Tax Evasion Detection by Using Combinatory Intelligent System

With the electronic taxpayers system being operationalized and the digital storage of tax data developed in Iran، it is now possible to design different models to analyze the available data. There are two main areas that have not been the focus of the fairly limited current studies in this field; one being the parallel optimization of parametric AI models and the other area is the selection of input variable combination. For this reason، in present study، we have used the harmony search (HS) optimization algorithm to do parallel optimization of multilayer perceptron (MLP) neural network parameters and also to find a suitable combination of input variables. In addition to that، the results have been compared with logistic regression results as the core of the system. In the present research، 21 initial input variables are selected for the system based on the survey done on similar studies in the last thirty years and it takes into account the specifications of the tax system in Iran and the opinions of the experts in the field are asked. After running the system on the data from the food and textile sectors and comparing the results from the neural network and logistic regression، we have concluded that neural network can produce more accurate results and the difference is statistically meaningful.