Combining unsupervised and supervised learning in credit card fraud detection

By Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Y. Kessaci, F. Oblé, Gianluca Bontempi. Information Sciences, 2019

452 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/70f612edc09538f147ec58710db41e06de65427a

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In Artificial Intelligence

1. A. Taha
2. Sharaf Jameel Malebary
3. Yanxia Sun
4. Ibomoiye Domor Mienye

Citations in the corpus

• An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, methodology): “[33] implemented a hybrid approach that utilizes unsupervised outlier scores to expand the set of features of the fraud detection classifier.”