Enhancing Precision of Automated Teller Machines Network Quality Assessment: Machine Learning and Multi Classifier Fusion Approaches
This provides a practical solution for financial institutions to enhance ATM reliability and customer satisfaction, though it is incremental as it applies existing machine learning techniques to a specific domain.
This study tackled the problem of improving ATM network quality assessment by introducing a multi-classifier fusion approach with Stacking Classifier and SMOTE for class imbalance, achieving a reduction in false alarms from 3.56% to 0.71% and an overall accuracy of 99.29%.
Ensuring reliable ATM services is essential for modern banking, directly impacting customer satisfaction and the operational efficiency of financial institutions. This study introduces a data fusion approach that utilizes multi-classifier fusion techniques, with a special focus on the Stacking Classifier, to enhance the reliability of ATM networks. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, enabling balanced learning for both frequent and rare events. The proposed framework integrates diverse classification models - Random Forest, LightGBM, and CatBoost - within a Stacking Classifier, achieving a dramatic reduction in false alarms from 3.56 percent to just 0.71 percent, along with an outstanding overall accuracy of 99.29 percent. This multi-classifier fusion method synthesizes the strengths of individual models, leading to significant cost savings and improved operational decision-making. By demonstrating the power of machine learning and data fusion in optimizing ATM status detection, this research provides practical and scalable solutions for financial institutions aiming to enhance their ATM network performance and customer satisfaction.