Network transaction fraud detection system based on twin neural network

Abstract

The invention discloses a network transaction fraud detection system based on a twin neural network. The input data of the network transaction fraud detection system is composed of a group of data pairs. The network transaction fraud detection system is composed of two neural network models with the same structure, and the two neural network models achieve the twinning purpose by sharing weights.The network transaction fraud method based on the twin neural network has a very good experimental effect; according to the method, for the problems of time sequence sparsity and data imbalance in network transactions, a twinning structure is used for processing unbalanced data, and an LSTM structure is used for enabling a network to have a memory function, so that the detection capability of thenetwork on fraud transactions is improved.

Description

technical field [0001] The invention relates to a network transaction fraud model, which belongs to the field of information technology. Background technique [0002] The rapid development and popularization of financial technology has greatly promoted the development of inclusive finance, and has also made great contributions to the sound multi-level financial market. However, the development of everything has two sides. Based on the development of various technologies in financial technology, some new types of fraud methods have also emerged, and the risk of online transaction fraud has continued to escalate. [0003] In order to deal with transaction fraud, most financial institutions have established their own risk prevention and control systems. Part of the existing risk prevention and control systems are based on expert rule engines. The expert rule system is based on existing industry experience rules, which can quickly and accurately intercept existing fraudulent p...

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved by the present invention is: network transactions have the characteristics of large volume, high frequency, and fast update speed, and at the same time, the network transaction data has the problems of unbalanced positive and negative samples and sparse transaction data timing

Most of the existing methods to solve the problem of data imbalance are through sampling, but this method will change the distribution of the data set, which is not conducive to improving the generalization ability of the model

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