Enhancing Tax Administration in Niger : A Data Mining Approach to Outlier Detection
Keywords:
CRISP-DM, Data mining, Machine Learning (ML), Outlier detection, Tax administrationAbstract
Developing countries face significant challenges in accurately forecasting tax revenues due to disparate databases and the presence of outliers in collected taxes. These anomalies can lead to inconsistencies in revenue predictions, impacting economic planning and policy decisions. This study applies the CRoss Industry Standard Process for Data Mining (CRISP-DM) framework to support Niger’s tax administration in detecting and addressing outliers. Boxplot analysis and extreme value detection algorithms were utilized to visualize outliers, while the Interquartile Range (IQR) Machine Learning (ML) algorithm was employed to remove them. The dataset covers the period from January 2019 to December 2022. The current analysis identified significant outliers in June 2020 and December 2021 for Value Added Tax (VAT) and in August 2021 for Processing Tax and Salary (ITS). The study found that with outliers, VAT, ITS, and Profit Tax (ISB) accounted for 61.2% of total tax revenues, whereas without outliers, their combined contribution increased to 64.8%, highlighting the importance of accurate anomaly detection for better fiscal planning.
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