Supervised Machine Learning for Tax Evasion Detection: A Case Study with the Brazilian Tax Administration
Cleyton Andre Pires · 2024
In this study, we present an innovative approach to enhance the audit case selection process within the Brazilian Tax Authority (RFB) by integrating Artificial Intelligence techniques. We employ supervised learning algorithms to predict taxpayers’ annual income coupled with outlier detection techniques to strategically prioritize cases of heightened fiscal interest. This involves leveraging a comprehensive dataset of socioeconomic variables available to the Tax Administration. A pivotal facet of our methodology is its commitment to model explainability for ensuring fairness and compliance with legal and ethical considerations. Preliminary findings demonstrate promising results, positioning our model as a valuable complement to the existing rule-based system.