Abnormal transaction account group identification method and device

Abstract

The embodiment of the invention provides an abnormal transaction account group identification method and device. The method comprises the steps of respectively inputting the attribute information of each account into a machine learning model for predicting an account risk, and determining at least one account as a high-risk account; respectively executing a local community mining step for each high-risk account: taking the current high-risk account as an initiating node, respectively determining association values between other nodes and the initiating node in the graph data, and determining arisk account community corresponding to the high-risk account; and if a plurality of risk account communities are obtained and the risk account communities meet a preset non-similar requirement, determining each risk account community as an abnormal transaction account group of the target financial institution. According to the invention, the efficiency, reliability and accuracy of abnormal transaction account group identification can be effectively improved, and the operation safety and reliability of a financial institution obtaining an abnormal transaction account group through identification can be further improved.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method and device for identifying abnormal transaction account groups. Background technique [0002] Abnormal financial activities have posed an increasingly serious threat to financial institutions and regional security. Although the anti-abnormal financial work has received a lot of attention from all parties, how to effectively detect abnormal financial activities is still a challenge in view of the fact that abnormal financial means are complex and changeable, mostly in the form of group accounts and account transfers within groups are frequent. A great challenge. [0003] At present, the vast majority of methods for anti-abnormal financial activities usually establish identification models based on rule-based or account-based feature information. On the one hand, although the rule-based abnormal financial account identification method can help find some...

AI Technical Summary

Problems solved by technology

Community discovery is divided into local mining and full graph mining. For example, although there are many algorithms for community discovery, they cannot be directly applied to the identification of group-type abnormal financial transaction accounts.

First, the large number of bank accounts is not suitable for the full-graph community detection algorithm with a large amount of calculation, which will lead to the problem of low recognition efficiency due to excessive calculation; second, the local community detection algorithm, compared with Although the whole graph mining algorithm has a small amount of calculation, there is no clear choice for the initiating node of community discovery, or the selected node does not conform to the business explanation of abnormal financial behavior in the group mode, which will affect the accuracy of abnormal financial behavior identification

That is to say, the existing abnormal transaction account community identification methods cannot meet the requirements of identification efficiency and identification accuracy at the same time.

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