Artificial Intelligence and Tax Governance: Toward Responsible Digital Fiscal Administration in a Southern African Country

Dr. Bruno Couto De Abreu Rodolfo

doi:10.34257/GJMBRAVOL25IS6PG11

Authors

Dr. Bruno Couto De Abreu Rodolfo

Abstract

This study examines the transformative role of Artificial Intelligence (AI) in modernising tax administration and enhancing fiscal governance in emerging economies, with an empirical focus on Country in the Southern African region. It explores how AI-driven digital fiscal administration is reshaping the tax landscape through automation, predictive analytics, and data-driven decision-making. Using a qualitative and exploratory design, the research integrates a systematic literature review with comparative case studies from Brazil and Singapore to contextualise international lessons for developing economies. The findings reveal that AI can substantially improve operational efficiency, predictive auditing, and transparency in tax collection processes, highlighting its transformative potential within fiscal institutions. These findings directly inform the development of the proposed AI-Driven Tax Governance Framework, which connects technological innovation with institutional, legal, and citizen-centric dimensions of responsible AI adoption.

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Artificial Intelligence and Tax Governance: Toward Responsible Digital Fiscal Administration in a Southern African Country. (2026). Global Journal of Management and Business Research, 25(A6), 11-19. https://doi.org/10.34257/GJMBRAVOL25IS6PG11

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References

D Almeida (2021) A inteligência artificial aplicada à tributação: Desafios e perspectivas jurídicas. 27(3), 45-62.

C Alves (2022) Ética e responsabilidade na automação de decisões fiscais. 56(4), 211-229.

P Baccarin, D Teixeira, L Amaral (2019) Inteligência artificial na gestão tributária: Análise de casos e tendências emergentes. 6(1), 112-126.

L Bardin (2016) Análise de conteúdo. 70.

J Creswell (2014) Research design: Qualitative, quantitative, and mixed methods approaches.

J Creswell, C Poth (2018) Qualitative inquiry and research design: Choosing among five approaches.

M De Sousa, J De Siqueira (2020) Desafios da aplicação da inteligência artificial no sistema tributário brasileiro. 8(1), 101-119.

Arthur Dornelas, Luciana Campos, Karla Figueiredo (2022) Modelos para Previsão Tributária Utilizando Redes Neurais LSTM. 95-105.

F Engelmann, M Lietz, M &dahlem (2020) Algorithmic accountability and transparency in tax administration. 36(4), 223-240.

(2021) EUROPEAN UNION'S ETHICS GUIDELINES FOR AI. 17-21.

(2021) Teasdale, Anthony Laurence, (born 4 June 1957), Director General, European Parliamentary Research Service, European Parliament, since 2013.

L Gao, H He, D Zhang (2020) Taxation in the Age of Artificial Intelligence: Opportunities, Challenges and Governance. 7, 71.

M Guevara (2019) Unknown Title.

E Wilson (2021) The Trustees of the Nelson Dance Family Settlement v The Commissioners for HM Revenue & Customs (HMRC). 15(4), 231-234.

(2024) Understanding Artificial Intelligence in Tax and Customs Administration.

F Ippolito, G Manzoni, S &zambon (2020) Tax Crime Prediction with Machine Learning: Evidence from Brazil. 8(2), 45-62.

Barbara Kitchenham, O Pearl Brereton, David Budgen, Mark Turner, John Bailey, Stephen Linkman (2021) Systematic literature reviews in software engineering - A systematic literature review. 51(1), 7-15.

M Lietz (2021) Bias and fairness in AI-based decision-making for taxation. 87(2), 392-410.

G Manikandan, R Maheswaran (2019) Artificial intelligence in public-sector transparency: A case for tax administration. 32(6), 501-518.

H Munir, M Setiawan (2020) Strengthening tax compliance through intelligent automation: Evidence from emerging economies. 3(2), 72-89.

Richard Woodward (2022) The OECD and global governance. 81-98.

(2023) Organisation for Economic Co-Operation and Development (OECD).

S Oliveira (2022) Taxation of artificial intelligence by its use and the implications for tax administration in validating its acts.

M Page, J Mckenzie, P Bossuyt, I Boutron, T Hoffmann, C Mulrow, L Shamseer, J Tetzlaff, E Akl, S Brennan, R Chou, J Glanville, J Grimshaw, A Hróbjartsson, M Lalu, T Li, E Loder, E Mayo-Wilson, S Mcdonald, . Moher, D (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. 372, 71.

D Resnik (2018) The ethics of research: An introduction.

L Rodrigues, E Gouveia (2020) Redes neurais e automatização da análise tributária: O novo paradigma digital. 35(1), 55-69.

P Rossi, M Torzilli (2019) Ethical AI governance in public finance: Justice and accountability. 21(3), 201-219.

D Tranfield, D Denyer, P Smart (2003) Towards a methodology for developing evidence informed management knowledge by means of systematic review. 14(3), 207-222.

Andre L Antunes, David Prieto, Pedro E Antunes (2021) Malignancy of a Benign Tumor. 34(3).

L Wang, D Kim, S Park (2021) Enhancing equity and efficiency in digital taxation through AI tools. 9(4), 310-326.

Otávio Xavier, Sandrerley Pires, Thyago Marques, Anderson Soares (2022) Tax evasion identification using open data and artificial intelligence. 56(3), 426-440.

Y Zhou, J Li, K Chen (2019) Automation and innovation in tax management: The role of artificial intelligence. 47(5), 827-848.

F Zilveti (2019) Automação fiscal e inteligência artificial: Um novo modelo de fiscalização tributária. 3(1), 9-28.