The nature of statistical learning theory
The nature of statistical learning theory
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Back propagation networks for credit card fraud prediction using stratified personalized data
ISP'06 Proceedings of the 5th WSEAS International Conference on Information Security and Privacy
A new binary classifier: clustering-launched classification
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Two-stage replenishment policies for deteriorating items at Taiwanese convenience stores
Computers and Operations Research
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One of the most potential methods to prevent credit card fraud is the questionnaire-responded transaction (QRT) approach. Unlike traditional approaches founded on past real transaction data, the QRT approach proposes to develop a personalized model to avoid credit card frauds from the initial use of new cards. Though this approach is promising, there are still some issues needed investigating. One of the most important issues concerning the QRT approach is how to predict accurately with only few data. The purpose of this paper is to investigate the prediction accuracy of this approach by using support vector machines (SVMs). Over-sampling, majority voting, and hierarchical SVMs are employed to investigate their influences on the prediction accuracy. Our results show that the QRT approach is effective in obtaining high prediction accuracy. They also show that combined strategies, such as weighting and voting, majority voting, and hierarchical SVMs can increase detection rate considerably.