Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran

Rahimikia, Eghbal; Mohammadi, Shahriar; Rahmani, Teymur; Ghazanfari, Mehdi

doi:10.1016/j.accinf.2016.12.002

Cited by 43 publications

(22 citation statements)

References 13 publications

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“…The Chile administration exploits neural networks, Bayesian networks, and decision trees to detect tax evasion performed with the use of fake invoices [8]. The Iranian tax administration adopts a hybrid intelligent system that combines multilayer perceptron neural networks, support vector machines, and logistic regression to detect corporate tax evasion [38]. Kim et al [39] develop multi-class financial misstatement detection models for discovering fraudulent activities.…”

Section: B Data Mining and Machine Learning Approachesmentioning

confidence: 99%

Combining Network Visualization and Data Mining for Tax Risk Assessment

et al. 2020

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This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency. INDEX TERMS Tax risk assessment, tax evasion discovery, network visualization, data mining, human-computer interaction.

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Section: B Data Mining and Machine Learning Approachesmentioning

confidence: 99%

Combining Network Visualization and Data Mining for Tax Risk Assessment

et al. 2020

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“…The SVM method finds the optimal hyperplane that separates feature points from two different classes with the largest margin possible in the feature space. Classification cases are linearly separable, the separating function sought is a linear function using (3).…”

Section: Classification Processmentioning

confidence: 99%

“…The researchers [2], conducted a study developing a system that can measure the dimensions of billboards without physically touching them automatically to calculate tax in Indonesia. The researchers [3], conducted a study using a hybrid intelligence system to detect tax evasion from corporate taxpayers, namely taxpayers who do business beverages and textile in Iran. The researchers [4], conducted a study investigating classic tax evasion cases using several methods aimed at classifying tax evasion behavior based on the network that has been simulated with real data.…”

Section: Introductionmentioning

confidence: 99%

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Jupri

Sarno

2020

IJEECS

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The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

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“…Supervisionado , [Jupri and Sarno 2018], [Xiangyu et al 2018], [Zhu et al 2018], [Wu et al 2019], [Rahimikia et al 2017], [Goumagias et al 2018], [Kleanthous and Chatzis 2019] 8 Não supervisionado [de Roux et al 2018], [Wei et al 2019], [Mehta et al 2019], [Vanhoeyveld et al 2020], [Mathews et al 2018], [Mehta et al 2018] 6…”

Section: Técnica Estudo Totalmentioning

confidence: 99%

“…Outro desafio apresentado por [Kleanthous andChatzis 2019, Wu et al 2019] diz respeito à falta de uma base pública de dados de fiscalização, isso dificulta o desenvolvimento de modelos de detecção, já que a maioria dos desenvolvedores não tem acesso a esse tipo de dado. [Didimo et al 2019, Rahimikia et al 2017, de Roux et al 2018, Wu et al 2019 afirmam que o processo de auditoria é muito demorado, isso atrapalha a evolução dos modelos de predição, dado que a validação in loco leva em média de 4 a 6 meses.…”

Section: Desafios Identificadosunclassified

O Uso de Inteligência Artificial no Combate à Evasão Fiscal: Uma Revisão Sistemática da Literatura

Soares¹,

Cunha²,

Filho³

2020

Anais Do Workshop De Computação Aplicada Em Governo Eletrônico (WCGE 2020)

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Evasão fiscal é um problema enfrentado pelos governos de todo mundo. O uso de IA tem sido uma alternativa viável para combater esse problema. Neste contexto, esse trabalho tem como objetivo identificar como a IA auxilia no combate à evasão fiscal. Para isso, foi desenvolvido uma revisão sistemática da literatura, encontrando 738 artigos, dos quais 18 foram selecionados para esta revisão. A revisão investiga os tipos de algoritmos utilizados, a forma de validação dos modelos e os desafios encontrados durante as pesquisas. Os resultados demonstram um panorama geral das últimas pesquisas desta área, as lacunas nas pesquisas atuais e classificação das técnicas utilizadas recentemente.

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Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran

Cited by 43 publications

References 13 publications

Combining Network Visualization and Data Mining for Tax Risk Assessment

Combining Network Visualization and Data Mining for Tax Risk Assessment

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

O Uso de Inteligência Artificial no Combate à Evasão Fiscal: Uma Revisão Sistemática da Literatura

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