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 |
Files
Original bundle
1 - 1 of 1