<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vladimir Zaslavsky</style></author><author><style face="normal" font="default" size="100%">Anna Strizhak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Credit Card Fraud Detection Using Self-Organizing Maps</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Fraud Detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Payment System</style></keyword><keyword><style  face="normal" font="default" size="100%">Self Organizing Map</style></keyword><keyword><style  face="normal" font="default" size="100%">Transaction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">48-63</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Nowadays, credit card fraud detection is of great importance to financial institutions. This article presents an automated credit card fraud detection system based on the neural network technology. The authors apply the Self-Organizing Map algorithm to create a model of typical cardholder’s behavior and to analyze the deviation of transactions, thus finding suspicious transactions.</style></abstract></record></records></xml>