Fraud detection within bankcard enrollment on mobile device based payment using machine learning
Author:
Funder
China UnionPay
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
http://link.springer.com/content/pdf/10.1631/FITEE.1800580.pdf
Reference23 articles.
1. Bhattacharyya S, Jha S, Tharakunnel K, et al., 2011. Data mining for credit card fraud: a comparative study. Dec Support Syst, 50(3):602–613. https://doi.org/10.1016/j.dss.2010.08.008
2. Bolton RJ, Hand DJ, 2002. Statistical fraud detection: a review. Stat Sci, 17(3):235–255. https://doi.org/10.1214/ss/1042727940
3. Chen TQ, Guestrin C, 2016. XGBoost: a scalable tree boosting system. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.785–794. https://doi.org/10.1145/2939672.2939785
4. Cheng J, Wang PS, Li G, et al., 2018. Recent advances in efficient computation of deep convolutional neural networks. Front Inform Technol Electron Eng, 19(1):64–77. https://doi.org/10.1631/FITEE.1700789
5. China UnionPay, 2017. China Bank Card Annual Fraud Report 2017.
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