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Modelización de los factores que afectan al fraude fiscal con técnicas de minería de datos: aplicación al Impuesto de la Renta en España

Author

Listed:
  • César Pérez López

    (Instituto de Estudios Fiscales- Universidad Rey Juan Carlos)

  • María Jesús Delgado Rodríguez

    (Universidad Rey Juan Carlos)

  • Sonia de Lucas Santos

    (Universidad Autónoma de Madrid)

Abstract

This paper presents a proposal to model and predict the behavior of Personal Income Tax (IRPF) tax-payers with data mining techniques. Decision trees and discriminant analysis are combined to quantify each taxpayer's propensity to fraud using the tax components with the highest incidence of fraud. The model achieves an average efficiency in predictions of more than 89%, allowing respondents to be segmented by level of propensity to fraud. The proposal can be used in the audit and control process carried out by the Tax Agency.

Suggested Citation

  • 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.
    • Handle: RePEc:hpe:journl:y:2023:v:246:i:3:p:137-164
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    References listed on IDEAS

    as
    1. James Alm, 2021. "Tax evasion, technology, and inequality," Economics of Governance, Springer, vol. 22(4), pages 321-343, December.
    2. Andreas BUEHN & Friedrich SCHNEIDER, 2016. "Size and Development of Tax Evasion in 38 OECD Coutries: What do we (not) know?," Journal of Economics and Political Economy, KSP Journals, vol. 3(1), pages 1-11, March.
    3. Amakoe D. Alognon & Antonios M. Koumpias & Jorge Martínez-Vázquez, 2021. "The Impact of Plastic Money Use on VAT Compliance: Evidence from EU Countries," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 5-26, November.
    4. 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.
    5. Sara Torregrosa-Hetland, 2020. "Inequality in tax evasion: the case of the Spanish income tax," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 28(83), pages 89-109, March.
    6. Pierce O’Reilly & Kevin Parra Ramírez & Michael A. Stemmer, 2021. "Exchange of Information and Bank Deposits in International Financial Centres," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 27-69, November.
    7. Prichard,Wilson & Custers,Anna Louise & Dom,Roel & Davenport,Stephen R. & Roscitt,Michael Anthony, 2019. "Innovations in Tax Compliance : Conceptual Framework," Policy Research Working Paper Series 9032, The World Bank.
    8. 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.
    9. Mr. John D Brondolo & Annette Chooi & Trevor Schloss & Anthony Siouclis, 2022. "Compliance Risk Management: Developing Compliance Improvement Plans," IMF Technical Notes and Manuals 2022/001, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Tax fraud; Data mining; Personal income tax; Prediction.;
    All these keywords.

    JEL classification:

    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance
    • 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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