KEYWORDS: Deep learning, Data modeling, Education and training, Random forests, Performance modeling, Machine learning, Industry, Matrices, Mathematical modeling, Detection and tracking algorithms
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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