Density-based clustering and radial basis function modeling to generate credit card fraud scores

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.