Betul Ekizoglu Bulut

It is of major importance that buildings are able to accommodate safe evacuation. Every manager and owner of a building is concerned about the safety of persons living or working in the building. In addition, government codes and standards require that buildings be 'safe'. Disaster response in areas of high population density is centered on efficient evacuation of people and/or goods. Developing evacuation plans suitable for different levels of urgency based on the intensity of threat is a challenging task. This paper describes evacuation simulation in which Simulex models the movement of the pedestrians through a number of exits. The study had been done in Istanbul Technical University, Faculty of Management as the demand of faculty managers.

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.

This article presents a novel approach for detecting fraudulent behaviors from automated teller machine (ATM) usage data by analyzing geo-behavioral habits of the customers and describe the use of a fuzzy rule-based system capable of classifying suspicious and non-suspicious financial transactions. Firstly, the geographic entropies of ATM cardholders are computed from the spatio-temporal ATM transactions data to form customer classes of mobility. ATM transactions exhibit spatio-temporal properties by inclusion of location information. The transition data can be generated by using transaction data from the current location to the next one. Once, the transition data are generated, statistical outlier detection techniques can be utilized. On top of classical methods, crisp unsupervised methods can easily be used for detecting outliers in the transition data. In addition, fuzzy C-Means algorithm can be implemented to determine outliers. In this study, ATM usage dataset containing around two years' worth of data, provided by a mid-size Turkish bank was analyzed. It was shown that a significant bulk of ATM users does not leave the vicinity of their living places. Some insightful business rules that can be extracted from geo-tagged ATM transaction data by means of using a fuzzy rule-based system were also presented.