A Bayesian deep learning method for credit card fraud detection with uncertainty quantification

Qiming Yu, Qizhi Zhang, Xihan Cao, Tianlin Zhang, Jiawei He, Ruimin Wang, Zhengyi Ma
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Abstract

With the changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.
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基于贝叶斯深度学习的不确定量化信用卡欺诈检测方法
随着人们消费观念的改变和移动支付的普及,信用卡在生活中似乎越来越不可或缺。然而,随着信用卡发行数量和信用额度的不断增加,涉及信用卡的诈骗案件也越来越多。由于互联网行业的快速发展,资金流动的渠道变得前所未有的畅通,使得信用卡诈骗案件的防范变得非常困难。如果这种情况持续下去,银行和其他金融机构在信用卡领域的发展将受到限制,这可能会影响人们的日常消费,甚至影响社会的正常运行。本文采用贝叶斯深度学习方法对信用卡欺诈预测的不确定性进行量化。通过实验分析,该模型的准确率在99%以上。与传统的分类模型相比,该模型具有更好的性能。
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