Telecmmunications Subscription Fraud Detection Using Artificial Nueral Networks
DOI:
https://doi.org/10.14738/tmlai.36.1695Keywords:
Telecommunications, Subscription, Artificial Neural Networks, Neurosolutions, Fraud, detection, Subscription Fraud.Abstract
Telecommunications Companies are facing a lot of problems due to fraud; hence the need for an effective fraud detection system for the telecommunications companies. This paper presents a design and implements of a subscription fraud detection system using Artificial Neural Networks. Neurosolutions for Excel was used to implement the Artificial Neural Network. The system was tested and found to be user friendly, effective and 85.7% success rate achieved.
References
(1) Laudon K. C., Laudon, J. P. Brabston, M. E, Management Information System: Managing the Digital Firm: Pearson Education Canada Inc; Toronto, Ontario, 2002.
(2) Alexopoulos, P. and Kafentzis, K., Towards a Generic Fraud Ontology in E Government, ICE-B, 2007. p. 269-276.
(3) Hollmen, J., User Profiling and Classification for Fraud Detection in Mobile Communication Networks, PhD thesis, Helsinki University of Technology, Department of Cognitive and Computer Science and Engineering. Espoo, Finland. 2000.
(4) Hiyam, A. E. Tawashi, Detecting Fraud in Cellular Telephone Networks Jawwal Case Study. MBA thesis Islamic University, Faculty of Commerce, Department of Business Administration, Gaza. 2010.
(5) Bolton, R. J. and Hand, D. J., Statistical Fraud Detection, A review, Institute of Mathematical Statistics, 2002. 17(3), p. 235–255.
(6) Pieprzyk, J., Ghodosi, H. and Dawson, E., Information Security and Privacy, 12th Australasian Conference, ACISP 2007, Townsville, Australia, July 2-4, 2007: Proceedings, Springer, Germany, p. 446-447.
(7) Prasad, S. K., Routray, S. and Khurana, R., Information Systems, Technology and Management. Third International Conference, ICISTM 2009, Ghaziabad, India, March 12-13, 2009, Proceedings, Springer, Germany, p. 259-260
(8) Żytkow, J. M. and Rauch, J., Principles of Data Mining and Knowledge Discovery: Third European Conference, PKDD'99, Prague, Czech Republic, September 15-18, 1999: Proceedings, Springer, USA, p. 251.
(9) Kaplan, D. A., Intrigue in High Places, To Catch a Leaker, Hewlett– Packard’s Chairwoman Spied on the Home–Phone Records of Its Board of Directors, Newsweek (September), 2006.
(10) Liatsis, P., Recent Trends in Multimedia Information Processing, Proceedings of the 9th International Workshop on Systems, Signals and Image Processing, World Scientific Publishing, London, 2002. P. 474-475.
(11) Samarati, P., Information Security Theory and Practices: Security and Privacy of Pervasive Systems and Smart Devices: 4th IFIP WG 11.2 International Workshop, WISTP 2010, Passau, Germany, April 12-14,
, Proceedings, Springer, USA, p. 201.
(12) Perner, P., Advances in Data Mining, Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006: Proceedings, Springer, Germany, p. 535.
(13) Neurosolutions for Excel. http://www.neurosolutions.com/documentation/NeuroSolutionsforExcel.pdf Retrieved, September 28, 2015.
