ATM Shield: Analysis of Multitier Security Issues of ATM in the Context of Bangladesh

Over the last decade, consumers have been largely dependent on and trust the Automatic Teller Machine (ATM) to conveniently meet their banking needs. However, despite the numerous advantages of ATM system, ATM fraud has recently become more widespread. In this paper, we provide an overview of the possible fraudulent activities that may be perpetrated against ATMs and investigates recommended approaches to prevent these types of frauds. In particular, we develop a prototype model for the utilization of three tier security equipped ATM to provide security solutions against must of the well-known breaches. In this research article, the tools and techniques of ATM fraud are contemplated. A secure three layer electronic transaction mechanism of ATM is developed to prevent ATM frauds. In this three layer authentication systems the users can improve ATM security against frauds and crimes

Scoring neighborhoods for locating atm using machine learning

Facility location is a general problem that is important for many different sectors and it is even more important when building the facility costs too much. In this project we analyzed the neighborhoods of Turkey and built two different models to estimate the good and bad neighborhoods for locating an ATM, which has significant costs for banks to build one. We used demographic and socio-economic data of 4,504 neighborhoods in Turkey and built models using Linear Regression and Decision Tree techniques of Machine Learning to find the best neighborhoods for locating a new ATM for a new bank entering the market. We compared the results of two machine learning methods and the results showed that we can make successful predictions of the neighborhoods by using machine learning methods which are good to locate an ATM without classical optimization techniques that requires complex calculations and machine learning methods.Tesis yer seçimi, birçok farklı sektörde var olan genel ve önemli bir sorundur. Eğer kurulmak istenen tesis maliyeti yüksek ve kurması zor / karmaşık bir tesis ise sorun daha da önem kazanmaktadır. Bu projede, Türkiye'nin mahallelerini analiz ettik ve bankalar için oldukça yüksek maliyeti olan “Nereye ATM konulmalı” sorusuna cevap olarak ATM yerleştirmek için iyi ve kötü mahalleleri tahminleyen iki farklı model geliştirdik. Türkiye'deki 4.504 mahallenin demografik, sosyoekonomik ve diğer bazı verilerini kullanarak, sektöre yeni giren bir bankanın hangi mahallelere ATM açması gerektiğini tahminleyen ve Makine Öğreniminin Doğrusal Regresyon ve Karar Ağacı tekniklerini kullanan modeller oluşturduk . İki makine öğrenim yönteminin sonuçlarını karşılaştırdık ve gördük ki geleneksel ve karmaşık olan optimizasyon yöntemi yerine makina öğrenim yöntemlerini kullanarak ATM kurmak için iyi olan mahalleler başarılı bir şekilde tahmin edilebilmektedir