Self-Reporting System For Incidents Detection In Automated Teller Machine (Atm) Using Machine Learning Techniques

Abstract

Automated Teller Machines (ATMs) have increased over the past decade due to their advantages in the banking sector. ATMs provide convenience to customers, optimizes banking operations, and minimizes transaction cost. However, undesirable security incidents such as tempering, skimming, physical attacks, robbery, and transaction reversal fraud may occur on ATM systems and negatively affect the user experience and banking institutions. ATM incidents occur either by system defect or through a deliberate act of physical attack by an intruder. In most security incidents, financial losses are imminent, and the customers' confidence in banking reduces. Developing a Self-Reporting System for ATM Incident Detection (SRSAID) is needed to avert the threats posed by security incidents on ATM systems. This research uses a machine-learning approach to solve this problem. Regional Convolutional Neural Network (R-CNN) and Support Vector Machine (SVM) algorithms are used to develop a detection model that detects occurrences of security incidents on an ATM system. Datasets used in the machine learning model development were obtained from NCR Ghana and the online repository. Experimental results showed that two CNN architecture models, ALEXNET and ssdlite_mobilenet_V2, obtained an accuracy score of 80% and 96%, respectively. SVM classifiers were developed using the linear, polynomial, and radial basis kernels, getting accuracy scores of 70.6%, 72.56%, and 81.21%, respectively. The initial results necessitated hyperparameter optimization to improve the performance of the classifiers. This resulted in improved accuracy scores of 76%, 77%, and 86% for linear, polynomial, and radial basis kernels, respectively, for the SVM models. The machine learning model was later deployed on a Raspberry Pi system which connected to a web application that provided a graphical user interface for user interactivity and viewing of reports.