. One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments. Keywords: fraud detection, rule l...

Abstract. One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.

: This paper discusses the status of research on detection of fraud undertaken as part of the European Commission-funded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project. A first task has been the identification of possible fraud scenarios and of typical fraud indicators which can be mapped to data in Toll Tickets. Currently, the project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule-based approach and two approaches based on neural networks, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations. 1. INTRODUCTION It is estimated that the mobile communications industry loses several million ECUs per year due to fraud. Therefore, prevention and early detection of fraudulent activity is an important goal for network operators. It is clear that the additional securit...

OF THE LICENTIATE'S THESIS Author and name of the thesis: Jaakko Hollmn Probabilistic Approaches to Fraud Detection Date: 15.12.1999 Number of pages: 37 Department: Department of Computer Science and Engineering Professorship: Tik-115 Information Sciences Supervisor: Professor Olli Simula Instructor: Professor Olli Simula In telecommunication, a network operator may loose several percent of its revenue due to fraud. Fraud may be defined as dishonest or illegal use of services, with the intention to avoid or to reduce service charges. Fraud detection attempts to discover fraudulent activity in a telecommunication network. In this thesis, the problem of fraud detection is treated as a pattern recognition problem. Detection is based on the call data of mobile phone subscribers, which are used for describing calling behavior. The goal is to develop learning methods that detect fraud accurately based on call data. Fraud detection can be based on two hypotheses. On the one h...