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Fraud Detection Using Neural Networks: A Case Study of Income Tax

Author

Listed:
  • Belle Fille Murorunkwere

    (African Center of Excellence in Data Science, University of Rwanda, KK 737 Street, Gikondo, Kigali P.O. Box 4285, Rwanda)

  • Origene Tuyishimire

    (African Institute for Mathematical Sciences, KN 3 Street, Remera, Kigali P.O. Box 7150, Rwanda)

  • Dominique Haughton

    (Department of Mathematical Sciences and Global Studies, Bentley University, Watham, MA 02452-4705, USA
    Department of Mathematical Sciences and Global Studies, Université Paris 1 (SAMM), 75634 Paris, France
    Department of Mathematical Sciences and Global Studies, Université Toulouse 1 (TSE-R), 31042 Toulouse, France)

  • Joseph Nzabanita

    (Department of Mathematics, College of Science and Technology, University of Rwanda, KN 67 Street, Nyarugenge, Kigali P.O. Box 3900, Rwanda)

Abstract

Detecting tax fraud is a top objective for practically all tax agencies in order to maximize revenues and maintain a high level of compliance. Data mining, machine learning, and other approaches such as traditional random auditing have been used in many studies to deal with tax fraud. The goal of this study is to use Artificial Neural Networks to identify factors of tax fraud in income tax data. The results show that Artificial Neural Networks perform well in identifying tax fraud with an accuracy of 92%, a precision of 85%, a recall score of 99%, and an AUC-ROC of 95%. All businesses, either cross-border or domestic, the period of the business, small businesses, and corporate businesses, are among the factors identified by the model to be more relevant to income tax fraud detection. This study is consistent with the previous closely related work in terms of features related to tax fraud where it covered all tax types together using different machine learning models. To the best of our knowledge, this study is the first to use Artificial Neural Networks to detect income tax fraud in Rwanda by comparing different parameters such as layers, batch size, and epochs and choosing the optimal ones that give better accuracy than others. For this study, a simple model with no hidden layers, softsign activation function performs better. The evidence from this study will help auditors in understanding the factors that contribute to income tax fraud which will reduce the audit time and cost, as well as recover money foregone in income tax fraud.

Suggested Citation

  • Belle Fille Murorunkwere & Origene Tuyishimire & Dominique Haughton & Joseph Nzabanita, 2022. "Fraud Detection Using Neural Networks: A Case Study of Income Tax," Future Internet, MDPI, vol. 14(6), pages 1-14, May.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:6:p:168-:d:828622
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    References listed on IDEAS

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    1. 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.
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