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

作者:

Highlights:

• 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.

摘要

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.

论文关键词:00-01,99-00,Fraud detection,Credit-card,Online retail,Supervised learning,Practical implementation

论文评审过程:Received 17 February 2016, Revised 28 September 2016, Accepted 3 January 2017, Available online 7 January 2017, Version of Record 3 March 2017.

论文官网地址:https://doi.org/10.1016/j.dss.2017.01.002