9789264241046-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
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly influence in the field of management: A bibliometric analysis. Journal of Management, 34(4), 641-720. https://doi.org/10.1177/0149206308319533
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
Shi, W., & Zhang, Z. (2025). Exploring the role of digital governance: The effect of audit digitalization on firms’ internal control weaknesses in China. International Journal of Accounting Information Systems, 56, Article 100756. https://doi.org/10.1016/j.accinf.2025.100756
Supriyati, S., Hapsari, I., & Nahumury, J. (2024). The influence of trust in the government, perceived fairness, and tax morale on taxpayer compliance: Implications for budget formation. Public and Municipal Finance, 13(2), 129-139. https://doi.org/10.21511/pmf.13(2).2024.11
Thaha, A. R., Antoro, A. F. S., & Muhtarom. (2023). Analytical review of tax compliance studies in the SMEs sector: A bibliometric approach. Journal of Tax Reform, 9(3), 398-412. https://doi.org/10.15826/jtr.2023.9.3.149
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
Tu, W., Du, A. M., & Saddler, S. B. (2025). Digital tax administration, investor risk perception, and stock return volatility. International Review of Economics & Finance, 103, Article 104434. https://doi.org/10.1016/j.iref.2025.104434
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717743530
Wang, X., Chan, K. F., Chang, M., Shan, Y. G., & Yang, J. W. (2025). Does tax-aggressive behavior motivate more CSR engagement? Economics & Politics, 37(2), 669-684. https://doi.org/10.1111/ecpo.12333
Yanto, H., Hajawiyah, A., & Baroroh, N. (2025). The reciprocal effect of environmental, social, and governance (ESG) practices and tax aggressiveness in Indonesian and Malaysian companies. Problems and Perspectives in Management, 23(1), 339-351. https://doi.org/10.21511/ppm.23(1).2025.25
Zhang, X., Liu, Y., Yu, S., Lin, O., & Meng, L. (2025). Impact of environmental protection tax on enterprise digital transformation: Evidence from Chinese listed firms. International Review of Economics & Finance, 97, Article 103743. https://doi.org/10.1016/j.iref.2024.103743
Zhou, Y., & Hao, Y. (2025). Big data tax collection and administration and quality of corporate accounting information. Finance Research Letters, 85, Article 108041. https://doi.org/10.1016/j.frl.2025.108041
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629

How to cite this article

APA

Zaim, H., & Sahbani, S. (2026). Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025). European Journal of Sustainable Development Research, 10(4), em0436. https://doi.org/10.29333/ejosdr/19260

Vancouver

Zaim H, Sahbani S. Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025). EUR J SUSTAIN DEV RES. 2026;10(4):em0436. https://doi.org/10.29333/ejosdr/19260

AMA

Zaim H, Sahbani S. Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025). EUR J SUSTAIN DEV RES. 2026;10(4), em0436. https://doi.org/10.29333/ejosdr/19260

Chicago

Zaim, Houda, and Siham Sahbani. "Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025)". European Journal of Sustainable Development Research 2026 10 no. 4 (2026): em0436. https://doi.org/10.29333/ejosdr/19260

Harvard

Zaim, H., and Sahbani, S. (2026). Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025). European Journal of Sustainable Development Research, 10(4), em0436. https://doi.org/10.29333/ejosdr/19260

MLA

Zaim, Houda et al. "Artificial intelligence in tax compliance: A bibliometric mapping and research agenda (2015–2025)". European Journal of Sustainable Development Research, vol. 10, no. 4, 2026, em0436. https://doi.org/10.29333/ejosdr/19260