%0 Journal Article
%T A Study on Fraud Detection Based on Data Mining Using Decision Tree
%A A. N. Pathak
%A Manu Sehgal
%A Divya Christopher
%J International Journal of Computer Science Issues
%D 2011
%I IJCSI Press
%X Fraud is a million dollar business and it is increasing every year. The U.S. identity fraud incidence rate increased in 2008 returning to levels unseen since 2003. Almost 10 million Americans learned they were victims of identity (ID) fraud in 2008 which is up from 8.1 million victims in 2007. More consumers are becoming identity (ID) fraud victims reversing the previous trend in which identity (ID) fraud had been gradually decreasing. This reverse makes sense since overall criminal activity tends to increase where there is a recession. Fraud involves one or more persons who intentionally act secretly to deprive another of something of value, for their own benefit. Fraud is as old as humanity itself and can take an unlimited variety of different forms. However, in recent years, the development of new technologies has also provided further ways in which criminals may commit fraud (Bolton and Hand 2002). In addition to that, business reengineering, reorganization or downsizing may weaken or eliminate control, while new information systems may present additional opportunities to commit fraud.
%K Data mining
%K decision tree
%K gini impurity
%K information gain
%K leaf
%K binary decision diagram
%K IJCSI
%U http://www.ijcsi.org/papers/IJCSI-8-3-2-258-261.pdf