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 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.
Fraud detection Neural network model Spending pattern Confidence ROC
TAO GUO GUI-YANG LI
College of Computer Science and Technology in Sichuan Normal University, Chengdu, 610068, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3630-3634
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）