Research on the Risk Warning of Mobile Payment Based on BP Neural Network
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Jinling Yan, and Peiluan Li
This paper focuses on the risk warning of mobile payment. Based on BP neural network theory, we try to provide a new risk warning method. Firstly, we select the transaction scale of mobile payment as entry point to construct a risk early warning model by using BP neural network. Then, based on the data of development status of China Mobile Payment from 2009 to 2017, we train the model via MATLAB. From the trained model, the transaction scale of 2018 can be predicted, and the prediction error rate is just 5.66%. Finally, by analyzing the weight of input layer of the network, we find the influential economic factors for risk warning, which includes bank card circulation, mobile shopping transaction scale and online shopping user scale.
BP Neural Network, Mobile Payment, Risk Warning