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Electronic banking anti-fraud method and device

An electronic banking and behavioral technology, applied in the field of computer information, can solve the problems of low accuracy rate and achieve the effect of improving accuracy rate and ensuring account security

Inactive Publication Date: 2019-01-11
BEIJING TRUSFORT TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Due to the large differences in the operation behaviors of different users, the operation behavior of the same user in different time periods will also be different. Therefore, only based on the user's current operation behavior and the existing machine learning model to detect user operation behavior security, low accuracy

Method used

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  • Electronic banking anti-fraud method and device
  • Electronic banking anti-fraud method and device
  • Electronic banking anti-fraud method and device

Examples

Experimental program
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Effect test

Embodiment 1

[0066] Such as figure 2 As shown, it is a flow chart of an electronic bank anti-fraud method provided in the embodiment of this application, and the specific steps are as follows:

[0067] S101. Obtain an operation behavior request, where the operation behavior request includes current operation behavior data currently requested by the user; and acquire historical operation behavior data within a preset time period from the current one.

[0068] In a specific implementation, when the electronic bank client detects that the user is operating the electronic bank, the electronic bank client may transmit the detected operation behavior request of the user to the electronic bank anti-fraud system. Furthermore, the electronic banking anti-fraud system can obtain the user's operation behavior request, and the operation behavior request includes the current operation behavior data currently requested by the user. Among them, the type of the operation behavior request can be divided ...

Embodiment 2

[0118] Such as Figure 8 As shown, it is a flow chart of training the operation behavior detection model in an electronic bank anti-fraud method provided by the embodiment of the present application. The specific steps are as follows:

[0119] S401. Obtain a training sample set, the training sample set includes a positive sample pair and a negative sample pair, wherein each positive sample pair includes the normal historical operation behavior data at the i-th moment and the preset time before the i-th moment Each negative sample pair includes the abnormal historical operating behavior data at the jth moment and the historical operating behavior data within the preset time period before the jth moment.

[0120] Before the operation behavior detection model to be trained is trained, a training sample set is obtained, which includes a large number of positive sample pairs and negative sample pairs, and the obtained training sample set is input into the operation behavior detecti...

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Abstract

The present application provides an electronic banking anti-fraud method, which comprises the following steps: obtaining an operation behavior request, the operation behavior request including the current operation behavior data currently requested by a user; and acquiring historical operation behavior data that is currently within a preset time period; determining a risk probability value of thecurrent operation behavior requested in the operation behavior request according to the current operation behavior data and the historical operation behavior data; comparing the risk probability valuewith the preset risk threshold value corresponding to the operation behavior request; When the risk probability value is greater than the preset risk threshold, the current operation behavior requested in the operation behavior request is intercepted. The present application combines the current operation behavior data and the historical operation behavior data to judge whether the current operation behavior requested in the user operation behavior request is normal or not, so as to improve the accuracy of judging whether the user operation behavior is normal or not, and ensure the account safety of the user.

Description

technical field [0001] This application relates to the field of computer information technology, in particular, to an anti-fraud method and device for electronic banking. Background technique [0002] At present, the rapid development of the Internet and the popularity of smart terminals have brought great convenience to users when using e-banking to handle balance inquiries, transfers, shopping payments, financial management and other services. While providing users with convenience, account information appears Abnormal operations such as brute force cracking, account information theft, account information embezzlement, and stolen funds have damaged the interests of users. [0003] Currently, in the existing technology, a training set containing historical operation behaviors is used to train a machine learning model, and then according to the user's current operation behavior and the trained machine learning model, it can be identified whether the user's current operation ...

Claims

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Application Information

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IPC IPC(8): G06Q20/40
CPCG06Q20/4016
Inventor 郭豪孙善萍宋昕蔡准孙悦郭晓鹏
Owner BEIJING TRUSFORT TECH CO LTD
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