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

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Carneiroa, N (Author) Other; figueira, g (Author) FEUP; Costa, M (Author) Other

Journal

Title: Decision Support Systems

Vol. 95

Pages: 91-101

ISSN: 0167-9236

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 44 Citations

Scopus - 100 Citations

Other information

Authenticus ID: P-00M-GZ6

DOI: 10.1016/j.dss.2017.01.002

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11