Building Machine Learning Models for Fraud Detection in Customs Declarations in Senegal

To improve the customs declaration control system in Senegal, we propose fraud risk prediction models built with machine learning methods such as Neural Networks (MLP), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). These models were built from historical customs declaration data and then tested on a part of the data reserved for this purpose to evaluate their prediction performance according to the metrics of accuracy, precision, recall, and F1-Score. The RF model proved to be the more performant model and is followed, in order, by the XGBoost model, and the MLP and SVM models.