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A study about fraud detection and the implementation of SUSPECT - Supervised and UnSuPervised Erlang Classifier Tool
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2014 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Fraud detection is a game of cat and mouse between companies and people trying to commit fraud. Most of the work within the area is not published due to several reasons. One of the reasons is that if a company publishes how their system works, the public will know how to evade detection. This paper describes the implementation of a proof-of-concept fraud detection system. The prototype  named SUSPECT uses two different methods for fraud detection. The first one being a supervised classifier in form of an artificial neural network and the second one being an unsupervised classifier in the form of clustering with outlier detection. The two systems are compared with each other as well as with other systems within the field. The paper ends with conclusions and suggestions on how to to make SUSPECT perform better.

Place, publisher, year, edition, pages
UPTEC IT, ISSN 1401-5749 ; 14 005
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-222774OAI: diva2:712190
Educational program
Master of Science Programme in Information Technology Engineering
Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2014-04-14Bibliographically approved

Open Access in DiVA

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