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Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

1
Instituto de Estudios Fiscales, Universidad Rey Juan Carlos, 28670 Madrid, Spain
2
Economía de la Empresa (ADO), Economía Aplicada II y Fundamentos Análisis Económico, Universidad Rey Juan Carlos, 28670 Madrid, Spain
3
Facultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(4), 86; https://doi.org/10.3390/fi11040086
Received: 27 February 2019 / Revised: 21 March 2019 / Accepted: 26 March 2019 / Published: 30 March 2019
(This article belongs to the Special Issue Future Intelligent Systems and Networks 2019)
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Abstract

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud. View Full-Text
Keywords: tax fraud; neural networks; intelligent systems and networks; personal income tax; prediction tax fraud; neural networks; intelligent systems and networks; personal income tax; prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pérez López, C.; Delgado Rodríguez, M.J.; de Lucas Santos, S. Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers. Future Internet 2019, 11, 86.

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