ATM TRANSACTION FAILURE DETECTION USING MACHINE LEARNING

Abstract

Automated Teller Machine (ATM) services represent a critical component of modern banking strategies aimed at enhancing service quality. However, the performance of these services often falls short of expectations, leading to customer dissatisfaction and revenue losses for financial institutions. This research focuses on creating a predictive model to identify failures in ATM transactions through the application of machine learning techniques. Transaction failures can arise from various factors, including technical issues with the ATM itself and customer-related problems, resulting in frustration for users and the necessity for frequent reconciliation by banks. Prior studies have not sufficiently tackled the issue of detecting transaction failures using machine learning methods. Therefore, this research utilizes machine learning algorithms to recognize and forecast transaction failures. Data for this research was collected from Zemen Bank's ATM reconciliation records and CR2 database, encompassing a total of 20,516 transaction datasets for a selected month in 2024. The research employs supervised machine learning techniques, applying a range of classification algorithms such as Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors, Gaussian Naïve Bayes, and Support Vector Classifier (SVC). The experiments were carried out using Python 3.7 within the Jupyter Notebook environment (Anaconda distribution). The Random Forest algorithm was employed to create the predictive model for identifying ATM transaction failures, which achieved an accuracy of 96.78%, while the Naïve Bayes classifier demonstrated the least performance with an accuracy of 86.94%. The findings indicate that machine learning techniques can effectively predict ATM transaction statuses, thereby enhancing the management of ATM operations. Key factors influencing transaction failures include the available balance, the amount of cash requested, the ATM's location, the time of the transaction, the type of transaction, and whether the transaction occurred on a working day. This research highlights the promise of machine learning techniques in enhancing the reliability and efficiency of ATM services, providing important insights for financial institutions aiming to streamline their transaction operations.