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International Journal of Data Science and Analytics
Published in: International Journal of Data Science and Analytics 4/2018

04-04-2018 | Applications

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

Authors: Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi

Published in: International Journal of Data Science and Analytics | Issue 4/2018

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Abstract

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data are peculiar because they are obtained in a streaming fashion, and they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated with the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.
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Footnotes
1
Though some papers on fraud detection present datasets with still lower rates (0.01% in [15], 0.005% in [2], 0.02% in [51] and 0.004% in [36]), our dataset is inline with other recent works on fraud detection ([22, 47] and [39] have a class imbalance rate of 0.8, 0.5 and 0.4%, respectively).
 
2
The use of two different learning strategies is justified by the need to assess the robustness of the AL strategies with respect to different learning methods and different detection tasks (transaction based and card based).
 
4
We made the Streaming Active Learning Strategies repository available in http://​github.​com/​fabriziocarcillo​/​.
 
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Metadata
Title
Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization
Authors
Fabrizio Carcillo
Yann-Aël Le Borgne
Olivier Caelen
Gianluca Bontempi
Publication date
04-04-2018
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics / Issue 4/2018
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-018-0116-z

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