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

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