Neural data mining for credit card fraud detection

Due to a rapid advancement in the electronic commerce technology, use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment, credit card frauds are becoming increasingly rampant in recent years. In this paper, we model the sequence of operations in credi...

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Bibliographic Details
Published in2008 International Conference on Machine Learning and Cybernetics Vol. 7; pp. 3630 - 3634
Main Authors Tao Guo, Gui-Yang Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Artificial neural networks
Confidence
Credit cards
Data mining
Fraud detection
Machine learning
Merchandise
Neural network model
ROC
Spending pattern
Testing
Training
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ISBN1424420954
9781424420957
ISSN2160-133X
DOI10.1109/ICMLC.2008.4621035

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Summary:Due to a rapid advancement in the electronic commerce technology, use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment, credit card frauds are becoming increasingly rampant in recent years. In this paper, we model the sequence of operations in credit card transaction processing using a confidence-based neural network. Receiver operating characteristic (ROC) analysis technology is also introduced to ensure the accuracy and effectiveness of fraud detection. A neural network is initially trained with synthetic data. If an incoming credit card transaction is not accepted by the trained neural network model (NNM) with sufficiently low confidence, it is considered to be fraudulent. This paper shows how confidence value, neural network algorithm and ROC can be combined successfully to perform credit card fraud detection.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4621035