Invariant Diversity As A Proactive Fraud Detection Mechanism For Online Merchants

Abstract

Online merchants face difficulties in using existing card fraud detection algorithms, so in this paper we propose a novel proactive fraud detection model using what we call invariant diversity to reveal patterns among attributes of the devices (computers or smartphones) that are used in conducting the transactions. The model generates a regression function from a diversity index of various attribute combinations, and use it to detect anomalies inherent in certain fraudulent transactions. This approach allows for proactive fraud detection using a relatively small number of unsupervised transactions and is resistant to fraudsters' device obfuscation attempt. We tested our system successfully on real online merchant transactions and it managed to find several instances of previously undetected fraudulent transactions.

Publication Date

7-1-2017

Publication Title

2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings

Volume

2018-January

Number of Pages

1-6

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/GLOCOM.2017.8254499

Socpus ID

85046361491 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046361491

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