Intelligent Technologies for Managing Fraud and Identity Theft

Superimposition frauds with respect to credit card and mobile (cell) phone usages, as well as identity thefts have become some of the fastest growing crimes worldwide. As fraudsters increasingly leverage technology systems, products and channels to commit crime, it becomes critical that businesses employ intelligent automated systems to proactively reduce their exposure to fraud, minimize risk losses and defend their organizations' reputation. Thus businesses are increasingly getting involved in studying emerging security technologies as well as developing new security applications with these technologies to stay ahead of the competition in retaining and growing their customer base. This research is concerned about examining the aforementioned frauds and providing a summary review of the latest artificial intelligence technologies that have been used to develop security applications to consistently discover, monitor, examine and manage the aforementioned fraudulent activities across organizations - thus helping managers to identify and employ effective controls and prevention measures, as well as helping researchers, who are seeking more robust ways to protect the users from consumer fraud, with the identification of research projects to undertake to further advance the field

[1]  J. Stuart Aitken,et al.  Multiple algorithms for fraud detection , 2000, Knowl. Based Syst..

[2]  C. J. Gahan URU — On-line Identity Verification , 2004 .

[3]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[4]  Ramasamy Uthurusamy,et al.  Data mining and knowledge discovery in databases , 1996, CACM.

[5]  José R. Dorronsoro,et al.  Neural fraud detection in credit card operations , 1997, IEEE Trans. Neural Networks.

[6]  Dean Abbott,et al.  An evaluation of high-end data mining tools for fraud detection , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[7]  John Shawe-Taylor,et al.  An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection , 2001, J. Parallel Distributed Comput..

[8]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[9]  Min-Jung Kim,et al.  A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection , 2002, IDEAL.

[10]  Siu Cheung Hui,et al.  A Multi-View Facial Analysis Technique for Identity Authentication , 2003, IEEE Pervasive Comput..

[11]  RadhaKanta Mahapatra,et al.  Business data mining - a machine learning perspective , 2001, Inf. Manag..