Open AccessSegmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques
Author(s) -
Celina González,
AUTHOR_ID,
Denis Covès,
Sonia de Lucas,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
hacienda pública española/hacienda pública española
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.185
H-Index - 12
eISSN - 2386-4176
pISSN - 0210-1173
DOI - 10.7866/hpe-rpe.21.4.4
Subject(s) - taxpayer , audit , dimension (graph theory) , sample (material) , income tax , tax planning , business , personal income , socioeconomic status , accounting , international taxation , public economics , economics , tax reform , population , chemistry , demography , mathematics , chromatography , sociology , pure mathematics , macroeconomics , economic growth
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.