A Model Based on Siamese Neural Network for Online Transaction Fraud Detection

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With the rapid development of Internet finance, the volume of online transactions increases gradually, but the risk of exposure is increasing, and fraud is emerging. Because of the characteristics of online transaction, such as large volume, high frequency and fast update speed. In addition, online transaction data has the problems of unbalanced positive and negative sample and sparse timing of transaction data. Most of the existing methods to solve data imbalance are sampled, but this method will change the dataset’s distribution, which is not conducive to improving the generalization ability of the model. There are some timing characteristics of online transaction data, and the common fraud detection model does not take the problem into account in the design of the model. Based on the problems, this paper puts forward the siamese neural network structure based on CNN and LSTM, uses the siamese neural network structure to solve the problem of sample imbalance in online transaction and uses the LSTM structure to make model memory user's transaction information, in order to better detect the fraudulent transaction. The model presented in this paper is verified in real B2C transaction data, and its precision and recall reach about 95% and 96%, respectively.