False Positives in Credit Card Fraud Detection: Measurement and Mitigation

dc.contributor.author Wallny, Florian
dc.date.accessioned 2021-12-24T17:31:02Z
dc.date.available 2021-12-24T17:31:02Z
dc.date.issued 2022-01-04
dc.description.abstract Credit Card Fraud Detection is a classification problem where different types of classification errors cause different costs. Previous works quantified the financial impact of data-driven fraud detection classifiers using a cost-matrix based evaluation approach, however, none of them considered the significant financial impact of false declines. Analysts reported that fraud prediction in e-commerce still has to deal with false positive rates of 30-70%, and many cardholders reduce card usage after being wrongly declined. In our paper, we propose a new method for assessing the cost of false declines and evaluate several state-of-the-art fraud detection classifiers using this method. Further, we investigate the effectiveness of ensemble learning as previous work supposed that a combination of diverse, individual classifiers can improve performance. Our results show that cost-based evaluation yields valuable insights for practitioners and that our ensemble learning strategy indeed cuts fraud cost by almost 30%.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.195
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79527
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Fraud Detection Using Machine Learning
dc.subject cost-sensitive learning
dc.subject credit card fraud detection
dc.subject ensemble learning
dc.subject false declines
dc.subject false positive
dc.title False Positives in Credit Card Fraud Detection: Measurement and Mitigation
dc.type.dcmi text
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