CN112598507A - Excessive credit granting risk prediction system and method based on knowledge graph - Google Patents
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Abstract
The application provides a system and a method for predicting excessive credit granting risk based on a knowledge graph, which comprises a data platform, a computing platform and an application platform; the data platform is used for constructing a member map of the enterprise group according to public opinion data and full industrial and commercial data of the enterprise group based on a knowledge map technology; the computing platform is used for computing the credit scale of the enterprise group according to the member map and the credit data of each member in the member map; and the application platform is used for determining the credit granting risk condition of the enterprise group according to the credit scale. The system avoids the situation that a plurality of members in a group are simultaneously credited to cause excessive crediting, provides an effective identification means for excessive risk crediting supervision of the group or a cluster, reduces the risk of a financial risk supervision system, and improves the stability of the financial risk supervision system.
Description
Technical Field
The application relates to the technical field of computers, in particular to a system and a method for predicting excessive credit granting risk based on a knowledge graph.
Background
Financing is the act of an enterprise raising funds. When enterprises excessively finance and occupy a large amount of social resources, serious financial risk hidden danger can be caused to market economy. At present, in order to restrict excessive authorization and financing of a single enterprise by one or more financial institutions, a corresponding supervision system has been adopted by a supervision department aiming at centralized risks of credit granting. However, since some enterprises exist in the form of enterprise groups, the enterprise groups currently lack information technology means to effectively identify the potential enterprises with excessive credit, and further lack necessary identification means for the enterprise groups/clusters.
Disclosure of Invention
The embodiment of the application aims to provide a system and a method for predicting excessive credit granting risk based on a knowledge graph, which are applied to multi-head excessive credit granting risk management of an enterprise group through the knowledge graph and a group identification algorithm, identify internal group members of the enterprise group, supervise only excessive credit granting risk of the enterprise group, exceed the supervision category of excessive credit granting of a single large enterprise, provide an identification means for the enterprise group/cluster, and improve the stability of financial risk supervision.
In a first aspect, an embodiment of the present application provides a system for predicting excessive credit risk based on a knowledge graph, which includes a data platform, a computing platform, and an application platform;
the data platform is used for constructing a member map of the enterprise group according to public opinion data and full industrial and commercial data of the enterprise group based on a knowledge map technology;
the computing platform is used for computing the credit scale of the enterprise group according to the member map and the credit data of each member in the member map;
and the application platform is used for determining the credit granting risk condition of the enterprise group according to the credit scale.
In the implementation process, the data platform, the computing platform and the application platform in the excessive credit granting risk prediction system are established, and the membership of the enterprise group is combed by the data platform through the knowledge map technology, so that the total credit granting risk prediction can be performed on the group members in the enterprise group through the computing platform and the application platform, the situation that excessive credit granting is caused by the fact that a plurality of members in the group are granted with credit at the same time is avoided, an effective identification means is provided for the excessive risk credit granting supervision of the group or the cluster, the risk of the financial risk supervision system is reduced, and the stability of the financial risk supervision system is improved.
Further, a data platform comprising:
the acquisition module is used for acquiring public opinion data and full business data of an enterprise group;
the first determining module is used for determining group members and member control relations in an enterprise group according to public opinion data and full industrial and commercial data;
and the building module is used for building the member map of the enterprise group according to the group member and the member control relationship.
In the implementation process, public opinion data and full-scale industrial and commercial data are utilized through the acquisition module, the first determination module and the construction module to trace members and control relations of the group or the similar group, and a member map is constructed according to the members and the control relations, so that the structures of the members in the group can be clearly known, and the subsequent trust risk aiming at the group can be predicted.
Further, the first determining module includes:
the determining unit is used for determining the control relation between the leading enterprise and the member in the enterprise group according to the public sentiment data and the full-scale industrial and commercial data;
and the traversal unit is used for traversing the member control relationship downwards by taking the tap enterprise as an initial node to obtain all group members of the enterprise group.
In the implementation process, the control relationship of the members is traversed by taking the faucet enterprises in the group as the starting nodes through the determining unit and the traversing unit, so that the group members can be rapidly known, and the identification efficiency of the group members is improved.
Further, a building block comprising:
and the building unit is used for building a member map of the enterprise group by taking the group members in the enterprise group as data entities and taking the member control relationship as a data relationship.
In the implementation process, the group members are used as data entities, the member control relationship is used as a data relationship, and the member map is constructed, so that the group member structure of the enterprise group can be more clearly and intuitively expressed.
Further, an application platform comprising:
the scoring module is used for scoring the enterprise group by using a preset credit granting risk scoring strategy according to the credit scale to obtain a scoring result;
and the second determination module is used for determining the credit granting risk condition of the enterprise group based on the scoring result.
In the implementation process, the identification of the group excessive credit risk is realized through the scoring model and the second determination model, and the risk of a supervision system is reduced.
Further, the application platform further comprises:
and the pushing module is used for sending the credit granting risk condition of the enterprise group to a preset terminal.
In the implementation process, the multi-head excessive credit granting risk management report of the group is pushed instead of directly intervening in bank operation and the credit limit is proposed to each bank, so that the operation aspect does not relate to commercial confidentiality and is easier to fall to the ground.
In a second aspect, an embodiment of the present application provides a method for predicting excessive credit risk based on a knowledge graph, including:
based on knowledge graph technology, establishing a member graph of an enterprise group according to public sentiment data and full-scale industrial and commercial data of the enterprise group;
calculating the credit scale of the enterprise group according to the member map and the credit data of each member in the member map;
and determining the credit granting risk condition of the enterprise group according to the credit scale.
Further, based on the knowledge graph technology, according to public sentiment data and full-scale industrial and commercial data of the enterprise group, a member graph of the enterprise group is constructed, and the method comprises the following steps:
acquiring public opinion data and full-scale industrial and commercial data of an enterprise group;
determining group members and member control relations in the enterprise group according to public opinion data and full industrial and commercial data;
and constructing a member map of the enterprise group according to the group members and the member control relationship.
Further, determining group members and member control relations in the enterprise group according to public opinion data and full industrial and commercial data, comprising:
determining the control relation between the leading enterprises and the members in the enterprise group according to the public opinion data and the full industrial and commercial data;
and (4) taking the leading enterprise as an initial node, and traversing the member control relationship downwards to obtain all group members of the enterprise group.
Further, determining the credit granting risk condition of the enterprise group according to the credit scale, wherein the method comprises the following steps:
according to the credit scale, scoring the enterprise group by using a preset credit granting risk scoring strategy to obtain a scoring result;
and determining the credit granting risk condition of the enterprise group based on the scoring result.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method for predicting an excessive trust risk as in the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which is characterized by storing a computer program, and when the computer program is executed by a processor, the method for predicting excessive credit risk according to the first aspect is implemented.
It is understood that the beneficial effects of the second to fourth aspects can be seen from the description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a method for predicting excessive trust risk according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an excessive trust risk prediction system provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a data platform according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a first determining module provided in an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a building block provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an application platform provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
As described in the related art, since some enterprises exist in the form of enterprise groups, the enterprise groups currently lack information technology means to effectively identify the potential enterprises that may be over-trusted, and further lack necessary identification means for the enterprise groups/clusters.
In order to solve the problems in the prior art, the application provides a method and a system for predicting risk of excessive credit granting based on a knowledge graph, by establishing a data platform, a computing platform and an application platform in the system for predicting risk of excessive credit granting, and by using the knowledge graph technology to comb the member relationship of an enterprise group through the data platform, the method and the system can perform total credit granting risk prediction on group members in the enterprise group through the computing platform and the application platform, avoid the situation of excessive credit granting caused by simultaneous credit granting to a plurality of members in the group, provide an effective identification means for the supervision of excessive risk credit granting of the group or the group, reduce the risk of a financial risk supervision system, and improve the stability of the financial risk supervision system.
Example one
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a method for predicting risk of excessive trust based on a knowledge graph according to an embodiment of the present application. The excessive credit risk prediction method described in the embodiments of the present application may be applied to electronic devices, including but not limited to computer devices such as smart phones, tablet computers, desktop computers, supercomputers, personal digital assistants, physical servers, and cloud servers. The excessive credit granting risk prediction method based on the knowledge graph comprises the following steps of S101 to S103, which are detailed as follows:
and S101, constructing a member map of the enterprise group according to public sentiment data and full-scale industrial and commercial data of the enterprise group based on a knowledge map technology.
In this embodiment, the public opinion data is data on various network platforms such as websites, blogs, microblogs, WeChats, and forums. The total business data is all business information of the enterprise, including enterprise name, enterprise operating range, enterprise shareholder, main personnel, information change records, branch organizations and the like.
Optionally, the big data is used for breaking through the traditional public opinion management, recording public opinion data in real time, comprehensively analyzing public opinion propagation dynamics, and checking corresponding data under corresponding conditions according to the classification, keywords and news properties of the public opinions. The data platform is used for combing the member relationship of the enterprise group by using a knowledge graph technology, so that the credit granting risk prediction can be carried out on the group members in the enterprise group, the condition that excessive credit granting is caused by the fact that a plurality of members in the group are granted credit at the same time is avoided, an effective identification means is provided for the excessive risk credit granting supervision of the group or the cluster, the risk of a financial risk supervision system is reduced, and the stability of the financial risk supervision system is improved.
In a possible implementation manner, based on the knowledge graph technology, the method for constructing the member graph of the enterprise group according to the public sentiment data and the full-scale industrial and commercial data of the enterprise group comprises the following steps: acquiring public opinion data and full-scale industrial and commercial data of an enterprise group; determining group members and member control relations in the enterprise group according to public opinion data and full industrial and commercial data; and constructing a member map of the enterprise group according to the group members and the member control relationship.
In the above implementation, public opinion data may be analyzed based on big data technology to get an enterprise, for example, news platform promulgates "subsidiary B of company a is listed today". The fact that the company A and the company B are members of an enterprise group or a class cluster can be analyzed, so that the company A and the company B can be obtained; determining the upstream and downstream control relationship of the enterprise based on the total industrial and commercial data of the enterprise, and tracing to obtain a group member list with member control relationship; and finally, constructing a member map of the enterprise group based on the member map.
Optionally, determining group members and member control relations in the enterprise group according to the public opinion data and the full industrial and commercial data, comprising: determining the control relation between the leading enterprises and the members in the enterprise group according to the public opinion data and the full industrial and commercial data; and (4) taking the leading enterprise as an initial node, and traversing the member control relationship downwards to obtain all group members of the enterprise group.
In this embodiment, the faucet business is a business that cannot be traced back to the stakeholder with a controlling corporate. When the faucet enterprise is "national resource committee", one or more enterprises invested by the "national resource committee" are respectively used as the faucet enterprises, that is, one or more faucet enterprises exist in a certain group. Specifically, the control relationship is traversed by taking the leading enterprise as an initial node, all the enterprises controlled by the leading enterprise and the enterprises controlled by key management personnel are divided into group members, meanwhile, the group members are not repeated, one enterprise exists and only exists in one group, the control relationship judgment standard is that the direct or indirect control ratio is over 50 percent (can be adjusted according to a threshold value), and finally, the enterprise members of three types including the leading enterprise, the core enterprise and the common enterprise can be obtained. The core enterprise means that the member enterprise with relatively high enterprise importance (the number of related enterprises) is the core member enterprise (the first 30%) in the group related structure. One or more core enterprises exist within a certain group. A typical enterprise refers to an enterprise within a group other than a lead member enterprise and a core member enterprise.
Optionally, constructing a member map of the enterprise group according to the group members and the member control relationship includes: and establishing a member map of the enterprise group by taking the group members in the enterprise group as data entities and taking the member control relationship as a data relationship.
In the embodiment, the knowledge map is a knowledge carrier represented by a graph data structure and describes objects of the objective world and the relationship of the objects, wherein the nodes represent the objects of the objective world and the edges represent the relationship between the objects. In this embodiment, group members in an enterprise group are used as data entities, and a member control relationship is a data relationship, so as to construct a member map of the enterprise group. Specifically, for every two enterprise members, the member control relationship is used as a connecting line to connect the two enterprise members with the control relationship. Further, control states (control or controlled) may also be fused into the member graph as data attributes, e.g., company a is controlled by company B, and the orientation of the connecting lines may be adjusted to that company B points to company a.
And step S102, calculating the credit scale of the enterprise group according to the member map and the credit data of each member in the member map.
In this embodiment, the credit data is the credit data reported by the financial institution, which may be credit amount, credit time, credit condition, etc. And calculating the credit scales of all the group members in the enterprise group based on the credit data, and accumulating the credit scales into the total credit scale of the enterprise group.
And step S103, determining the credit granting risk condition of the enterprise group according to the credit scale.
In this embodiment, the credit granting risks of the enterprise group can be scored and ranked based on credit scale by combining the conditions of macro economy, industry perspective, loan institution distribution and the like, and the credit granting risk condition of the enterprise group is determined based on the ranking result. For example, if the multi-head credit granting risk value of the group D is ranked at position 1, it is determined that the group D has an excessive credit granting risk.
In one implementation, determining the credit risk condition of the enterprise group according to the credit scale comprises the following steps: according to the credit scale, scoring the enterprise group by using a preset credit granting risk scoring strategy to obtain a scoring result; and determining the credit granting risk condition of the enterprise group based on the scoring result.
In the above implementation manner, the credit risk scoring policy may be a scoring manner in the industry corresponding to the enterprise. And based on the credit scale of each group member, scoring each group member by using the credit granting risk scoring strategy, and calculating the total credit granting risk score of the enterprise group according to the scoring results of all the group members.
Further, after determining the credit granting risk condition of the enterprise group according to the credit scale, the method further comprises the following steps: and sending the credit granting risk condition of the enterprise group to a preset terminal. In the embodiment, the group with the higher rank and the group multi-head credit granting risk is pushed to the preset terminal of each financial institution in the form of a report, so that each financial institution is prompted to perform corresponding risk prevention and control work, and the stability of risk prevention and control is improved.
Example two
In order to implement the corresponding method of the above embodiments to achieve the corresponding functions and technical effects, the following provides an excessive credit risk prediction system based on a knowledge graph. Referring to fig. 2, fig. 2 is a block diagram of a system for predicting risk of oversubscription according to an embodiment of the present application. The system in this embodiment includes platforms/modules/units for executing steps in the embodiment corresponding to fig. 1, and refer to fig. 1 and the related description in the embodiment corresponding to fig. 1 specifically. For convenience of explanation, only the part related to the embodiment is shown, and the system for predicting excessive trust risk based on the knowledge graph provided by the embodiment of the application comprises a data platform, a computing platform and an application platform;
the data platform 201 is used for constructing a member map of an enterprise group according to public opinion data and full-scale industrial and commercial data of the enterprise group based on a knowledge map technology;
the computing platform 202 is used for computing the credit scale of the enterprise group according to the member map and the credit data of each member in the member map;
and the application platform 203 is used for determining the credit granting risk condition of the enterprise group according to the credit scale.
In one embodiment, referring to fig. 3, a data platform 201, comprises:
an obtaining module 301, configured to obtain public opinion data and full business data of an enterprise group;
a first determining module 302, configured to determine group members and member control relationships in an enterprise group according to public opinion data and full industrial and commercial data;
the building module 303 is configured to build a member map of the enterprise group according to the group member and the member control relationship.
In one embodiment, referring to fig. 4, the first determining module 302 includes:
the determining unit 401 is configured to determine control relationships between leading enterprises and members in an enterprise group according to public opinion data and full-scale industrial and commercial data;
and a traversing unit 402, configured to traverse the member control relationship downwards with the lead enterprise as an initial node, to obtain all group members of the enterprise group.
In one embodiment, referring to FIG. 5, build module 303 includes:
the building unit 501 is configured to build a member graph of an enterprise group by using group members in the enterprise group as data entities and using a member control relationship as a data relationship.
In one embodiment, referring to fig. 6, the application platform 203 comprises:
the scoring module 604 is configured to score the enterprise group according to the credit scale by using a preset credit granting risk scoring policy to obtain a scoring result;
and a second determining module 602, configured to determine a credit risk condition of the enterprise group based on the scoring result.
In one embodiment, the application platform further comprises:
the pushing module 603 is configured to send the credit granting risk condition of the enterprise group to a preset terminal.
The excessive credit granting risk prediction system based on the knowledge graph can implement the excessive credit granting risk prediction method based on the knowledge graph in the first embodiment. The alternatives in the first embodiment are also applicable to the present embodiment, and are not described in detail here. The rest of the embodiments of the present application may refer to the contents of the first embodiment, and in this embodiment, details are not repeated.
EXAMPLE III
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic apparatus 7 of this embodiment includes: at least one processor 70 (only one shown in fig. 7), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, the processor 70 implementing the steps of any of the method embodiments described above when executing the computer program 72.
The electronic device 7 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a supercomputer, a personal digital assistant, a physical server, and a cloud server. The electronic device may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is merely an example of the electronic device 7, and does not constitute a limitation of the electronic device 7, and may include more or less components than those shown, or combine some of the components, or different components, such as an input-output device, a network access device, etc.
The Processor 70 may be a Central Processing Unit (CPU), and the Processor 70 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may in some embodiments be an internal storage unit of the electronic device 7, such as a hard disk or a memory of the electronic device 7. The memory 71 may also be an external storage device of the electronic device 7 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the electronic device 7. The memory 71 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
The embodiments of the present application provide a computer program product, which when running on an electronic device, enables the electronic device to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An excessive credit granting risk prediction system based on a knowledge graph is characterized by comprising a data platform, a computing platform and an application platform;
the data platform is used for constructing a member map of the enterprise group according to public opinion data and full industrial and commercial data of the enterprise group based on a knowledge map technology;
the computing platform is used for computing the credit scale of the enterprise group according to the member map and the credit data of each member in the member map;
and the application platform is used for determining the credit granting risk condition of the enterprise group according to the credit scale.
2. The system of claim 1, wherein the data platform comprises:
the acquisition module is used for acquiring public opinion data and full business data of an enterprise group;
the first determining module is used for determining group members and member control relations in the enterprise group according to the public opinion data and the full amount of industrial and commercial data;
and the construction module is used for constructing the member map of the enterprise group according to the group member and the member control relationship.
3. The system of claim 2, wherein the first determining module comprises:
the determining unit is used for determining the control relation between the leading enterprise and the members in the enterprise group according to the public opinion data and the full amount of industrial and commercial data;
and the traversal unit is used for traversing the member control relationship downwards by taking the leading enterprise as an initial node to obtain all group members of the enterprise group.
4. The oversubscription risk prediction system of claim 2, wherein said building module comprises:
and the construction unit is used for constructing the member map of the enterprise group by taking the group members in the enterprise group as data entities and taking the member control relationship as a data relationship.
5. The system according to claim 1, wherein the application platform comprises:
the scoring module is used for scoring the enterprise group by using a preset credit granting risk scoring strategy according to the credit scale to obtain a scoring result;
and the second determination module is used for determining the credit granting risk condition of the enterprise group based on the scoring result.
6. The system of claim 1, wherein the application platform further comprises:
and the pushing module is used for sending the credit granting risk condition of the enterprise group to a preset terminal.
7. A method for predicting excessive credit granting risk based on a knowledge graph is characterized by comprising the following steps:
based on knowledge graph technology, establishing a member graph of the enterprise group according to public sentiment data and full-scale industrial and commercial data of the enterprise group;
calculating the credit scale of the enterprise group according to the member map and the credit data of each member in the member map;
and determining the credit granting risk condition of the enterprise group according to the credit scale.
8. The method of predicting excessive credit risk according to claim 7, wherein the constructing a member map of the corporate based on the knowledge map technology according to public opinion data and total business data of the corporate comprises:
acquiring public opinion data and full-scale industrial and commercial data of an enterprise group;
determining group members and member control relations in the enterprise group according to the public opinion data and the full amount of industrial and commercial data;
and constructing a member map of the enterprise group according to the group members and the member control relationship.
9. The method of predicting excessive credit risk according to claim 8, wherein the determining of the corporate group membership and membership control relationships in the corporate group based on the public opinion data and the full business data comprises:
determining the control relation between the leading enterprises and the members in the enterprise group according to the public opinion data and the full amount of industrial and commercial data;
and traversing the member control relationship downwards by taking the leading enterprise as an initial node to obtain all group members of the enterprise group.
10. The method for predicting excessive credit risk according to claim 7, wherein the determining the credit risk condition of the business entity according to the credit scale comprises:
according to the credit scale, scoring the enterprise group by using a preset credit granting risk scoring strategy to obtain a scoring result;
and determining the credit granting risk condition of the enterprise group based on the scoring result.
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