Enhancing Precision of Automated Teller Machines Network Quality Assessment: Machine Learning and Multi Classifier Fusion Approaches

TL;DR

This paper presents a machine learning-based data fusion framework using multi-classifier stacking and SMOTE to significantly improve ATM network reliability, reducing false alarms and achieving high accuracy for better operational efficiency.

Contribution

It introduces a novel multi-classifier fusion approach with SMOTE for ATM network assessment, achieving superior accuracy and false alarm reduction compared to existing methods.

Findings

False alarm rate reduced from 3.56% to 0.71%.

Achieved overall accuracy of 99.29%.

Demonstrated scalable, practical solutions for ATM network quality assessment.

Abstract

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.