Blacklist screening method and related product

Abstract

The invention provides a blacklist screening method and a related product. The method comprises the steps of detecting and obtaining first user data input by a target user; calling behavior data of the target user from a user database according to the first user data; extracting a first preset number of behavior characteristics of the target user from the behavior data of the target user accordingto a preset rule; sequentially comparing the numerical values of the behavior characteristics of the first preset number of target users with threshold values of corresponding risk customer behaviorcharacteristics on a blacklist platform; if the comparison result shows that the number of the behavior characteristics of the target user exceeding the threshold value of the corresponding risk customer behavior characteristics is greater than a second preset number, determining that the target user is a risk customer; and cutting off the business process which is operated by the target user. Bymeans of the method, blacklist users can be automatically recognized, and the service process of the blacklist users is cut off.

Description

Technical field [0001] This application relates to the field of blacklist technology, in particular to a blacklist screening method and related products. Background technique [0002] The existing blacklist technology generally ends the user's entire online operation process, and after manually searching for user operations and discovering malicious operations, the relevant departments take measures to add the user to the blacklist and restrict future online operations of the blacklisted user . [0003] However, due to the large number of users and frequent user operations, the amount of online operation data of users that needs to be retrieved is very large, and manual inspections are easy to miss and low efficiency. Secondly, measures can only be taken after malicious operations occur, and data is obtained afterwards to verify whether they are target customers. There is a risk of loss of profit due to data lag. It is relatively passive, unable to eliminate the malicious impact c...

AI Technical Summary

Problems solved by technology

[0002]The existing blacklist technology generally takes measures after the user's entire online operation process is completed and malicious operations are found through artificial retrieval of user operations. Add the user to the blacklist and limit the future online operations of the blacklisted user

[0003] However, due to the large number of users and frequent user operations, the amount of data that needs to be retrieved from the user's online operations is very large, and manual inspection is easy to miss and the efficiency is low.

Secondly, measures can only be taken after malicious operations occur. By obtaining data after the event and then verifying whether it is the target customer, there is a risk of loss of revenue caused by data lag. It is relatively passive and cannot eliminate the malicious influence caused by malicious operations. The initiative is poor.

Thirdly, users can perform malicious operations again after changing the registration ID on the website, and the reusability is poor

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