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

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, 28670 Madrid, Spain
María Jesús Delgado Rodríguez: Economía de la Empresa (ADO), Economía Aplicada II y Fundamentos Análisis Económico, Universidad Rey Juan Carlos, 28670 Madrid, Spain

Future Internet, 2019, vol. 11, issue 4, 1-13

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.

Keywords: tax fraud; neural networks; intelligent systems and networks; personal income tax; prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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