A data mining based system for credit-card fraud detection in e-tail

Credit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance. A case study of credit-card fraud detection in an e-tail company is presented.The design and implementation of a fraud detection system is reported.A practical perspective on the complete development process is given.The combination of an automatic classifier with manual revision is explored.Different supervised learning methods are compared.

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