Fraud group identification method based on modularity and balanced label propagation

A technology of label propagation and identification method, applied in the field of fraud gang identification based on modularity and balanced label propagation, it can solve the problems of poor fraud recognition ability and ignore the correlation of fraudulent transaction gangs, and achieve excellent accuracy, great research significance and Use value, the effect of good community structure

Active Publication Date: 2018-10-19
ZHEJIANG BANGSUN TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Traditional online fraud detection methods usually model each online transaction or merchant entity, and implement the construction of relevant characteristics of business flow for fraud detection. This method has an excellent effect on

Method used

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  • Fraud group identification method based on modularity and balanced label propagation
  • Fraud group identification method based on modularity and balanced label propagation
  • Fraud group identification method based on modularity and balanced label propagation

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Embodiment Construction

[0036] Below in conjunction with the accompanying drawings and examples, the specific implementation of the present invention will be further described in detail, the following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0037] The steps of the method for identifying fraudulent gangs based on modularity and balanced label propagation proposed by the present invention are as follows:

[0038] Step 1. Extract features such as card number, account number, ip and device fingerprint; as shown in Table 1

[0039] Table 1. Transaction Characteristics Table

[0040]

[0041]

[0042] Step 2. Use the features extracted from the transaction data to calculate pairwise similarities for all users (including fraudulent blacklists and normal users), establish a similarity matrix, and establish an association graph through the matrix, as shown in figure 1 As shown in , the circles in the figure represent user nodes, the ...

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Abstract

The invention discloses a fraud group identification method based on modularity and balanced label propagation. The method comprises: calculating the similarity degree between every two users of all users by using ID features in combination with the known fraud identifiers of the users, establishing a similarity matrix, and establishing an association diagram by using the similarity matrix; running a Louvain algorithm on the established diagram to obtain the community to which each node belongs and the hierarchical information of each node; using the community to which each node belongs, the hierarchical information of each node, and the fraud identifiers as the initial community information of each node, running a balanced label propagation process to obtain the community to which each node ultimately belongs, then dividing a network according to whether the nodes belong to the common community, and determining the fraud group according to the fraud identifiers obtained by the propagation. For the first time, the present invention applies the fraud group identification method based on modularity and balanced label propagation to the application anti-fraud and transaction anti-fraud fields. The method constructs the association diagram by using information such as transaction association, detects a fraud association by using a balanced label propagation algorithm in combinationwith the community modularity information, and prevents potential fraudulent transactions.

Description

technical field [0001] The invention belongs to the fields of transaction anti-fraud and application anti-fraud, and in particular relates to a fraud group identification method based on modularity and balanced label propagation Background technique [0002] With the explosive growth of online businesses such as e-commerce and third-party payment, online fraud cases are becoming more and more rampant, and showing a trend of changing techniques and fields. How to effectively and timely identify frequent online frauds Behavior has become a pressing issue. Traditional online fraud detection methods usually model each online transaction or merchant entity, and implement the construction of relevant characteristics of business flow for fraud detection. This method has an excellent effect on fraudulent behavior with obvious characteristics of the transaction itself, but it ignores the The gang association behind the fraudulent transaction is poor in identifying the fraud of the g...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06F17/30
CPCG06Q30/0609
Inventor 高杨唐迪佳孙斌杰王新根鲁萍黄滔
Owner ZHEJIANG BANGSUN TECH CO LTD
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