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

This paper concentrates on the effectiveness of using a hybrid intelligent system that combines multilayer perceptron (MLP) neural network, support vector machine (SVM), and logistic regression (LR) classification models with harmony search (HS) optimization algorithm to detect corporate tax evasion for the Iranian National Tax Administration (INTA). In this research, the role of optimization algorithm is to search and find the optimal classification model parameters and financial variables combination. Our proposed system finds optimal structure of the classification model based on the characteristics of the imported dataset. This system has been tested on the data from the food and textile sectors using an iterative structure of 10-fold cross-validation involving 2451 and 2053 test set samples from the tax returns of a two-year period and 1118 and 906 samples as out-of-sample using the tax returns of the consequent year. The results from out-of-sample data show that MLP neural network in combination with HS optimization algorithm outperforms other combinations with 90.07% and 82.45% accuracy, 85.48% and 84.85% sensitivity, and 90.34% and 82.26% specificity, respectively in the food and textile sectors. In addition, there is also a difference between the selected models and obtained accuracies based on the test data and out-of-sample data in both sectors and selected financial variables of every sector.