Digital economy tax compliance model in Malaysia using machine learning approach

The field of digital economy income tax compliance is still in its infancy. The limited collection of government income taxes has forced the Inland Revenue Board of Malaysia (IRBM) to develop a solution to improve the tax compliance of the digital economy sector so that its taxpayers may report voluntary income or take firm action. The ability to diagnose the taxpayer’s compliance will ensure the IRBM effectively collects the income tax and gives revenues to the country. However, it gives challenges in extracting necessary knowledge from a large amount of data, leading to the need for a predictive model to detect the taxpayers’ compliance level. This paper proposes the descriptive and predictive analytics models for predicting the digital economic income tax compliance in Malaysia. We conduct descriptive analytics to explore and extract a summary of data for initial understanding. Through a brief description of the descriptive model, the data distribution in a histogram shows that the information extracted can give a clear picture in influencing the results to classify digital economic tax compliance. In predictive modeling, single and ensemble approaches are employed to find the best model and important factors contributing to the incompliance of tax payment among the digital economic retailers. Based on the validation of training data with the presence of seven single classifier algorithms, three performance improvements have been established through ensemble classification, namely wrapper, boosting, and voting methods, and two techniques involving grid search and evolution parameters. The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. The knowledge analysis phase learns meaningful features and hidden knowledge that could classify the contexts of taxpayers that could potentially influence the degree of tax compliance in the digital economy are categorized. Overall, this collection of information has the potential to help stakeholders make future decisions on the tax compliance of the digital economy