CN107943879A - Fraud group detection method and system based on social networks - Google Patents
Fraud group detection method and system based on social networks Download PDFInfo
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Abstract
The invention discloses a kind of fraud group detection method and system based on social networks, wherein method includes the following steps：The step of S1 is used to obtain test source data by social graph；The step of S2 is used to be tested test source data to system under test (SUT) and generates prediction model；The step of S3 is used to perform operation by the fraud group detection technique based on social networks.Fraud group detection method and system provided by the present invention based on social networks, fraud group detection technique based on social networks, user can excavate potential colony according to social networks and cheat, predict fraud colony, be conducive to improve the global risk identification ability of network, avoid unnecessary risk loophole.
Description
Technical field
The present invention relates to computer software technical field, more particularly to fraud group detection method based on social networks and
System.
Background technology
Machine learning techniques based on figure are not only widely used in image, natural language processing, knowledge mapping, network peace
Congruent field, it is also proved to extremely effectively reliable in financial anti-fraud link.Especially in the market ring of current general favour finance
Under border, risk of fraud change is very frequent on line, single individual fraud in the past evolved into rapidly in a organized way, have the group of scale
Body is cheated and corresponding co-related risks.And traditional anti-fraudulent mean includes authentication, customer information logic verify, exterior letter
The modes such as the contrast verification of breath, blacklist filtering are main or in identification individual risk, can not be dug according to the relation of countless ties
Dig potential colony's fraud, it is impossible to predict " good " or the disaggregated model of " bad " people, can not network global risk identification
Ability covers the risk loophole of this part.
The content of the invention
The object of the present invention is to provide a kind of fraud group detection method and system based on social networks.
Fraud group detection method provided by the present invention based on social networks, includes the following steps：
The step of S1 is used to obtain test source data by social graph；
The step of S2 is used to be tested test source data to system under test (SUT) and generates prediction model；
The step of S3 is used to perform operation by the fraud group detection technique based on social networks.
Fraud group detecting system provided by the present invention based on social networks, including：For being obtained by social graph
Take the module of test source data；For test source data to test system under test (SUT) and generates the module of prediction model；With
In the module that operation is performed by the fraud group detection technique based on social networks.
In this way, the fraud group detection technique based on social networks, user can excavate potential group according to social networks
Body is cheated, and predicts fraud colony, is conducive to improve the global risk identification ability of network, is avoided unnecessary risk loophole.
Brief description of the drawings
The step of fraud group detection method based on social networks that Fig. 1,2 are provided by the embodiment of the present invention one, is illustrated
Figure；
Fig. 3 is the step schematic diagram that S1 described in the embodiment of the present invention one is used to obtain test source data by social graph；
Fig. 4 is used to be tested test source data to system under test (SUT) and generates prediction model for S2 described in the embodiment of the present invention one
Step schematic diagram.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
All other embodiments obtained without making creative work, belong to the scope of protection of the invention.
Embodiment one
As shown in Figure 1, 2, the present embodiment provides a kind of fraud group detection method based on social networks, include the following steps：
The step of S1 is used to obtain test source data by social graph；
The step of S2 is used to be tested test source data to system under test (SUT) and generates prediction model；
The step of S3 is used to perform operation by the fraud group detection technique based on social networks.
It will be understood by those skilled in the art that it is described test source data can include user authorize address list, message registration,
The information such as short message record, programmed emergency.In this way, the fraud group detection technique based on social networks, user can be according to society
The potential colony's fraud of relation excavation is handed over, fraud colony is predicted, is conducive to improve the global risk identification ability of network, avoids
Unnecessary risk loophole.
As shown in figure 3, the S1 is used to include the step of obtaining test source data by social graph：
S11 applies the step of being collected to user social contact relation by social graph；
The data being collected into are established the step of relational network between user by S12 by machine learning engine；
S13 is by the generating algorithm model and rule of above-mentioned relation network and the step of be arranged at different scenes.
It will be understood by those skilled in the art that in scenes such as anti-frauds, can be complementary with common application, complete credit
The functions such as assessment, anti-fraud.The social graph application is to authorize address list by the collection user to user social contact relation, lead to
Record, the information such as short message record, programmed emergency are talked about, then establishes by machine learning engine relational network between user, and
From learning and training algorithm model and rule, applied to different business fields such as collections after credit evaluation, fraud identification, loan
Scape.
As shown in figure 4, the S2 is used to be tested test source data to system under test (SUT) and generates the step of prediction model
Suddenly include：
S21 will have the step of applicant marked whether promise breaking is set to primordial seed node；
S22 by using the semi-supervised algorithm based on figure, by whether the mark broken a contract is broadcast to the applicant of no label the step of；
S23 is according to the graph structure for having a small amount of label node, with the other step of tag class of the unmarked node of propagation algorithm prediction.
It will be understood by those skilled in the art that in financial business, each applicant, cell-phone number, equipment, IP address
It is the node in figure, and it is exactly the side in figure that applicant, which possesses the oriented contact such as equipment, cell-phone number calling handset number, side
Weight is associated tightness degree.In the fraud group detection technique based on social networks, those have what is marked whether promise breaking
Applicant is primordial seed node, by using the semi-supervised algorithm based on figure, whether the mark broken a contract is broadcast to no label
Applicant, extremely huge risk network can be thus constructed on the sample for having label on a small quantity, finally makes to be formed
Effectively reliable violation correction model.
Further, the S3 is used to wrap the step of performing operation by the fraud group detection technique based on social networks
Include：The step of S31 is used to social graph data are stored and calculated.
It will be understood by those skilled in the art that the fraud group detection technique based on social networks technology realization on, base
In frames such as Spark GraphX, Neo4j, it is established that the storage and calculating of social graph data, not only facilitate data technique people
Member's operation, also has business scope expert good interface, propulsion of the support team in modeling and model evolution.
Further, it is described to be used to include the step of social graph data are stored and calculated：S311 builds blacklist
The step of collection of illustrative plates.
Further, the step of S311 structures blacklist collection of illustrative plates includes：
The natural assumption that S3110 is set based on blacklist；
S3111 analyzes the characteristics of each subgraph；
S3112 establishes collection of illustrative plates computation model and corresponding strategy rule；
S3113 establishes the related client of blacklist client corresponding strategy of rejecting loans.
It will be understood by those skilled in the art that the blacklist application of the graphic chart of social networks structure, based on " it is near black that be influenced by close association
Person is black " natural assumption, the characteristics of each subgraph community of analyzing and researching, showed with reference to business, establish collection of illustrative plates computation model and corresponding
Policing rule, for the corresponding strategy of rejecting loans of client related with blacklist client foundation, reduces loss.It is for example, right
In the client of application, during application approval, its address list authorized, message registration etc. " relation " can be tagged in real time
In big data platform socialgram, if there is the black list user judged in system discovery client associated with it, have
Reason believes that the client there are credit risk and risk of fraud, considers, which exists with blacklist client from operational angle
It is closely connected, it is most likely that the problems such as being accused of group's fraud, joint liabilities.
Embodiment two
The present embodiment provides a kind of fraud group detecting system based on social networks, including：
For obtaining the module of test source data by social graph；
For test source data to test system under test (SUT) and generates the module of prediction model；
For performing the module of operation by the fraud group detection technique based on social networks.
It will be understood by those skilled in the art that it is described test source data can include user authorize address list, message registration,
The information such as short message record, programmed emergency.In this way, the fraud group detection technique based on social networks, user can be according to society
The potential colony's fraud of relation excavation is handed over, fraud colony is predicted, is conducive to improve the global risk identification ability of network, avoids
Unnecessary risk loophole.
Further, the module for being used to obtain test source data by social graph includes：
The submodule being collected to user social contact relation is applied by social graph；
The data being collected into are established by machine learning engine to the submodule of the relational network between user；
By the generating algorithm model and rule of above-mentioned relation network and it is arranged at the submodule of different scenes.
It will be understood by those skilled in the art that in scenes such as anti-frauds, can be complementary with common application, complete credit
The functions such as assessment, anti-fraud.The social graph application is to authorize address list by the collection user to user social contact relation, lead to
Record, the information such as short message record, programmed emergency are talked about, then establishes by machine learning engine relational network between user, and
From learning and training algorithm model and rule, applied to different business fields such as collections after credit evaluation, fraud identification, loan
Scape.
Further, it is described to be used to be tested test source data to system under test (SUT) and generate the module bag of prediction model
Include：
The submodule that will there is the applicant marked whether promise breaking to be set to primordial seed node；
By using the semi-supervised algorithm based on figure, judge whether that the mark of promise breaking is broadcast to the submodule of the applicant of no label
Block；
According to the graph structure for having a small amount of label node, with the other submodule of tag class of the unmarked node of propagation algorithm prediction.
It will be understood by those skilled in the art that in financial business, each applicant, cell-phone number, equipment, IP address
It is the node in figure, and it is exactly the side in figure that applicant, which possesses the oriented contact such as equipment, cell-phone number calling handset number, side
Weight is associated tightness degree.In the fraud group detection technique based on social networks, those have what is marked whether promise breaking
Applicant is primordial seed node, by using the semi-supervised algorithm based on figure, whether the mark broken a contract is broadcast to no label
Applicant, extremely huge risk network can be thus constructed on the sample for having label on a small quantity, finally makes to be formed
Effectively reliable violation correction model.
Further, the module for being used to perform operation by the fraud group detection technique based on social networks includes：
For the submodule that social graph data are stored and calculated.
It will be understood by those skilled in the art that the fraud group detection technique based on social networks technology realization on, base
In frames such as Spark GraphX, Neo4j, it is established that the storage and calculating of social graph data, not only facilitate data technique people
Member's operation, also has business scope expert good interface, propulsion of the support team in modeling and model evolution.
Further, the submodule for social graph data to be stored and calculated includes：Build blacklist figure
The modular unit of spectrum.
Further, building the modular unit of blacklist collection of illustrative plates includes：
The natural assumption set based on blacklist；
The characteristics of analyzing each subgraph；
Establish collection of illustrative plates computation model and corresponding strategy rule；
Corresponding strategy of rejecting loans is established to the related client of blacklist client.
It will be understood by those skilled in the art that the blacklist application of the graphic chart of social networks structure, based on " it is near black that be influenced by close association
Person is black " natural assumption, the characteristics of each subgraph community of analyzing and researching, showed with reference to business, establish collection of illustrative plates computation model and corresponding
Policing rule, for the corresponding strategy of rejecting loans of client related with blacklist client foundation, reduces loss.It is for example, right
In the client of application, during application approval, its address list authorized, message registration etc. " relation " can be tagged in real time
In big data platform socialgram, if there is the black list user judged in system discovery client associated with it, have
Reason believes that the client there are credit risk and risk of fraud, considers, which exists with blacklist client from operational angle
It is closely connected, it is most likely that the problems such as being accused of group's fraud, joint liabilities.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
To modify to the technical solution described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical solution spirit and
Scope.
Claims (12)
1. a kind of fraud group detection method based on social networks, it is characterised in that include the following steps：
The step of S1 is used to obtain test source data by social graph；
The step of S2 is used to be tested test source data to system under test (SUT) and generates prediction model；
The step of S3 is used to perform operation by the fraud group detection technique based on social networks.
2. the fraud group detection method based on social networks as claimed in claim 1, it is characterised in that the S1 is used to lead to
Crossing the step of social graph obtains test source data includes：
S11 applies the step of being collected to user social contact relation by social graph；
The data being collected into are established the step of relational network between user by S12 by machine learning engine；
S13 is by the generating algorithm model and rule of above-mentioned relation network and the step of be arranged at different scenes.
3. the fraud group detection method based on social networks as claimed in claim 2, it is characterised in that S2 is used for will test
Source data is tested system under test (SUT) and is included the step of generating prediction model：
S21 will have the step of applicant marked whether promise breaking is set to primordial seed node；
S22 by using the semi-supervised algorithm based on figure, by whether the mark broken a contract is broadcast to the applicant of no label the step of；
S23 is according to the graph structure for having a small amount of label node, with the other step of tag class of the unmarked node of propagation algorithm prediction.
4. the fraud group detection method based on social networks as claimed in claim 3, it is characterised in that the S3 is used to lead to
Crossing the step of fraud group detection technique based on social networks performs operation includes：S31 is used to carry out social graph data
The step of storage is with calculating.
5. the fraud group detection method based on social networks as claimed in claim 4, it is characterised in that described to be used for society
Intersection graph modal data is stored and included the step of calculating：S311 builds the step of blacklist collection of illustrative plates.
6. the fraud group detection method based on social networks as claimed in claim 5, it is characterised in that S311 builds black name
The step of free hand drawing is composed includes：
The natural assumption that S3110 is set based on blacklist；
S3111 analyzes the characteristics of each subgraph；
S3112 establishes collection of illustrative plates computation model and corresponding strategy rule；
S3113 establishes the related client of blacklist client corresponding strategy of rejecting loans.
7. a kind of fraud group detecting system based on social networks, including：
For obtaining the module of test source data by social graph；
For test source data to test system under test (SUT) and generates the module of prediction model；
For performing the module of operation by the fraud group detection technique based on social networks.
8. the fraud group detection method based on social networks as claimed in claim 7, it is characterised in that described to be used to pass through
The module that social graph obtains test source data includes：
The submodule being collected to user social contact relation is applied by social graph；
The data being collected into are established by machine learning engine to the submodule of the relational network between user；
By the generating algorithm model and rule of above-mentioned relation network and it is arranged at the submodule of different scenes.
9. the fraud group detection method based on social networks as claimed in claim 8, it is characterised in that for by test source
Data, which test system under test (SUT) and generate the module of prediction model, to be included：
The submodule that will there is the applicant marked whether promise breaking to be set to primordial seed node；
By using the semi-supervised algorithm based on figure, judge whether that the mark of promise breaking is broadcast to the submodule of the applicant of no label
Block；
According to the graph structure for having a small amount of label node, with the other submodule of tag class of the unmarked node of propagation algorithm prediction.
10. the fraud group detection method based on social networks as claimed in claim 9, it is characterised in that described to be used to lead to
Crossing the module of the fraud group detection technique execution operation based on social networks includes：For being stored to social graph data
With the submodule of calculating.
11. the fraud group detection method based on social networks as claimed in claim 10, it is characterised in that it is described be used for pair
The submodule that social graph data are stored and calculated includes：Build the modular unit of blacklist collection of illustrative plates.
12. the fraud group detection method based on social networks as claimed in claim 11, it is characterised in that structure blacklist
The modular unit of collection of illustrative plates includes：
The natural assumption set based on blacklist；
The characteristics of analyzing each subgraph；
Establish collection of illustrative plates computation model and corresponding strategy rule；
Corresponding strategy of rejecting loans is established to the related client of blacklist client.
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| CN108681936A (en) * | 2018-04-26 | 2018-10-19 | 浙江邦盛科技有限公司 | A kind of fraud clique recognition methods propagated based on modularity and balance label |
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| CN109191281A (en) * | 2018-08-21 | 2019-01-11 | 重庆富民银行股份有限公司 | A kind of group's fraud identifying system of knowledge based map |
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