Predicting risky taxpayers in Kenya using machine learning
Date
2024
Authors
Cheboi, C. J.
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Taxation is a fundamental tool for governments to raise revenue and fulfill their responsibilities to society. It is an essential component of modern governance, facilitating economic development, social welfare, and the provision of goods and services for the benefit of the public. Conversely, tax evasion poses a pervasive challenge impacting both advanced and emerging economies globally. In Kenya, addressing tax evasion is a significant hurdle, with the government estimating substantial annual revenue losses as a result leaving the government to seek debt financing for its programs. This study used machine learning models to classify taxpayers according to certain attributes and predict those who are most likely to evade. The study explored 24 such attributes. The target output variable was the payment time. The dataset was trained using six supervised machine learning algorithms including the Decision Tree, Logistic Regression, Random Forest, XGBoost, Support Vector Machines and Stacking. Among the trained models, the Random Forest classifier exhibited the optimal performance with a precision score of 90% and recall score of 86%. This suggests that the model can effectively predict the risky taxpayers to be subjected to a tax audit with likelihood of high returns. The study identified the top five crucial features influencing optimal tax evasion prediction as installment tax paid, total liabilities, credit brought forward, withholding value added tax credit and total expenses. Accordingly, adjusting these parameters within specified ranges is anticipated to result in an increased accuracy of the prediction of taxpayer classes. These results offer valuable insights for understanding determinants of tax compliance and enhancing the accuracy of predicting risky taxpayers towards optimizing resource allocation for better tax revenue mobilization outcomes.
Keywords: Taxation, Tax evasion, Risky taxpayers, Tax Audit, Machine Learning
Description
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