This research investigates the possibility of identifying patterns for corporate tax payment behaviour behind the Government database on companies. The aim is to reveal companies showing abnormal behaviour or outliers (companies whose behaviour on tax payment is away from the average). Knowing whether or not the outliers are evading taxes will be the result of in site inspections which are under development. We are particularly interested in analysing whether there is correlation between the dependent the tax paid in earlier years and the independent variables, such as total revenue and number of employees. A preliminary data analysis shows that there is strong correlation between those variables, and therefore the data might be appropriate for the task. However, patterns are only unveiled after using a two-step methodology: Clustering and Backpropagation Neural Networks (BNN). Clusters of similar companies are investigated separately using BNN. Analysis of results from the Artificial Neural Networks (ANN) was based on the comparison between the forecast and the true amount of tax paid by companies each year.
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