CN111861281A - Knowledge graph-based risk employee discovery method and device - Google Patents

Knowledge graph-based risk employee discovery method and device Download PDF

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CN111861281A
CN111861281A CN202010776023.7A CN202010776023A CN111861281A CN 111861281 A CN111861281 A CN 111861281A CN 202010776023 A CN202010776023 A CN 202010776023A CN 111861281 A CN111861281 A CN 111861281A
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丁平
李帅
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Bank of China Ltd
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Abstract

The invention provides a knowledge graph-based risk employee discovery method and a knowledge graph-based risk employee discovery device, wherein the method comprises the following steps: constructing a risk knowledge graph, wherein the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities; determining the identity weight of each entity by querying an entity weight table based on the entity attribute; acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table; determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship; and determining the employee entity with the risk value larger than the preset threshold value as a risk employee. The invention can find out the risk staff in the bank with high accuracy.

Description

Knowledge graph-based risk employee discovery method and device
Technical Field
The invention relates to the technical field of risk early warning analysis, in particular to a method and a device for discovering risk staff based on a knowledge graph.
Background
Banking employees are always engaged in financial transaction related work. Some illegal employees are called risk employees by facilitating transactions such as drawing credit funds, credit card funds, and carrying out funds bridging. And the business administration judges whether the employee has violation behaviors manually or by technologies such as big data technology and machine learning according to the basic information of the internal employee. The method can discover the illegal behaviors of the customers to a certain degree, and can save the loss of the customers through the intra-row information warning or transaction blocking mode, but for some relatively complex transaction conditions such as nested transactions, associated transactions and the like, the basic information, transaction information and the like of the staff are only difficult to be mined.
Disclosure of Invention
The embodiment of the invention provides a knowledge graph-based method for discovering risky employees in a bank, which is used for discovering the risky employees in the bank and has high accuracy, and comprises the following steps:
constructing a risk knowledge graph, wherein the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities;
determining the identity weight of each entity by querying an entity weight table based on the entity attribute;
acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table;
determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship;
and determining the employee entity with the risk value larger than the preset threshold value as a risk employee.
The embodiment of the invention provides a knowledge graph-based risk employee discovery device, which is used for discovering risk employees in a bank and has high accuracy, and comprises the following components:
the system comprises a knowledge graph construction module, a risk knowledge graph analysis module and a risk knowledge graph analysis module, wherein the risk knowledge graph construction module is used for constructing a risk knowledge graph, the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities;
the identity weight determination module is used for determining the identity weight of each entity by inquiring the entity weight table based on the entity attribute;
the entity relationship risk value obtaining module is used for obtaining the risk value of the entity relationship corresponding to each employee entity by inquiring the risk relationship table;
the employee entity risk value determining module is used for determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship;
and the risk employee determining module is used for determining the employee entity with the risk value larger than the preset threshold value as a risk employee.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the above knowledge graph-based risk employee discovery method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the above knowledge-graph-based risk employee discovery method.
In the embodiment of the invention, a risk knowledge graph is constructed, wherein the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities; determining the identity weight of each entity by querying an entity weight table based on the entity attribute; acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table; determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship; and determining the employee entity with the risk value larger than the preset threshold value as a risk employee. In the above embodiment, the process of determining the risky employee not only considers the attribute information of the employee, but also sufficiently digs the entity and entity relationship related to each employee by constructing the risk knowledge graph, so as to determine the risk value of each employee entity, and thus, the determined risky employee is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method for discovering risky employees based on a knowledge graph according to an embodiment of the present invention;
FIG. 2 is a schematic representation of a risk profile in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a knowledge-graph-based apparatus for discovering riskers according to an embodiment of the present invention;
FIG. 4 is another schematic diagram of a knowledge-graph based risker discovery apparatus according to an embodiment of the present invention;
FIG. 5 is a diagram of a computer device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are used in an open-ended fashion, i.e., to mean including, but not limited to. Reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is for illustrative purposes to illustrate the implementation of the present application, and the sequence of steps is not limited and can be adjusted as needed.
Fig. 1 is a flowchart of a method for discovering risky employees based on a knowledge graph in an embodiment of the present invention, as shown in fig. 1, the method includes:
step 101, constructing a risk knowledge graph, wherein the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities;
102, determining the identity weight of each entity by inquiring an entity weight table based on the entity attribute;
103, acquiring a risk value of an entity relationship corresponding to each employee entity by inquiring a risk relationship table;
104, determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship;
and 105, determining the employee entity with the risk value larger than the preset threshold value as a risk employee.
In the embodiment of the invention, the process of determining the risk employees not only considers the attribute information of the employees, but also fully digs the entity and entity relationship related to each employee by constructing the risk knowledge graph, thereby determining the risk value of each employee entity and ensuring that the determined risk employees are more accurate.
In a specific implementation, the entity attribute may be obtained from different systems and different data sources, and includes a teller management system, an employee management system, various data platforms related to banking services, and the like. For example, Zhang three, age 33, and row length. Establish entity < (name, Zhang III), (age, 33), (duty, length) >.
In one embodiment, the entity relationship comprises: at least one of a personal relationship, a person-to-institution relationship, and a transaction relationship.
In the above embodiment, the human-affair relationship includes a superior-subordinate relationship, a colleague relationship, a member-client relationship, and the like, the human-institution relationship includes YY employees in the XX division, the transaction relationship includes a transaction relationship between employees, a transaction relationship between employees and clients, a bank-certificate transfer relationship, and the like, for example, 50 yuan is transferred between employee zhang and employee lie si, and a transfer relationship between zhang and lie si can be constructed, and fig. 2 is a schematic diagram of a risk knowledge graph in the embodiment of the present invention.
In step 102, the identity weight of each entity is determined by querying the entity weight table based on the entity attributes, where table 1 is an example of the entity weight table, and the identity weight of each entity can be conveniently obtained by querying the entity weight table for determining the employee entity risk value.
TABLE 1
Entity Attribute information Identity weight
Zhang three Age 33, division of the title 0.5
Li four Age 22, staff member 0.2
XX is divided into rows The number of the staff is 100 0.7
In step 103, the risk value of the entity relationship corresponding to each employee entity is obtained by querying the risk relationship table, and in the specific implementation, the query risk relationship table is determined by the security department through formulation rules. For example, a risk value may be determined when a teller has a transfer relationship with a leader, the teller has received XX branch transfers at office hours, a clerk has received a large amount of money transfer from a customer, and the clerk has transferred money from a personal account of the clerk to a public institution, and the risk value of the entity relationship in the risk relationship table is determined according to the attribute information of the entity and the entity relationship, and table 2 is an example of a risk relationship table.
TABLE 2
Figure BDA0002618438100000041
Figure BDA0002618438100000051
In an embodiment, the following formula is adopted to determine the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship:
Figure BDA0002618438100000052
wherein, RV (A)i) For employee entity AiA risk value of (d);
Rjfor employee entity AiA risk value of a corresponding jth entity relationship;
wjfor employee entity AiAnd the identity weight of the entity at the other end of the corresponding jth entity relationship.
According to the method, the risk staff needing to be determined only need to search the staff entities, and the risk value of each staff entity can be determined rapidly and accurately through the formula, so that the staff entities with the risk values larger than the preset threshold value are the risk staff, the incidence relation of the risk staff is analyzed, the behavior of the risk staff is determined, and the staff entities smaller than the preset threshold value are not processed.
In an embodiment, the method further comprises:
and generating early warning information based on the attribute information of the risk staff and the corresponding entity relationship.
The generated early warning information can be sent to a safety management department to carry out investigation. In specific implementation, the risk value of the employee entity can be updated at intervals, for example, every day, employees with higher risks can be automatically found, and an early warning prompt is given, so that whether violation behaviors exist or not is analyzed and determined, and thus the occurrence of illegal transactions is blocked.
In summary, in the method provided in the embodiment of the present invention, a risk knowledge graph is constructed, where the risk knowledge graph includes entities, entity attributes, and entity relationships, and the entities include a banking institution entity, an employee entity, and a customer entity; determining the identity weight of each entity by querying an entity weight table based on the entity attribute; acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table; determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship; and determining the employee entity with the risk value larger than the preset threshold value as a risk employee. In the above embodiment, the process of determining the risky employee not only considers the attribute information of the employee, but also sufficiently digs the entity and entity relationship related to each employee by constructing the risk knowledge graph, so as to determine the risk value of each employee entity, and thus, the determined risky employee is more accurate.
The embodiment of the invention also provides a knowledge graph-based risk employee discovery device, the principle of which is similar to that of a knowledge graph-based risk employee discovery method, and details are not repeated here.
Fig. 3 is a schematic diagram of a knowledge graph-based apparatus for discovering riskers according to an embodiment of the present invention, as shown in fig. 3, the apparatus includes:
the knowledge graph building module 301 is configured to build a risk knowledge graph, where the risk knowledge graph includes entities, entity attributes, and entity relationships, and the entities include banking institution entities, employee entities, and customer entities;
an identity weight determination module 302, configured to determine an identity weight of each entity by querying an entity weight table based on an entity attribute;
an entity relationship risk value obtaining module 303, configured to obtain a risk value of an entity relationship corresponding to each employee entity by querying the risk relationship table;
an employee entity risk value determination module 304, configured to determine a risk value of each employee entity according to a risk value of an entity relationship corresponding to each employee entity and an identity weight of an entity at the other end of the corresponding entity relationship;
and the risk employee determination module 305 is configured to determine that the employee entity with the risk value greater than the preset threshold is a risk employee.
In one embodiment, the entity attributes include at least one of personal information, account information, transaction information, behavioral information, and administrative information.
In one embodiment, the entity relationship comprises: at least one of a personal relationship, a person-to-institution relationship, and a transaction relationship.
In an embodiment, the employee entity risk value determination module 304 is specifically configured to:
determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship by adopting the following formula:
Figure BDA0002618438100000061
wherein, RV (A)i) For employee entity AiA risk value of (d);
Rjfor employee entity AiA risk value of a corresponding jth entity relationship;
wjfor employee entity AiAnd the identity weight of the entity at the other end of the corresponding jth entity relationship.
In an embodiment, fig. 4 is another schematic diagram of an apparatus for discovering a risky employee based on a knowledge graph according to an embodiment of the present invention, and it can be seen that the apparatus further includes an early warning information generating module 401, configured to:
and generating early warning information based on the attribute information of the risk staff and the corresponding entity relationship.
In summary, in the apparatus provided in the embodiment of the present invention, a risk knowledge graph is constructed, where the risk knowledge graph includes entities, entity attributes, and entity relationships, and the entities include a banking institution entity, an employee entity, and a customer entity; determining the identity weight of each entity by querying an entity weight table based on the entity attribute; acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table; determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship; and determining the employee entity with the risk value larger than the preset threshold value as a risk employee. In the above embodiment, the process of determining the risky employee not only considers the attribute information of the employee, but also sufficiently digs the entity and entity relationship related to each employee by constructing the risk knowledge graph, so as to determine the risk value of each employee entity, and thus, the determined risky employee is more accurate.
An embodiment of the present application further provides a computer device, fig. 5 is a schematic diagram of the computer device in the embodiment of the present invention, where the computer device is capable of implementing all steps in the method for discovering a risky employee based on a knowledge graph in the embodiment, and the electronic device specifically includes the following contents:
a processor (processor)501, a memory (memory)502, a communication interface (communications interface)503, and a bus 504;
the processor 501, the memory 502 and the communication interface 503 complete mutual communication through the bus 504; the communication interface 503 is used for implementing information transmission between related devices such as server-side devices, detection devices, and user-side devices;
the processor 501 is configured to call the computer program in the memory 502, and when the processor executes the computer program, the processor implements all the steps of the method for discovering a risk employee based on a knowledge graph in the above embodiments.
Embodiments of the present application further provide a computer-readable storage medium, which is capable of implementing all steps of the method for discovering a risk employee based on a knowledge graph in the foregoing embodiments, and the computer-readable storage medium stores a computer program, which when executed by a processor implements all steps of the method for discovering a risk employee based on a knowledge graph in the foregoing embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (12)

1. A knowledge graph-based risk employee discovery method is characterized by comprising the following steps:
constructing a risk knowledge graph, wherein the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities;
determining the identity weight of each entity by querying an entity weight table based on the entity attribute;
acquiring a risk value of an entity relation corresponding to each employee entity by inquiring a risk relation table;
determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship;
and determining the employee entity with the risk value larger than the preset threshold value as a risk employee.
2. A knowledge-graph based risky employee discovery method according to claim 1 wherein said entity attributes comprise at least one of personal information, account information, transaction information, behavioral information and administrative information.
3. A knowledge-graph-based risky employee discovery method according to claim 1 wherein said entity relationships comprise: at least one of a personal relationship, a person-to-institution relationship, and a transaction relationship.
4. The knowledge-graph-based risky employee discovery method of claim 1, wherein the risk value of each employee entity is determined according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship using the following formula:
Figure FDA0002618438090000011
wherein, RV (A)i) For employee entity AiA risk value of (d);
Rjfor employee entity AiA risk value of a corresponding jth entity relationship;
wjfor employee entity AiAnd the identity weight of the entity at the other end of the corresponding jth entity relationship.
5. The knowledge-graph-based risky employee discovery method of claim 1, further comprising:
and generating early warning information based on the attribute information of the risk staff and the corresponding entity relationship.
6. A knowledge-graph-based risker discovery apparatus, comprising:
the system comprises a knowledge graph construction module, a risk knowledge graph analysis module and a risk knowledge graph analysis module, wherein the risk knowledge graph construction module is used for constructing a risk knowledge graph, the risk knowledge graph comprises entities, entity attributes and entity relations, and the entities comprise bank institution entities, employee entities and client entities;
the identity weight determination module is used for determining the identity weight of each entity by inquiring the entity weight table based on the entity attribute;
the entity relationship risk value obtaining module is used for obtaining the risk value of the entity relationship corresponding to each employee entity by inquiring the risk relationship table;
the employee entity risk value determining module is used for determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship;
and the risk employee determining module is used for determining the employee entity with the risk value larger than the preset threshold value as a risk employee.
7. The knowledgegraph-based risker discovery apparatus of claim 6, wherein said entity attributes comprise at least one of personal information, account information, transaction information, behavioral information and administrative information.
8. The knowledge-graph-based risky employee discovery apparatus of claim 6 wherein said entity relationships comprise: at least one of a personal relationship, a person-to-institution relationship, and a transaction relationship.
9. The knowledge-graph-based risky employee discovery apparatus of claim 6, wherein the employee entity risk value determination module is specifically configured to:
determining the risk value of each employee entity according to the risk value of the entity relationship corresponding to each employee entity and the identity weight of the entity at the other end of the corresponding entity relationship by adopting the following formula:
Figure FDA0002618438090000021
wherein, RV (A)i) For employee entity AiA risk value of (d);
Rjfor employee entity AiA risk value of a corresponding jth entity relationship;
wjfor employee entity AiAnd the identity weight of the entity at the other end of the corresponding jth entity relationship.
10. The knowledge-graph-based risky employee discovery apparatus of claim 6, further comprising an early warning information generation module for:
and generating early warning information based on the attribute information of the risk staff and the corresponding entity relationship.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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CN109670937A (en) * 2018-09-26 2019-04-23 平安科技（深圳）有限公司 Risk subscribers recognition methods, user equipment, storage medium and device
CN109508825A (en) * 2018-11-12 2019-03-22 平安科技（深圳）有限公司 Employee's image method for prewarning risk and relevant apparatus
CN110503236A (en) * 2019-07-08 2019-11-26 中国平安人寿保险股份有限公司 Risk Forecast Method, device, equipment and the storage medium of knowledge based map
CN110414806A (en) * 2019-07-10 2019-11-05 平安科技（深圳）有限公司 Employee's method for prewarning risk and relevant apparatus
CN111309824A (en) * 2020-02-18 2020-06-19 中国工商银行股份有限公司 Entity relationship map display method and system
CN111429255A (en) * 2020-03-19 2020-07-17 中国建设银行股份有限公司 Risk assessment method, device, equipment and storage medium
CN111402064A (en) * 2020-06-03 2020-07-10 天云融创数据科技（北京）有限公司 Risk value evaluation method and device

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CN112581283A (en) * 2020-12-28 2021-03-30 中国建设银行股份有限公司 Method and device for analyzing and alarming business bank employee transaction behaviors
CN115099664A (en) * 2022-07-08 2022-09-23 中国银行股份有限公司 Method and device for controlling internal risk of bank

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