Evaluation of Undersampling and Oversampling Techniques in Term Deposit Prediction: A Gradient Boosting Approach

Abstract

Time deposits play a pivotal role in maintaining banking liquidity, yet telemarketing campaigns designed to secure them are often inefficient due to low response rates and untargeted outreach. The primary challenge in predictive marketing modeling lies in extreme data class imbalance, which renders standard algorithms prone to bias and leads to a failure in detecting potential customers. This study aims to validate the effectiveness of Gradient Boosting models and empirically evaluate the impact of various resampling techniques in mitigating class distribution disparities. The applied methodology encompasses the utilization of XGBoost, LightGBM, and CatBoost algorithms on the UCI Bank Marketing dataset, integrated with Random Under-Sampling, Random Over-Sampling, SMOTENC, and Tomek Links strategies. Experimental results reveal a significant trade-off between sensitivity and precision, wherein LightGBM paired with Random Under-Sampling achieved the highest detection capability with a Recall of 88.28%. Concurrently, the combination of CatBoost with Random Over-Sampling demonstrated the optimal balance, attaining an F1-Score of 0.6040, a Recall of 81.95%, and an AUC-ROC value reaching 0.9326. These findings offer a strategic contribution to bank management in selecting analytic approaches aligned with business priorities, whether the focus is on operational cost efficiency or aggressive market penetration to optimize customer acquisition.