Using Location Aware Business Rules for Preventing Retail Banking Frauds

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.