Neural data mining for credit card fraud detection

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.

Metadaten
Author:	Rüdiger W. Brause GND, Timm Sebastian Langsdorf, Hans-Michael Hepp
URN:	urn:nbn:de:hebis:30-79161
Document Type:	Article
Language:	English
Date of Publication (online):	2010/09/08
Year of first Publication:	1999
Publishing Institution:	Universitätsbibliothek Johann Christian Senckenberg
Release Date:	2010/09/08
Note:	Postprint, zuerst in: IEEE Int. Conf. on Tools with Art. Intell. ICTAI-99, IEEE Press, 1999, S. 103-106
Source:	IEEE Int. Conf. on Tools with Art. Intell. ICTAI-99, IEEE Press, 1999, pp. 103-106
HeBIS-PPN:	227736397
Institutes:	Informatik und Mathematik / Informatik
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:	Universitätspublikationen
Licence (German):	Deutsches Urheberrecht

Open Access