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Social network-based fraud group detection method and system

A technology of social network and detection method, applied in the field of fraud group detection, can solve the problems of mining group fraud, unable to predict classification model, unable to cover and so on

Inactive Publication Date: 2018-04-20
上海维信荟智金融科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, traditional anti-fraud methods include identity verification, customer information logic verification, external information comparison verification, blacklist filtering, etc., mainly to identify individual risks, and cannot dig out potential group fraud based on the inextricable relationship. The classification model that predicts "good" or "bad" people cannot cover this part of the risk vulnerability based on the global risk identification ability of the network

Method used

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  • Social network-based fraud group detection method and system
  • Social network-based fraud group detection method and system
  • Social network-based fraud group detection method and system

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0015] Such as figure 1 , 2 As shown, the present embodiment provides a method for detecting fraudulent groups based on social networks, including the following steps:

[0016] S1 is a step for obtaining test source data through a social graph;

[0017] S2 is a step for testing the system under test with the test source data and generating a prediction model;

[0018] S3 is a step for performing operations through social network-based fraudulent group detection techniques.

[0019] Those skilled in the art can understand that the test source data may include user authorized address book, call records, short message records, emergency contacts and other information. In this way, based on social network-based fraud group detection technology, users can mine potential group frauds according to social relationships and predict fraud groups, which is conducive to improving the global risk identification ability of the network and avoiding unnecessary risk loopholes.

[0020] Su...

Embodiment 2

[0040] This embodiment provides a system for detecting fraudulent groups based on social networks, including:

[0041] A module for obtaining test source data through social graphs;

[0042] A module for testing the system under test with test source data and generating a predictive model;

[0043] A module for performing operations through social network-based fraudulent community detection techniques.

[0044] Those skilled in the art can understand that the test source data may include user authorized address book, call records, short message records, emergency contacts and other information. In this way, based on social network-based fraud group detection technology, users can mine potential group frauds according to social relationships and predict fraud groups, which is conducive to improving the global risk identification ability of the network and avoiding unnecessary risk loopholes.

[0045] Further, the module for obtaining test source data through a social graph i...

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Abstract

The invention discloses a social network-based fraud group detection method and system. The method comprises the following steps of S1, obtaining test source data through a social graph; S2, testing atested system by the test source data and generating a prediction model; and S3, executing operation through a social network-based fraud group detection technology. According to the social network-based fraud group detection method and system provided by the invention, through the social network-based fraud group detection technology, a user can mine potential group fraud according to a social relationship and predict fraud groups, so that global risk identification capability of a network can be improved and unnecessary risk vulnerabilities are avoided.

Description

technical field [0001] The invention relates to the technical field of computer software, in particular to a social network-based fraud group detection method and system. Background technique [0002] Graph-based machine learning technology is not only widely used in image, natural language processing, knowledge map, network security and other fields, but also proved to be extremely effective and reliable in financial anti-fraud. Especially in the current market environment of inclusive finance, the risk of online fraud changes very frequently. In the past, a single individual fraud has rapidly evolved into an organized and large-scale group fraud and corresponding associated risks. However, traditional anti-fraud methods include identity verification, customer information logic verification, external information comparison verification, blacklist filtering, etc., mainly to identify individual risks, and cannot dig out potential group frauds based on inextricable relationshi...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q10/04G06Q10/06G06Q50/00
CPCG06Q10/04G06Q10/0635G06Q50/01G06F16/9024
Inventor 金家芳陈斌张俊飞匡文豪薛贤巨
Owner 上海维信荟智金融科技有限公司
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