Machine learning approaches for customs fraud detection

Abstract

Customs duties are based on the origin and value of the goods and their classification (the customs tariff to be applied). Falsifying any of these factors when importing or exporting products is fraud. This includes falsely declaring the origin of the goods, declaring a lower value on the goods, misclassifying the goods and smuggling goods. In this paper we apply machine learning algorithms (Artificial Neural Network, Fuzzy Min-Max Classifier and Logistic Regression) for fraud detection in customs declarations. Performance of the models are evaluated using accuracy, sensitivity and specificity. The best results were achieved using Logistic Regression. In further research it would be useful to analyze applicability of ensemble learning methods and others fraud detection models.