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

César Pérez López (), María Jesús Delgado Rodríguez () and de Lucas Santos Sonia ()
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César Pérez López: Instituto de Estudios Fiscales- Universidad Rey Juan Carlos
María Jesús Delgado Rodríguez: Universidad Rey Juan Carlos

Hacienda Pública Española / Review of Public Economics, 2023, vol. 246, issue 3, 137-164

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.

Keywords: Tax fraud; Data mining; Personal income tax; Prediction. (search for similar items in EconPapers)
JEL-codes: C38 C55 H26 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hpe:journl:y:2023:v:246:i:3:p:137-164

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