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Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

Author

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  • César Pérez López

    (Instituto de Estudios Fiscales, Universidad Rey Juan Carlos, 28670 Madrid, Spain)

  • María Jesús Delgado Rodríguez

    (Economía de la Empresa (ADO), Economía Aplicada II y Fundamentos Análisis Económico, Universidad Rey Juan Carlos, 28670 Madrid, Spain)

  • Sonia de Lucas Santos

    (Facultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain)

Abstract

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:86-:d:218569
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    References listed on IDEAS

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    1. repec:bla:germec:v:11:y:2010:i::p:109-149 is not listed on IDEAS
    2. Mendoza, Juan P. & Wielhouwer, Jacco L. & Kirchler, Erich, 2017. "The backfiring effect of auditing on tax compliance," Journal of Economic Psychology, Elsevier, vol. 62(C), pages 284-294.
    3. Lars P. Feld & Friedrich Schneider, 2010. "Survey on the Shadow Economy and Undeclared Earnings in OECD Countries," German Economic Review, Verein für Socialpolitik, vol. 11(2), pages 109-149, May.
    4. Helmut Herwartz & Egle Tafenau & Friedrich Schneider, 2015. "One Share Fits All? Regional Variations in the Extent of the Shadow Economy in Europe," Regional Studies, Taylor & Francis Journals, vol. 49(9), pages 1575-1587, September.
    5. Félix Domínguez Barrero & Julio López Laborda & Fernando Rodrigo Sauco, 2014. ""El hueco que deja el Diablo": Una estimación del fraude en el IRPF con microdatos tributarios," Studies on the Spanish Economy eee2014-01, FEDEA.
    6. Félix Domínguez Barrero & Julio López Laborda & Fernando Rodrigo Sauco, 2015. "Fraude en el IRPF por fuentes de renta, 2005-2008: del impuesto sintético al impuesto dual," Studies on the Spanish Economy eee2015-14, FEDEA.
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    Cited by:

    1. 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.
    2. Camino González Vasco & María Jesús Delgado Rodríguez & Sonia de Lucas Santos, 2021. "Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 127-157, November.
    3. Carmen De-Pablos-Heredero, 2019. "Future Intelligent Systems and Networks," Future Internet, MDPI, vol. 11(6), pages 1-2, June.
    4. 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.
    5. César Pérez López & María Jesús Delgado Rodríguez & Sonia de Lucas Santos, 2023. "Modelización de los factores que afectan al fraude fiscal con técnicas de minería de datos: aplicación al Impuesto de la Renta en España," Hacienda Pública Española / Review of Public Economics, IEF, vol. 246(3), pages 137-164, September.

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