文章基本信息

标题：Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers
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作者：César Pérez López ; María Jesús Delgado Rodríguez ; Sonia de Lucas Santos 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2019
卷号：11
期号：4
页码：1-13
DOI：10.3390/fi11040086
出版社：MDPI Publishing
摘要：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.
关键词：tax fraud; neural networks; intelligent systems and networks; personal income tax; prediction tax fraud ; neural networks ; intelligent systems and networks ; personal income tax ; prediction