A New Machine Learning Model for Detecting levels of Tax Evasion Based on Hybrid Neural Network - Shujaaddeen, Abeer; Ba-Alwi, Fadl Mutaher; Al-Gaphari, Ghaleb (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Tax fraud is a general term that refers to the efforts of organizations or individuals to legally defraud, such as concealing the true status of the taxpayer to the tax authorities so that the value of the tax is reduced and included. The submission of false tax reports, such as declaring earnings that are undervalued. In other words, tax fraud is the lying on a tax return form to reduce tax liability. Therefore, detecting tax fraud is one of the main priorities of the tax authorities. Most of the recent works and modern business in tax fraud detection in many countries around the world rely on machine learning techniques that make use of labelled data. unsupervised learning has main advantage, that enabled this techniques e to deal with problems like detecting Tax Froud, which can be considered as a challenge in terms of decision.Also supervised learning is capable of classify the data. Therefore, this research paper aims to detect tax fraud and determine its level by building an optimal machine learning model by a new hybrid neural network technique that depend on two type of learning unsupervised learning and supervised learning for detecting tax fraud and determine its level. The proposed model is validated based on available machine learning techniques, it outperforms previous techniques in term of effort, computational time and cost reduction. The datasets used for validation and verification of the proposed model is given from the Tax Authority of Yemen. It consists of 1083 attributes.