Anomalous Behavior Detection in ATM using Different Methodologies: A Review

The necessity for standardized anomaly detection systems in financial transactions is driven by the alarming increase in ATM fraud and criminal activities. Many techniques for detecting anomalous behavior in ATM systems are also summarized and evaluated in this review article. To better secure financial institutions and customers from fraudsters, it is crucial to examine the reasoning behind these systems, particularly in the areas that pertain to improving security. The overarching purpose of this paper is to present a clear and succinct explanation of the cutting-edge machine learning (ML) and deep learning (DL) algorithms that are presently the backbone of ATM anomaly pattern detection, including more contemporary methods like transfer learning. This review delves into the theoretical foundations of these strategies and compares different algorithms, along with the values of performance indicators that show their strengths and drawbacks. Our goal is to show, via an examination of current methods in actual settings, any flaws or problems that may hinder the development of ATM security in the future. Finally, this work can be used by researchers and those interested in bettering ATM systems and associated secure technologies in the future to study the subject and explore anomaly detection technologies as a means of both practical experience and future advancement.