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Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques

Author

Listed:
  • Camino González Vasco

    (Instituto de Estudios Fiscales)

  • María Jesús Delgado Rodríguez

    (Universidad Rey Juan Carlos)

  • Sonia de Lucas Santos

    (Universidad Autónoma de Madrid)

Abstract

This paper proposes an analytical framework that combines dimension reduction and data mining techniques to obtain a sample segmentation according to potential fraud probability. In this regard, the purpose of this study is twofold. Firstly, it attempts to determine tax benefits that are more likely to be used by potential fraud taxpayers by means of investigating the Personal Income Tax structure. Secondly, it aims at characterizing through socioeconomic variables the segment profiles of potential fraud taxpayer to offer an audit selection strategy for improving tax compliance and improve tax design. An application to the annual Spanish Personal Income Tax sample designed by the Institute for Fiscal Studies is provided. Results obtained confirm that the combination of data mining techniques proposed offers valuable information to contribute to the study of tax fraud.

Suggested Citation

  • 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.
    • Handle: RePEc:hpe:journl:y:2021:v:239:i:4:p:127-157
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    References listed on IDEAS

    as
    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    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.
    3. Joel Slemrod, 2019. "Tax Compliance and Enforcement," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 904-954, December.
    4. Sara Torregrosa, 2015. "Bypassing progressive taxation: fraud and base erosion in the Spanish income tax (1970-2001)," Working Papers 2015/31, Institut d'Economia de Barcelona (IEB).
    5. Fox, William F. & Luna, LeAnn & Schaur, Georg, 2014. "Destination taxation and evasion: Evidence from U.S. inter-state commodity flows," Journal of Accounting and Economics, Elsevier, vol. 57(1), pages 43-57.
    6. David López-Rodríguez & Cristina García Ciria, 2018. "Estructura impositiva de España en el contexto de la Unión Europea," Occasional Papers 1810, Banco de España.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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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    More about this item

    Keywords

    Personal income tax; Tax compliance; Data mining techniques; Multilayer perceptron; Decision trees; Fiscal fraud detection; Tax evaluation.;
    All these keywords.

    JEL classification:

    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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