Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers
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 sp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/11/4/86 |
| _version_ | 1818952108770066432 |
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| author | César Pérez López María Jesús Delgado Rodríguez Sonia de Lucas Santos |
| author_facet | César Pérez López María Jesús Delgado Rodríguez Sonia de Lucas Santos |
| author_sort | César Pérez López |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T09:45:08Z |
| format | Article |
| id | doaj.art-1c03b30aacde474184c3776dbdaf1736 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-12-20T09:45:08Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-1c03b30aacde474184c3776dbdaf17362022-12-21T19:44:46ZengMDPI AGFuture Internet1999-59032019-03-011148610.3390/fi11040086fi11040086Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income TaxpayersCésar Pérez López0María Jesús Delgado Rodríguez1Sonia de Lucas Santos2Instituto de Estudios Fiscales, Universidad Rey Juan Carlos, 28670 Madrid, SpainEconomía de la Empresa (ADO), Economía Aplicada II y Fundamentos Análisis Económico, Universidad Rey Juan Carlos, 28670 Madrid, SpainFacultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, SpainThe 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.https://www.mdpi.com/1999-5903/11/4/86tax fraudneural networksintelligent systems and networkspersonal income taxprediction |
| spellingShingle | César Pérez López María Jesús Delgado Rodríguez Sonia de Lucas Santos Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers Future Internet tax fraud neural networks intelligent systems and networks personal income tax prediction |
| title | Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers |
| title_full | Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers |
| title_fullStr | Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers |
| title_full_unstemmed | Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers |
| title_short | Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers |
| title_sort | tax fraud detection through neural networks an application using a sample of personal income taxpayers |
| topic | tax fraud neural networks intelligent systems and networks personal income tax prediction |
| url | https://www.mdpi.com/1999-5903/11/4/86 |
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