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

Camino González Vasco (), María Jesús Delgado Rodríguez () and de Lucas Santos Sonia ()
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Camino González Vasco: Instituto de Estudios Fiscales
María Jesús Delgado Rodríguez: Universidad Rey Juan Carlos

Hacienda Pública Española / Review of Public Economics, 2021, vol. 239, issue 4, 127-157

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.

Keywords: Personal income tax; Tax compliance; Data mining techniques; Multilayer perceptron; Decision trees; Fiscal fraud detection; Tax evaluation. (search for similar items in EconPapers)
JEL-codes: C38 C55 H24 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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