Improving Vehicle Payment Method Classification Using XGBoost with SMOTE and SHAP Interpretation

Class imbalance in vehicle payment method classification can cause predictive models to become biased toward the majority. This study aims to build a classification model for automotive consumer payment methods using Extreme Gradient Boosting (XGBoost), with class balancing handled through the Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and model interpretability performed using SHAP (SHapley Additive Explanations). The dataset consisted of 11,011 records and 13 attributes derived from Toyota vehicle delivery order transactions. Results show that the XGBoost model without balancing achieved 67.37% accuracy but only 0.24 recall for the Cash class. After applying SMOTE, the recall for the Cash class improved to 0.58, while ADASYN produced a similar improvement at 0.59, with overall accuracy maintained at around 61–62% and a stable ROC-AUC of 0.65. Feature importance and SHAP analysis identified c_vehicle_model and c_city as the most influential factors in predicting the payment method. From a business perspective, the improved ability to detect cash customers reduces the risk of misclassification and enables dealers to better segment customer payment preference. This supports more effective marketing campaigns, sales strategies, and financing risk management. The combination of XGBoost, SMOTE, ADASYN, and SHAP has proven effective in handling imbalanced data while offering transparent interpretability of predictions, making it a practical foundation for data-driven decision-making in the automotive industry.