Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4972445 | Decision Support Systems | 2017 | 36 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Nuno Carneiro, Gonçalo Figueira, Miguel Costa,