Method for training fraudulent transaction detection model, detection method and corresponding device

Abstract

The embodiment of this specification provides a method for training a fraudulent transaction detection model, the fraudulent transaction detection model includes a convolutional layer and a classifier layer, the training method includes: obtaining a classification sample set, and the calibration samples in the sample set include user operation sequences and time sequences , the user operation sequence includes a predetermined number of user operations arranged in time order, and the time sequence includes time intervals between adjacent user operations in the user operation sequence. For such a sample set, in the convolution layer, the first convolution processing is performed on the user operation sequence to obtain the first convolution data; the second convolution processing is performed on the time series to obtain the second convolution data; The first convolution data and the second convolution data are combined to obtain time-adjusted convolution data. The thus obtained time-adjusted convolutional data is fed into a classifier layer, and a fraudulent transaction detection model is trained based on the classification results of the classifier layer. Such a trained model can detect fraudulent transactions more effectively.

Description

technical field [0001] One or more embodiments of this specification relate to the field of computer technology, and in particular, to a method for training a fraudulent transaction detection model, a method for detecting fraudulent transactions, and a corresponding device. Background technique [0002] The development of Internet technology has made people's lives more and more convenient. People can use the Internet to conduct various transactions and operations such as shopping, payment, bill payment, and transfer. However, at the same time, the security problems caused by it are becoming more and more prominent. In recent years, financial fraud has occurred from time to time, and criminals have used various means to trick users into making some fraudulent transactions. For example, some fraudulent links are disguised as official links of banks or communication companies to induce users to pay fees or transfer money; or, through some false information, trick users into o...

AI Technical Summary

Problems solved by technology

However, the detection method is not comprehensive and the results are not accurate enough

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