An Ensemble Machine Learning Model to Detect Tax Fraud: Conceptual Framework

Abstract

Most governments throughout the world, especially in developing and underdeveloped countries, depend more on tax revenue to fund public expenditure and investments. In the wake of Covid19, even governments that did not depend largely on tax revenue are forced to do that since their other sources of income were affected by the pandemic as borders were closed and nations were on lockdowns. Research, however, has shown that tax fraud is rampant especially in less developed countries. Traditional methods of detecting tax fraud are costly and they largely depend on the experts’ past experience. This renders them less effective where new mechanisms of tax fraud are involved. In this work I provide a conceptual framework on the use of ensemble machine learning models to detect tax fraud. I use decision trees, support vector machines and logistic regression as the base models. I hypothesize that ensemble methods outperform unsupervised machine learning models and the use of a single algorithm under supervised machine learning models. The outcomes of this research will serve to provide a framework that will help tax authorities to detect tax fraud thereby increasing the revenue collected.