Research Area:  Machine Learning
Supervised learning techniques are widely employed in credit card fraud detection, as they make use of the assumption that fraudulent patterns can be learned from an analysis of past transactions. The task becomes challenging, however, when it has to take account of changes in customer behavior and fraudsters ability to invent novel fraud patterns. In this context, unsupervised learning techniques can help the fraud detection systems to find anomalies. In this paper we present a hybrid technique that combines supervised and unsupervised techniques to improve the fraud detection accuracy. Unsupervised outlier scores, computed at different levels of granularity, are compared and tested on a real, annotated, credit card fraud detection dataset. Experimental results show that the combination is efficient and does indeed improve the accuracy of the detection.
Author(s) Name:  FabrizioCarcillo,Yann-AëlLe Borgne,OlivierCaelen,YacineKessaci,FrédéricOblé,GianlucaBontempi
Journal name:  Information Sciences
Publisher name:  Elsevier
Volume Information:  Volume 557, May 2021, Pages 317-331
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0020025519304451