An Explainable ML Framework for Five Customs Fraud Type Predictions Using XGBoost and SMOTE-Based Class Balancing

Customs fraud causes significant revenue losses and undermines fair international trade. Common fraud types include undervaluation, misclassification, false declaration of origin, and smuggling, yet most existing machine learning approaches only predict whether a declaration is fraudulent, without indicating the fraud type or explaining the prediction. This paper presents a multiclass and explainable machine learning framework for customs fraud detection. A dataset of 14,873 customs import declarations was extended from binary fraud labels to five classes: no fraud, false origin, undervaluation, misclassification, and smuggling. The framework applies missing-value handling, domainbased feature engineering, SMOTE-based class-imbalance treatment, and an ensemble feature-selection method combining ChiSquare, ANOVA, Recursive Feature Elimination, and SHAP. Several models were benchmarked under different preprocessing configurations and tuned via hyperparameter search. XGBoost achieved the best performance, with 96.0% accuracy, macroF1 of 0.840, and macro-AUCROC of 0.947. SHAP, LIME, and counterfactual explanations were integrated to make the predictions interpretable.