Applying XGBoost-ADASYN in the Classification Process of Bank Customers Who Will Take Time Deposits - Abdilah, Muhammad Fariz Fata; Mazdadi, Muhammad Itqan; Farmadi, Andi; Muliadi, Muliadi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay (Bright Publisher, 2024)
Investment in the form of time deposits at banks offers stable returns. Identifying and attracting potential customers, however, poses challenges. This research enhances the predictive capabilities of deposit classification models by addressing data imbalance with a combination of XGBoost, ADASYN, and Random Search optimization techniques. The integration of ADASYN improves minority class representation, while Random Search efficiently optimizes model parameters. Our findings show a significant accuracy of 94.93%, benchmarked against baseline models, highlighting our method's effectiveness compared to traditional approaches. This hybrid model advances customer data analysis and achieves our research objectives. We discuss the integration challenges, including computational demands and technique selection. The research underscores the application of machine learning to address financial industry issues, emphasizing the impact of data preprocessing and feature engineering on performance. Future studies might explore AutoML to reduce complexity further and enhance model scalability, promising more innovation in customer data analysis.