978-1-4799-7620-1/15/$31.00 ©2015 IEEE Using Location Aware Business Rules for Preventing Retail Banking Frauds Ayhan Demiriz Sakarya University 54187, Sakarya, Turkey ademiriz@gmail.com Betül Ekizoğlu Sakarya University 54187, Sakarya, Turkey ekizoglubetul@gmail.com Abstract—Fraud detection procedures for national and international economies have become quite an important task. Ensuring the security of transactions carried out by banks and other financial institutions is one of the major factors affecting the reputation and profitability of such organizations. However, since people who perform fraudulent transactions change their methods constantly in order not to get caught up, it gets more difficult to identify and detect this type of transactions. Detecting this type of transactions makes the support of technology compulsory, considering high volume and intensity of transactions. In this paper, we explore practicality of using location data to aid finding better business rules where they can easily be deployed with a rule-based fraud detection and prevention system for retail banking. In order to study the importance of location data, we first compiled a set of anonymized automated teller machine (ATM) usage data from a mid-size bank in Turkey. Depending on how much mobile the card owners are, we can easily devise business rules to detect the anomalies. Such anomalies can be directed to appropriate business units to be analyzed further or account owners may be required additional authorizations for banking activities (such as internet money transfers and payments). We have shown in this paper that a significant bulk of ATM users does not leave the vicinity of their living place. We also give some brief use cases and hints regarding what types of business rules can be extracted from location data. Keywords—Location intelligence; fraud detection; sequence mining; spatio-temporal outlier I. INTRODUCTION Financial fraud detection and prevention have been receiving increasing attention in the past few years, due to the dramatic increase of losses because of fraud transactions every year. Fraud detection activities involves monitoring the behavior of transactions and prevention means a proactive approach that involves the analysis of transactions before they completed and identifying if they are fraud or not [1]. In highly connected societies that we now live in, financial fraud is very common to the point that financial institutions form various business units to guard their customers, capital and infrastructure as well as their reputation. Fraud cases involve criminal purposes and they are very hard to identify in most cases. As new application channels increase in use (e.g., mobile), new fraud opportunities present themselves, and anonymity becomes easier. The issue is that if financial institutions’ fraud detection tools remain static, they can be exploited by the fraudsters who quickly identify thresholds and take advantage [3]. Financial fraud is composed of bank fraud, securities and commodities fraud, insurance fraud and other related financial frauds [4]. Detecting financial fraud is very crucial for preventing often large scale and devastating consequences [4]. Kou et al. present a survey of fraud detection techniques for the types of credit card fraud, computer intrusion and telecommunication fraud [5]. Phua et al. firstly defines the fraudsters based on their motivation and identifies the affected commercial industries from fraudulent activities in their survey study [6]. They also formalize the main and sub types of fraud and present the nature of data evidence collected within affected industries. Based on the available data types they examine supervised, unsupervised, semi-supervised and hybrid techniques that are used for fraud detection. Supervised and unsupervised methods can be used for modeling fraudulent events [2]. Related predictive and explanatory variables can be included in statistical, machine learning and data mining models to understand, predict and distinguish fraud from other events. Classification, outlier detection, clustering, regression, and visualization are some of the well-known methods used for detecting and preventing fraud. Logistic regression, neural networks, Bayesian belief networks, decision trees, and naive Bayes come in front of the pack as the leading supervised methods used [4]. Clustering and link analysis are major unsupervised methods utilized for fraud detection and prevention. In their survey studies about anomaly detection Chandola et al. and Gupta et al. also reported that fraud detection problem can be defined as detecting anomalies in discrete sequences and the related data set can be classified as temporal or spatio- temporal data sequences [7], [8]. Anderka et al. reported that the total losses from ATM fraud during 2008 across Europe are estimated to 485.15 million EUR. They propose a method for ATM fraud detection in which they identify the ATM fraud as a sequence–based anomaly detection problem [9]. On the other hand, behavioral profiles are very commonly used in fraud detection systems [10], [11], [12], [13], [14]. In their survey for financial fraud detection with signature based methods Edge et al. provide a survey of existing research into account signatures, fraud detection architectures, processing models and applications. They summarized that the signature