Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability.This is done by ...Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability.This is done by either reducing sales or increasing purchases.According to recent studies,governments lose over$500 billion annually due to tax fraud.A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies.Most of the work done in tax fraud using machine learning is centered on supervised models.A significant drawback of this approach is that it requires tax returns that have been previously audited,which constitutes a small percentage of the data.Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns,though ignore whether the tax returns are fraudulent or not.Therefore,unsupervised models are limited in their usefulness if they are used independently to detect tax fraud.The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns.The framework consists of four modules:A supervised module,which utilizes a tree-based model to extract knowledge from the data;an unsupervised module,which calculates anomaly scores;a behavioral module,which assigns a compliance score for each taxpayer;and a prediction module,which utilizes the output of the previous modules to output a probability of fraud for each tax return.We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority.展开更多
The fraudulent behavior of taxpayers impacts negatively the resources available to finance public services. It creates distortions of competition and inequality, harming honest taxpayers. Such behavior requires the go...The fraudulent behavior of taxpayers impacts negatively the resources available to finance public services. It creates distortions of competition and inequality, harming honest taxpayers. Such behavior requires the government intervention to bring order and establish a fiscal justice. This study emphasizes the determination of the interactions linking taxpayers with tax authorities. We try to see how fiscal audit can influence taxpayers’ fraudulent behavior. First of all, we present a theoretical study of a model pre established by other authors. We have released some conditions of this model and we have introduced a new parameter reflecting the efficiency of tax control;we found that the efficiency of a fiscal control have an important effect on these interactions. Basing on the fact that the detection of fraudulent taxpayers is the most difficult step in fiscal control, We established a new approach using DATA MINING process in order to improve fiscal control efficiency. We found results that reflect fairly the conduct of taxpayers that we have tested based on actual statistics. The results are reliable.展开更多
基金This work was supported by ZATCAThe author is grateful for the help provided by the risk and intelligence department as well as the continued support of the governor for advancing the field of AI and machine learning in government entities。
文摘Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability.This is done by either reducing sales or increasing purchases.According to recent studies,governments lose over$500 billion annually due to tax fraud.A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies.Most of the work done in tax fraud using machine learning is centered on supervised models.A significant drawback of this approach is that it requires tax returns that have been previously audited,which constitutes a small percentage of the data.Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns,though ignore whether the tax returns are fraudulent or not.Therefore,unsupervised models are limited in their usefulness if they are used independently to detect tax fraud.The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns.The framework consists of four modules:A supervised module,which utilizes a tree-based model to extract knowledge from the data;an unsupervised module,which calculates anomaly scores;a behavioral module,which assigns a compliance score for each taxpayer;and a prediction module,which utilizes the output of the previous modules to output a probability of fraud for each tax return.We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority.
文摘The fraudulent behavior of taxpayers impacts negatively the resources available to finance public services. It creates distortions of competition and inequality, harming honest taxpayers. Such behavior requires the government intervention to bring order and establish a fiscal justice. This study emphasizes the determination of the interactions linking taxpayers with tax authorities. We try to see how fiscal audit can influence taxpayers’ fraudulent behavior. First of all, we present a theoretical study of a model pre established by other authors. We have released some conditions of this model and we have introduced a new parameter reflecting the efficiency of tax control;we found that the efficiency of a fiscal control have an important effect on these interactions. Basing on the fact that the detection of fraudulent taxpayers is the most difficult step in fiscal control, We established a new approach using DATA MINING process in order to improve fiscal control efficiency. We found results that reflect fairly the conduct of taxpayers that we have tested based on actual statistics. The results are reliable.
