IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v8y2024i6p2276-2282.html
   My bibliography  Save this article

An Ensemble Machine Learning Model to Detect Tax Fraud: Conceptual Framework

Author

Listed:
  • Kudzanai Charity Muchuchuti

    (Lecturer, BA ISAGO University, Department of Accounting and Finance, Botswana)

Abstract

Most governments throughout the world, especially in developing and underdeveloped countries, depend more on tax revenue to fund public expenditure and investments. In the wake of Covid19, even governments that did not depend largely on tax revenue are forced to do that since their other sources of income were affected by the pandemic as borders were closed and nations were on lockdowns. Research, however, has shown that tax fraud is rampant especially in less developed countries. Traditional methods of detecting tax fraud are costly and they largely depend on the experts’ past experience. This renders them less effective where new mechanisms of tax fraud are involved. In this work I provide a conceptual framework on the use of ensemble machine learning models to detect tax fraud. I use decision trees, support vector machines and logistic regression as the base models. I hypothesize that ensemble methods outperform unsupervised machine learning models and the use of a single algorithm under supervised machine learning models. The outcomes of this research will serve to provide a framework that will help tax authorities to detect tax fraud thereby increasing the revenue collected.

Suggested Citation

  • Kudzanai Charity Muchuchuti, 2024. "An Ensemble Machine Learning Model to Detect Tax Fraud: Conceptual Framework," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 2276-2282, June.
  • Handle: RePEc:bcp:journl:v:8:y:2024:i:6:p:2276-2282
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-6/2276-2282.pdf
    Download Restriction: no

    File URL: https://rsisinternational.org/journals/ijriss/articles/an-ensemble-machine-learning-model-to-detect-tax-fraud-conceptual-framework/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James Alm, 2012. "Measuring, explaining, and controlling tax evasion: lessons from theory, experiments, and field studies," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(1), pages 54-77, February.
    2. César Pérez López & María Jesús Delgado Rodríguez & Sonia de Lucas Santos, 2019. "Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers," Future Internet, MDPI, vol. 11(4), pages 1-13, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jacquemet, N. & Luchini, S. & Malézieux, A. & Shogren, J.F., 2020. "Who’ll stop lying under oath? Empirical evidence from tax evasion games," European Economic Review, Elsevier, vol. 124(C).
    2. Schnellenbach, Jan & Schubert, Christian, 2015. "Behavioral political economy: A survey," European Journal of Political Economy, Elsevier, vol. 40(PB), pages 395-417.
    3. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "On evasion behaviour in public transport: Dissatisfaction or contagion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 626-651.
    4. Gunter, Samara, 2013. "State Earned Income Tax Credits and Participation in Regular and Informal Work," National Tax Journal, National Tax Association;National Tax Journal, vol. 66(1), pages 33-62, March.
    5. Laszlo Goerke, 2014. "Tax Evasion by Individuals," IAAEU Discussion Papers 201409, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).
    6. Jan-Emmanuel De Neve & Clément Imbert & Johannes Spinnewijn & Teodora Tsankova & Maarten Luts, 2021. "How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1425-1463.
    7. Alm, James & Bruner, David M. & McKee, Michael, 2016. "Honesty or dishonesty of taxpayer communications in an enforcement regime," Journal of Economic Psychology, Elsevier, vol. 56(C), pages 85-96.
    8. Martin Abraham & Kerstin Lorek & Friedemann Richter & Matthias Wrede, 2017. "Collusive tax evasion and social norms," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(2), pages 179-197, April.
    9. Montalvo, José G. & Piolatto, Amedeo & Raya, Josep, 2020. "Transaction-tax evasion in the housing market," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    10. Bazart, C. & Bonein, A., 2014. "Reciprocal relationships in tax compliance decisions," Journal of Economic Psychology, Elsevier, vol. 40(C), pages 83-102.
    11. Michele Lalla & Patrizio Frederic & Daniela Mantovani, 2022. "The inextricable association of measurement errors and tax evasion as examined through a microanalysis of survey data matched with fiscal data: a case study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1375-1401, December.
    12. Langenmayr, Dominika, 2017. "Voluntary disclosure of evaded taxes — Increasing revenue, or increasing incentives to evade?," Journal of Public Economics, Elsevier, vol. 151(C), pages 110-125.
    13. repec:osf:osfxxx:5as84_v1 is not listed on IDEAS
    14. Konda, Laura & Patel, Elena & Seegert, Nathan, 2022. "Tax enforcement and the intended and unintended consequences of information disclosure," Journal of Public Economics, Elsevier, vol. 212(C).
    15. Guizhou Wang & Kjell Hausken, 2021. "Governmental Taxation of Households Choosing between a National Currency and a Cryptocurrency," Games, MDPI, vol. 12(2), pages 1-24, April.
    16. James Alm, 2014. "Does an uncertain tax system encourage üaggressive tax planningý?," Economic Analysis and Policy, Elsevier, vol. 44(1), pages 30-38.
    17. Hallsworth, Michael & List, John A. & Metcalfe, Robert D. & Vlaev, Ivo, 2017. "The behavioralist as tax collector: Using natural field experiments to enhance tax compliance," Journal of Public Economics, Elsevier, vol. 148(C), pages 14-31.
    18. Colin C Williams, 2021. "Explaining And Tackling Undeclared Work In South East Europe: Lessons From A 2019 Eurobarometer Survey," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(1), pages 1-18.
    19. Miloš Fišar & Tommaso Reggiani & Fabio Sabatini & Jiří Špalek, 2022. "Media negativity bias and tax compliance: experimental evidence," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(5), pages 1160-1212, October.
    20. Irene Di Marzio & Sauro Mocetti & Enrico Rubolino, 2024. "The market externalities of tax evasion," Temi di discussione (Economic working papers) 1467, Bank of Italy, Economic Research and International Relations Area.
    21. Andualem T Mengistu & Kiflu G Molla & Giulia Mascagni, 2022. "Trade Tax Evasion and the Tax Rate: Evidence from Transaction-level Trade Data," Journal of African Economies, Centre for the Study of African Economies, vol. 31(1), pages 94-122.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bcp:journl:v:8:y:2024:i:6:p:2276-2282. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Pawan Verma (email available below). General contact details of provider: https://rsisinternational.org/journals/ijriss/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.