Journal of Tax Research

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Abstract

Tax fraud includes a large spectrum of methods including denying the facts and realities, claiming wrong information and performing financial businesses without considering legal frameworks. Nowadays, with the development of tax systems and the large volume of tax data, it is necessary to have tools to process this large data and to exploit information and knowledge. According to tax policies, especially in value-added tax resource, the rate of tax fraud is increasing. Based on the investigations, researchers use standard methods such as association rules, clustering, neural networks, decision trees, Bayesian networks, regression and genetics to detect tax fraud. Because of large volume of tax database, most of the studied algorithms are time consuming. At first, Apriori Algorithm was used. This algorithm was one of the unsupervised learning models and association rules. It is used to detect suspicious behavior of tax fraudsters. Secondly, a system for tax fraud detection based on Bayesian networks is presented and its performance is improved using parallel processing techniques. Results of the study show that using available parallel processing patterns improve the execution time of tax fraud detection algorithm considerably.

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Abstract

Many believe that de facto Corporations are not commercial due to the lack of legal personality. Here, they are viewed through a new approach from the tax and state rights perspective in order to achieve a healthy economy in the framework of companies’ law. The Commercial companies’ income is the most important tax resources of governments. The de facto corporations’ failure to register themselves as taxpayers result in major tax evasion, tax discrimination, and finally discontent among other taxpayers. The legislators must take actions to reconnaissance legal personality of these companies which have not been registered for the purpose of tax fraud. Since many jurists believe that social order depends on non-recognition of legal personality of these companies, by resorting to such an approach, a question arises from tax law perspective; does social order accepts the above opinion? And is it possible to effectively identify legal personality of these companies by using tax regulations? These questions and their answers are the main subject of this paper. The aforesaid tax approach is the reasonable logic to identify the legal personality of de facto Corporations.

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Today, one of the most important challenges of tax system is tax evasion of the taxpayers, which is an obstacle to the proper administration of the country. There are many ways to prevent tax evasion, one of which is the legal remedy for issuance under Article 161 of the Direct Taxes Terms and Conditions. This important legal entity can prevent tax evasion and prevent tax fraud by seizing the property of taxpayers. This article examines the conditions of the mentioned entity with some similar institutions in the legal system and therefore, by looking at its strengths and weaknesses, seeks to provide a more favorable model.

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One of the management measures to reduce tax liabilities is non-payment of taxes through tax fraud. Because personality factors may play a role in explaining tax ethics, examining personality traits and aspects of tax fraud can help to better understand the factors that influence tax decisions. The main purpose of this study is to provide a model for detecting tax fraud based on the personality types of corporate financial managers using the neural network approach. The statistical population of the study consists of all financial managers of listed and non-listed companies in 2020 who are not exempt from taxes. The information about financial managers was collected through a questionnaire and analyzed using SPSS software version 21 and MATLAB software version 2015. The results indicate that the neural network model designed with 10 neurons in the hidden layer with an accuracy of 79.5% has the ability to detect tax fraud committed by financial managers of companies. Also according to the results of the regression model test, personality traits of neuroticism, extraversion, flexibility and agreement have a positive and significant effect on tax fraud and the personality trait of conscientious has a negative effect on tax fraud.
 

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Tax revenues are one of the most important sources of funding for governments, and one of the reasons why the realized tax revenue is lower than the expected amount is tax fraud. In order to reduce tax frauds, tax audits should be done in the tax organization, so in this research, a tax audit model has been presented with regard to the effects of psychological safety and knowledge of tax audits during the detection of tax frauds. The method used in this research is qualitative, foundational data and inductive analysis. The statistical population of this research includes experts in the tax audit profession, which includes 25 experts. The questionnaire information was completed in 1401. And according to the analysis, it was observed that smart and digital provincial tax, psychological safety of tax auditors, knowledge of tax audit, detection of tax frauds, sharing information related to fraud, audit knowledge of the tax director general and psychological security created by the director general. The results show that the biggest impact on tax audit is related to the psychological safety of tax auditors and then the discovery of tax frauds.