CN110413901A - A kind of assessing credit risks method based on social network analysis - Google Patents

A kind of assessing credit risks method based on social network analysis Download PDF

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CN110413901A
CN110413901A CN201910713787.9A CN201910713787A CN110413901A CN 110413901 A CN110413901 A CN 110413901A CN 201910713787 A CN201910713787 A CN 201910713787A CN 110413901 A CN110413901 A CN 110413901A
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CN110413901B (en
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付志超
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Shenzhen Suoxinda Data Technology Co Ltd
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    • G06Q50/01—Social networking

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Abstract

A kind of assessing credit risks method based on social network analysis, it is related to credit financing air control assessment system technical field, the credit financing methods of risk assessment for solving existing credit financing air control assessment system manually relies on program height, in the presence of the low technical deficiency of fraud discrimination, include: data collection and cleaning；Social networks building；Social networks optimization；It is being made into different size of social networks based on history application part, the social networks of preset value is less than for application part, is cut；The building of network Rating Model；Realize the risk quantification to application part；Network graphic is shown.Risk assessment is carried out from new mode, is not only the supplement to current risk appraisal procedure, and have more effective identification for clique or group's fraud.

Description

A kind of assessing credit risks method based on social network analysis
Technical field
The present invention relates to credit financing air control assessment system technical fields, and in particular to assessing credit risks system Assessing credit risks method improves aspect.
Background technique
In recent years, with the fast development of credit financing business, while financial general favour masses, also because overdue and promise breaking is given Financial circles bring massive losses.Especially under the dual-impingement that macroeconomy downlink and supervision are severely administered, bank, China Industry has fallen into bad soaring predicament, and huge overdue loan burden causes credit resource to be difficult to discharge.Risk management ability Power will become the following core distinguished bank and manage superiority and inferiority.
Current credit financing methods of risk assessment is mainly based on expertise and traditional machine learning algorithm.Expert Business personnel is empirically based on to the familiarity of fraud scene, simple in rule, discrimination is not high.Machine learning utilizes great amount of samples Model training is carried out, fraud discrimination can be improved.But these two kinds of methods all carry out wind just with application part correlated characteristic variable Control identification does not excavate application part and applies for personage's social network relationships of part behind to carry out risk assessment.
Summary of the invention
In conclusion it is an object of the invention to solve the credit financing risk of existing credit financing air control assessment system Appraisal procedure manually relies on program height, there is the low technical deficiency of fraud discrimination, and proposes a kind of based on social network analysis Assessing credit risks method.
In order to solve technical problem proposed by the invention, the technical solution of use are as follows:
A kind of assessing credit risks method based on social network analysis, it is characterised in that the method includes having:
Data collection and cleaning；It collects application part and corresponds to the related social attribute feature of applicant, and cleaned, standard Change and packet transaction；
Social networks building；To apply for part as center node, related social attribute characteristic information is connecting node, according to not With application part in the matching relationship of identical connecting node, network building is carried out；
Social networks optimization；It is being made into different size of social networks based on history application part, for application part less than pre- If the social networks of value, is cut；
The building of network Rating Model；After completing social networks optimization, need to carry out risk score, each application to network Part will all belong to a network；It is then based on network structure and applies for the correlated characteristic of part, the machining feature factor constructs risk Rating Model realizes the risk quantification to application part；
Network graphic is shown；Diagram data is generated according to the social network relationships of application part, and is illustrated as social network diagram, into One step customizes according to demand shows style.
Echnical solution as defined further in the present invention includes:
In the data-gathering process, the related social attribute feature that the application part of collection corresponds to applicant includes: Based on the related structured and unstructured data of history application part, including address, contact method, circle of friends, contact person, identity Card, referrer apply for the overdue of part and promise breaking relevant information, the credit information of applicant, black and white lists information.
Data cleansing includes following treatment process:
1. shortage of data and abnormal processing；
2. entity extracts, entity object is extracted from structural data and unstructured data, entity attribute, between entity Relationship；
3. entity fusion, disappear qi: guaranteeing consistency, merged, disambiguated to unstructured data, standardize and obscure Match, is then grouped processing, same group of text variable distributes identical group number, and is grouped volume to all text features Code.
Complete social networks optimization after, the Rating Model to network carry out risk score, using logistic regression into Application part is labeled as positive negative sample, and according to network struction according to the markup information of history application part by the building of row risk model Correlated characteristic, training Logic Regression Models are as follows:
Logit (p)=β0+β1x1+β2x2+…+βkxk
The wherein probability of p=P (y=1 | X=x).
The invention has the benefit that the present invention corresponds to the related social attribute feature of applicant by collecting application part, It then, can be according to application part after social networks building, social networks optimization and network Rating Model building relevant treatment Social network relationships generate diagram data, and are illustrated as social network diagram, facilitate business personnel according to social network relationships carry out into The investigation of one step, the present invention carry out risk assessment from new mode, are not only the supplement to current risk appraisal procedure, and There is more effective identification for clique or group's fraud.
Detailed description of the invention
Fig. 1 is workflow schematic diagram of the invention；
Fig. 2 is social networks map；
Fig. 3 is to generate diagram data, and the social networks map being illustrated as according to the social network relationships of application part.
Specific embodiment
The present invention is further described below in conjunction with attached drawing and currently preferred specific embodiment.
Referring to figs. 1 to shown in Fig. 3, the present invention is based on the assessing credit risks method of social network analysis, feature exists In the method includes having:
Data collection and cleaning；It collects application part and corresponds to the related social attribute feature of applicant, and cleaned, standard Change and packet transaction；
Social networks building；To apply for part as center node, related social attribute characteristic information is connecting node, according to not With application part in the matching relationship of identical connecting node, network building is carried out；Based on this, the social network of history application part is constructed Network relationship, it will big and small social networks map is formed, shown in Fig. 2；The social network relationships figure will show different Shens It please connection relationship between part.There is certain association each other in the application part of consolidated network.
Social networks optimization；It is being made into different size of social networks based on history application part, for application part less than pre- If the social networks of value, is cut；Only one possible application part of some social networks, does not construct with other application parts and joins System.There may be hundreds and thousands of application parts for some networks, and the connection of network actually exists connection power.In Weak link Node, need to cut network, form relatively reasonable network group；
The building of network Rating Model；After completing social networks optimization, need to carry out risk score, each application to network Part will all belong to a network；It is then based on network structure and applies for the correlated characteristic of part, the machining feature factor constructs risk Rating Model realizes the risk quantification to application part；
Network graphic is shown；Diagram data is generated according to the social network relationships of application part, and is illustrated as social network diagram, into One step customizes according to demand shows style.It is further investigated in order to facilitate business personnel according to social network relationships, it can To generate diagram data according to the social network relationships of application part, and it is illustrated as social network diagram.Exhibition is further customized according to demand Show that style is as shown in Figure 3.
Technical solution of the present invention is described in detail as follows:
1, data collection: social networks related data is compiled.Related structured and non-knot based on history application part Structure data.Including but not limited to: all kinds of addresses, contact method, circle of friends, contact person, identity card, referrer apply for part Overdue and promise breaking relevant information, the credit information of applicant, black and white lists information etc..
2, data cleansing and pretreatment.Mainly include following treatment process:
1. shortage of data and abnormal processing.
2. entity extracts.Entity object is extracted from structural data and unstructured data, such as: customer name, customer account Number；Entity attribute, such as: company name, cell-phone number, home address；Relationship between entity, such as: Peer Relationships, neighborhood, visitor Family is with holding relationship etc. between cell-phone number.
3. entity fusion, disappear qi: guaranteeing consistency.Unstructured data is merged, is disambiguated, standardizes and obscures Match, is then grouped processing, same group of text variable distributes identical group number, and is grouped volume to all text features Code.
3, social networks constructs.According to the good history request for data of packet transaction, to apply for node, application centered on part The relevant information of part carries out network struction as connecting node, according to nonoriented edge relationship.Big and small social network will be will form Network map.
4, social networks optimizes.Also network cutting or community discovery are cried.It is different size of being made into based on history application part Social networks, there may be hundreds and thousands of application parts for some networks, and the connection of network actually exists connection power.In The node of Weak link needs to cut network, forms the relatively reasonable network group (community) of size.
5, network Rating Model constructs.After completing social networks optimization, need to carry out risk score to network.It is sharp herein Risk model building is carried out with logistic regression.According to the markup information of history application part, application part is labeled as positive negative sample.And According to network struction correlated characteristic, training Logic Regression Models.It is as follows:
Logit (p)=β0+β1x1+β2x3+…+βkxk
The wherein probability of p=P (y=1 | X=x).
It can refer to following dimension derivative feature:
1, feature of risk.Apply for number of packages, blacklist application number in network, gray list application number, network in network in network In overdue application number, promise breaking application number of packages in network, whether application part history occur the information such as overdue, promise breaking；
2, authentication feature." same identity card difference mobile phone " rule triggering number, " same company name different address " rule Number is triggered, " same mailbox different identity card " rule triggering number etc.；
3, essential attribute feature.The average application age of consolidated network, income etc.；
4, link information feature.Apply for part and blacklist, gray list, overdue, the connection number of promise breaking application part etc.；
6, network is shown.It is further investigated in order to facilitate business personnel according to social network relationships, Ke Yigen Diagram data is generated according to the social network relationships of application part, and is illustrated as social network diagram.Further customization shows wind according to demand Lattice.

Claims (4)

1. a kind of assessing credit risks method based on social network analysis, it is characterised in that the method includes having:
Data collection and cleaning；Collect application part correspond to the related social attribute feature of applicant, and cleaned, standardize and Packet transaction；
Social networks building；To apply for part as center node, related social attribute characteristic information is connecting node, according to different Shens Please part identical connecting node matching relationship, carry out network building；
Social networks optimization；It is being made into different size of social networks based on history application part, preset value is less than for application part Social networks, cut；
The building of network Rating Model；After completing social networks optimization, need to carry out risk score, each application part to network A network will be belonged to；It is then based on network structure and applies for the correlated characteristic of part, the machining feature factor constructs risk score Model realizes the risk quantification to application part；
Network graphic is shown；Diagram data is generated according to the social network relationships of application part, and is illustrated as social network diagram, further Customization shows style according to demand.
2. a kind of assessing credit risks method based on social network analysis according to claim 1, it is characterised in that: institute In the data-gathering process stated, the related social attribute feature that the application part of collection corresponds to applicant includes: being based on history Shen Please part related structured and unstructured data, including address, contact method, circle of friends, contact person, identity card, referrer, Apply for the overdue of part and promise breaking relevant information, the credit information of applicant, black and white lists information.
3. a kind of assessing credit risks method based on social network analysis according to claim 1, it is characterised in that: number Include following treatment process according to cleaning:
1. shortage of data and abnormal processing；
2. entity extracts, entity object is extracted from structural data and unstructured data, entity attribute, the pass between entity System；
3. entity fusion, disappear qi: guarantee consistency, unstructured data is merged, is disambiguated, standardization and fuzzy matching, Then it is grouped processing, same group of text variable distributes identical group number, and is grouped coding to all text features.
4. a kind of assessing credit risks method based on social network analysis according to claim 1, it is characterised in that: In After completing social networks optimization, the Rating Model carries out risk score to network, carries out risk model using logistic regression Application part is labeled as positive negative sample according to the markup information of history application part by building, and according to network struction correlated characteristic, Training Logic Regression Models are as follows:
Logit (p)=β0+β1x1+β2x2+…+βkxk
The wherein probability of p=P (y=1 | X=x).
CN201910713787.9A 2019-08-02 2019-08-02 Credit risk assessment method based on social network analysis Active CN110413901B (en)

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Cited By (3)

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CN113850663A (en) * 2021-08-24 2021-12-28 江苏中交车旺科技有限公司 Data processing method, system, equipment and medium for new user recommendation
CN114462414A (en) * 2022-04-11 2022-05-10 北京微聚未来科技有限公司 Personal credit evaluation method and system based on nested named entity recognition technology
CN116861101A (en) * 2023-06-30 2023-10-10 深圳市爱彼利科技有限公司 Data processing method and device for social matching

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113850663A (en) * 2021-08-24 2021-12-28 江苏中交车旺科技有限公司 Data processing method, system, equipment and medium for new user recommendation
CN114462414A (en) * 2022-04-11 2022-05-10 北京微聚未来科技有限公司 Personal credit evaluation method and system based on nested named entity recognition technology
CN116861101A (en) * 2023-06-30 2023-10-10 深圳市爱彼利科技有限公司 Data processing method and device for social matching
CN116861101B (en) * 2023-06-30 2024-03-29 深圳市爱彼利科技有限公司 Data processing method and device for social matching

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