Title :
Density-based clustering and radial basis function modeling to generate credit card fraud scores
Author :
Hanagandi, Vijay ; Dhar, Amitava ; Buescher, Kevin
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Abstract :
Historical information on credit card transactions can be used to generate a fraud score which can then be used to reduce credit card fraud. The report describes a fraud-nonfraud classification methodology using a radial basis function network (RBFN) with a density based clustering approach. The input data is transformed into the cardinal component space and clustering as well as RBFN modeling is done using a few cardinal components. The methodology has been tested on a fraud detection problem and the preliminary results obtained are satisfactory
Keywords :
credit transactions; feedforward neural nets; financial data processing; fraud; multilayer perceptrons; pattern classification; cardinal component space; credit card fraud score generation; credit card transactions; density-based clustering; fraud detection problem; fraud-nonfraud classification methodology; historical information; radial basis function modeling; radial basis function network; Adaptation model; Clustering algorithms; Credit cards; Laboratories; Multidimensional systems; Neural networks; Production facilities; Radial basis function networks; Testing; Training data;
Conference_Titel :
Computational Intelligence for Financial Engineering, 1996., Proceedings of the IEEE/IAFE 1996 Conference on
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-3236-9
DOI :
10.1109/CIFER.1996.501848
