CN112184012A - Enterprise risk early warning method, device, equipment and readable storage medium - Google Patents
Enterprise risk early warning method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN112184012A CN112184012A CN202011036277.1A CN202011036277A CN112184012A CN 112184012 A CN112184012 A CN 112184012A CN 202011036277 A CN202011036277 A CN 202011036277A CN 112184012 A CN112184012 A CN 112184012A
- Authority
- CN
- China
- Prior art keywords
- enterprise
- node
- risk
- early warning
- association
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012502 risk assessment Methods 0.000 claims abstract description 60
- 238000011156 evaluation Methods 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000010801 machine learning Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 7
- 230000007246 mechanism Effects 0.000 description 6
- 230000008901 benefit Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000012954 risk control Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- General Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Animal Behavior & Ethology (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an enterprise risk early warning method, an enterprise risk early warning device, enterprise risk early warning equipment and a readable storage medium, wherein the method comprises the following steps: receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects; drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects; calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map; judging whether the risk assessment value of the entity object is larger than a preset threshold value or not, if so, sending a risk early warning message to a specified terminal; the enterprise risk early warning method can accurately carry out enterprise risk early warning; the invention is suitable for the field of financial science and technology, and also relates to a block chain technology.
Description
Technical Field
The invention relates to the technical field of intelligent decision, in particular to an enterprise risk early warning method, device, equipment and readable storage medium.
Background
In recent years, domestic enterprise debts begin to break the law of just closing, default events occur, and the default quantity shows a growing trend. Therefore, risk control in the bond market and early warning of bond-issuing main bodies with credit risk are becoming more and more important to avoid major loss of investors. The existing mode for judging whether the enterprise has default risks is that a wind control worker judges the risks by utilizing expert experience and inference logic thereof based on various announcement information of the enterprise, and due to the limitations of self experience and view coverage of different professionals, all cases are difficult to cover, and omission is easy to generate; as machine learning algorithms continue to evolve, machine learning algorithms are increasingly being applied to various domains to address, for example: problems with data prediction, data classification, and data clustering; however, applying the machine learning algorithm to the field of enterprise default risk judgment still has the following technical problems: 1) how to process various bulletin information of the enterprise into information usable by the machine learning algorithm, and 2) how to improve the efficiency of the machine learning algorithm in utilizing various bulletin information of the enterprise.
Disclosure of Invention
The invention aims to provide an enterprise risk early warning method, device, equipment and storage medium, which can determine risk influence relation among enterprises, so that enterprise risk early warning is more accurately performed.
According to one aspect of the invention, an enterprise risk early warning method is provided, and the method comprises the following steps:
receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and judging whether the risk assessment value of the entity object is larger than a preset threshold value, if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
Optionally, the drawing an enterprise association map according to the information of the plurality of entity objects and the association relationship information between the entity objects includes:
drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
Optionally, the calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association graph includes:
aiming at a target node, determining a related node connected with the target node through an edge in the enterprise related graph;
respectively calculating a risk evaluation value of each associated node, and respectively calculating an attention weight value of each associated node to the target node;
and calculating the risk evaluation value of the target node according to the risk evaluation value of each associated node, the attention weight value of each associated node to the target node and the potential risk value in the node attribute of the target node.
Optionally, the calculating the attention weight value of each associated node to the target node respectively includes:
calculating the attention weight value W of the associated node b to the target node a according to the following formulaba：
Wba＝softmax(Qa×Rab)；
Wherein Q isaA potential risk value in the node attribute of the target node a;
Rabthe association vector in the edge attribute is the edge between the target node a and the associated node b.
Optionally, the calculating the risk assessment value of the target node according to the risk assessment value of each associated node, the attention weight value of each associated node to the target node, and the potential risk value in the node attribute of the target node includes:
calculating the conduction risk value V of the associated node to the target node a according to the following formulaa-：
Va-＝f(Wba×Vb+Wca×Vc+…)；
Wherein f () is a sigmoid function;
Vbrisk assessment value, V, for associated node bcA risk assessment value for the associated node c;
Wbaattention weight value, W, for associated node b to target node acaThe attention weight value of the associated node c to the target node a is set;
calculating the risk assessment value V of the target node a according to the following formulaa+：
Va+＝Va-+Va；
Wherein, VaAccording to the potential risk value in the node attribute of the target node a.
Optionally, the risk assessment model is a graph attention mechanism neural network GAT model.
In order to achieve the above object, the present invention further provides an enterprise risk early warning device, including:
the receiving module is used for receiving the early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
the drawing module is used for drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects so as to form the enterprise association map; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
the calculation module is used for calculating the risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and the early warning module is used for judging whether the risk assessment value of the entity object is greater than a preset threshold value, and if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
Optionally, the drawing module is configured to:
drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
In order to achieve the above object, the present invention further provides a computer device, which specifically includes: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the enterprise risk early warning method introduced above.
In order to achieve the above object, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the enterprise risk early warning method introduced above.
According to the enterprise risk early warning method, the enterprise risk early warning device, the enterprise risk early warning equipment and the storage medium, the announcement information of each enterprise and the incidence relation between the enterprises are converted into the enterprise incidence map, and the information input into the machine learning model is extracted from the enterprise incidence map, so that whether the enterprise has default risks or not is judged by using the machine learning model; in the invention, the influence of the associated enterprises on the target enterprises is comprehensively considered instead of only utilizing the information of the target enterprises, so that whether the target enterprises have default risks or not is judged more comprehensively and accurately. In addition, according to the invention, the announcement information of each enterprise and the association relation between the enterprises are converted into the enterprise association map, so that the technical problem that the machine learning model cannot utilize the announcement information of the enterprises is solved, and meanwhile, the efficiency of the machine learning model for risk assessment of the enterprises is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is an optional flowchart of an enterprise risk early warning method according to an embodiment;
fig. 2 is a schematic diagram of an optional component structure of the enterprise risk early warning device provided in the second embodiment;
fig. 3 is a schematic diagram of an alternative hardware architecture of the computer device according to the third embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment of the invention provides an enterprise risk early warning method, which specifically comprises the following steps of:
step S101: receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and association relation information between the entity objects.
Specifically, the entity object may be: company, institution, natural person; the association includes the following types: stock control, mortgage, guarantee, actual control people, legal people, business communication parties.
The information of the entity objects and the incidence relation information among the entity objects are notice information stored in the block link points; uploading the information of the entity objects and the incidence relation information among the entity objects to the block chain can ensure the safety and the fair transparency to users.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Step S102: drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: nodes characterizing entity objects and edges characterizing associations between entity objects.
Specifically, step S102 includes:
step A1: drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
step A2: drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
In this embodiment, the enterprise association map can be effectively represented by the information of the enterprise itself, the information of the associated party thereof, and the specific association relationship between the information of the enterprise and the associated party; the specific process for constructing the enterprise association map comprises the following steps: on the basis of the enterprise information extracted from various announcements and relationship data among enterprises, important entity objects such as companies, organizations, natural persons and the like are abstracted into nodes in an enterprise association map, corresponding state information is expressed in the form of node attributes, various association relations (such as stock control, mortgage, guarantee and the like) among companies, companies and organizations, companies and personnel are abstracted into edges among the nodes, and the state information of specific association relations is expressed in the form of edge attributes.
It should be noted that, the expert sets corresponding potential risk values for the entity objects in advance according to the basic information, the management information, the loan information and the like of each entity object; the potential risk value is used for representing the current default risk probability of the entity object; the greater the potential risk value for an entity object, the greater the probability that the entity object has a breach risk. In addition, since there may be one or more types of association between each entity object, in order to be able to represent the multiple types of association between each entity object by one enterprise association map, an association vector is adopted in this embodiment; the value of each dimension of the initial association vector is 0, and each dimension corresponds to one type of association relationship. If the two entity objects have the association relationship of two types of stock control and business communication, setting the value of the dimension representing the stock control and the service communication in the association vector as 1.
In the embodiment, the relationship edges are expressed by using the association vector in the form of the vector innovatively, and the form of the multilateral and directed Graph is effectively converted into the form of a single edge, so that the established enterprise association Graph is more suitable for a GAT (Graph Attention Network) learning model framework based on a single-edge structure, the special design of a complex GAT learning model framework based on the multilateral directed Graph structure is avoided, and the overall GAT learning model framework is more simplified.
Step S103: and calculating the risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map.
Specifically, the risk assessment model is a neural network of interest (GAT) model; if the risk assessment value of an entity object is larger, the probability that the entity object will have credit risk events in a future period of time is larger.
The GAT is a graph neural network model (GNN) based on a graph attention machine mechanism, has the end-to-end learning characteristic of a neural network, and reduces the complicated feature engineering work of excavating the conduction path factors. The graph attention machine neural network GAT model comprises: a plurality of GAT layers and a full link layer, and in each GAT layer: an attention layer and a residual network layer. The node attribute of each node and the edge attribute of each edge are input into the graph attention neural network GAT model, and the output of the graph attention neural network GAT model is the risk assessment value of each node. Furthermore, the graph attention mechanism neural network GAT model is a model that is trained in advance from a historical sample set.
Further, step S103 includes:
step B1: aiming at a target node, determining a related node connected with the target node through an edge in the enterprise related graph;
step B2: respectively calculating a risk evaluation value of each associated node, and respectively calculating an attention weight value of each associated node to the target node;
step B3: and calculating the risk evaluation value of the target node according to the risk evaluation value of each associated node, the attention weight value of each associated node to the target node and the potential risk value in the node attribute of the target node.
It should be noted that the risk assessment value of each associated node is calculated according to the above steps B1 to B3, that is, the above steps B1 to B3 are performed with one associated node as a target node to obtain the risk assessment value of the associated node. And if one target node has no associated node, taking the potential risk value of the target node as the risk assessment value of the target node.
Further, step B2 includes:
calculating the attention weight value W of the associated node b to the target node a according to the following formulaba：
Wba＝softmax(Qa×Rab)；
Wherein Q isaA potential risk value in the node attribute of the target node a;
Rabthe association vector in the edge attribute is the edge between the target node a and the associated node b.
Further, step B3 includes:
step B31: calculating the conduction risk value V of the associated node to the target node a according to the following formulaa-：
Va-＝f(Wba×Vb+Wca×Vc+…)；
Wherein f () is a sigmoid function;
Vbrisk assessment value, V, for associated node bcA risk assessment value for the associated node c;
Wbaattention weight value, W, for associated node b to target node acaThe attention weight value of the associated node c to the target node a is set;
step B32: calculating the risk assessment value V of the target node a according to the following formulaa+：
Va+＝Va-+Va；
Wherein, VaAccording to the potential risk value in the node attribute of the target node a.
The GAT automatically learns the influence degree of the associated nodes on the target node through an attention mechanism, namely the importance of different corresponding association relations of each enterprise can be learned. In the learning process, the state information of the enterprise is an important characteristic factor. Namely, the comprehensive interaction of the self state of the enterprise and the related party is learned at the same time. In reality, the probability of risk propagation is not only related to the related party, but also highly related to the risk resistance of the GAT, so that the GAT has more accurate advantages compared with the learning algorithm only paying attention to the importance of the relationship.
In addition, the risk assessment value of each entity object can be uploaded into a block chain to prevent the entity objects from being tampered; other users may download risk assessment values for each physical object from the blockchain.
Step S104: and judging whether the risk assessment value of the entity object is larger than a preset threshold value, if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
Because the relationship abstraction degree of the risk conduction probability among enterprises and the enterprise association relationship is high, the types of the relationships among the enterprises are more, the difference among different relationships is large, the combination enhancement effect also exists among different relationships and the like, the existing model has poor generalization performance and cannot obtain good effect. In the embodiment, a new GAT-based credit risk conduction model between associated enterprises is provided, different attention weights of different relationship types can be effectively and automatically learned, meanwhile, the influence factors of the states of the enterprises are learned while the relationship importance is learned, namely, the comprehensive characteristics of the associated parties and the enterprises are learned simultaneously, and the display condition is simulated more comprehensively. In addition, the embodiment also innovatively adopts a vector form to express the relation edges, effectively converts the form of the multi-edge and directed graph into a single-edge form, and avoids specially designing a complex GAT structure based on the multi-edge directed graph, so that the GAT structure based on the single-edge learning can be applied through simple modification. Compared with wind control service personnel, as the model is automatically operated based on a full data set, the global coverage can be effectively achieved, and the efficiency is improved.
Example two
The embodiment of the invention provides an enterprise risk early warning device, which specifically comprises the following components as shown in fig. 2:
a receiving module 201, configured to receive an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
the drawing module 202 is configured to draw an enterprise association map according to the information of the plurality of entity objects and association relationship information between the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
the calculation module 203 is configured to calculate a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
the early warning module 204 is configured to determine whether the risk assessment value of the entity object is greater than a preset threshold, and if so, send a risk early warning message to a designated terminal corresponding to the entity object.
Specifically, the drawing module 202 is configured to:
drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
Further, the calculating module 203 specifically includes:
the determining unit is used for determining an associated node which is connected with a target node through an edge in the enterprise associated map aiming at the target node;
the calculation unit is used for calculating the risk assessment value of each associated node and calculating the attention weight value of each associated node on the target node;
and the processing unit is used for calculating the risk evaluation value of the target node according to the risk evaluation value of each associated node, the attention weight value of each associated node to the target node and the potential risk value in the node attribute of the target node.
Further, the computing unit is specifically configured to:
calculating the attention weight value W of the associated node b to the target node a according to the following formulaba：
Wba＝softmax(Qa×Rab)；
Wherein Q isaA potential risk value in the node attribute of the target node a;
Rabthe association vector in the edge attribute is the edge between the target node a and the associated node b.
Further, the processing unit is configured to:
calculating the conduction risk value V of the associated node to the target node a according to the following formulaa-：
Va-＝f(Wba×Vb+Wca×Vc+…)；
Wherein f () is a sigmoid function;
Vbrisk assessment value, V, for associated node bcA risk assessment value for the associated node c;
Wbaattention weight value, W, for associated node b to target node acaThe attention weight value of the associated node c to the target node a is set;
calculating the risk assessment value of the target node a according to the following formulaVa+：
Va+＝Va-+Va；
Wherein, VaAccording to the potential risk value in the node attribute of the target node a.
Further, the risk assessment model is a graph attention mechanism neural network GAT model.
EXAMPLE III
The embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. As shown in fig. 3, the computer device 30 of the present embodiment includes at least but is not limited to: a memory 301, a processor 302 communicatively coupled to each other via a system bus. It is noted that FIG. 3 only shows the computer device 30 having components 301 and 302, but it is understood that not all of the shown components are required and that more or fewer components may be implemented instead.
In this embodiment, the memory 301 (i.e., the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 301 may be an internal storage unit of the computer device 30, such as a hard disk or a memory of the computer device 30. In other embodiments, the memory 301 may also be an external storage device of the computer device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 30. Of course, the memory 301 may also include both internal and external storage devices for the computer device 30. In the present embodiment, the memory 301 is generally used for storing an operating system and various types of application software installed in the computer device 30. In addition, the memory 301 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 302 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 302 generally serves to control the overall operation of the computer device 30.
Specifically, in this embodiment, the processor 302 is configured to execute a program of an enterprise risk early warning method stored in the processor 302, and when executed, the program of the enterprise risk early warning method implements the following steps:
receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and judging whether the risk assessment value of the entity object is larger than a preset threshold value, if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
The specific embodiment process of the above method steps can be referred to in the first embodiment, and the detailed description of this embodiment is not repeated here.
Example four
The present embodiments also provide a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., having stored thereon a computer program that when executed by a processor implements the method steps of:
receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and judging whether the risk assessment value of the entity object is larger than a preset threshold value, if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
The specific embodiment process of the above method steps can be referred to in the first embodiment, and the detailed description of this embodiment is not repeated here.
It should be noted that, in this document, 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 like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An enterprise risk early warning method is characterized by comprising the following steps:
receiving an early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and judging whether the risk assessment value of the entity object is larger than a preset threshold value, if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
2. The enterprise risk early warning method according to claim 1, wherein the drawing an enterprise association graph according to the information of the plurality of entity objects and the association relationship information among the entity objects comprises:
drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
3. The enterprise risk early warning method according to claim 2, wherein the calculating a risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association graph comprises:
aiming at a target node, determining a related node connected with the target node through an edge in the enterprise related graph;
respectively calculating a risk evaluation value of each associated node, and respectively calculating an attention weight value of each associated node to the target node;
and calculating the risk evaluation value of the target node according to the risk evaluation value of each associated node, the attention weight value of each associated node to the target node and the potential risk value in the node attribute of the target node.
4. The enterprise risk early warning method according to claim 3, wherein the calculating the attention weight value of each associated node to the target node comprises:
calculating the attention weight value W of the associated node b to the target node a according to the following formulaba：
Wba＝softmax(Qa×Rab)；
Wherein Q isaA potential risk value in the node attribute of the target node a;
Rabthe association vector in the edge attribute is the edge between the target node a and the associated node b.
5. The enterprise risk early warning method according to claim 3, wherein the calculating the risk assessment value of the target node according to the risk assessment value of each associated node, the attention weight value of each associated node to the target node, and the potential risk value in the node attribute of the target node comprises:
calculating the conduction risk value V of the associated node to the target node a according to the following formulaa-：
Va-＝f(Wba×Vb+Wca×Vc+…)；
Wherein f () is a sigmoid function;
Vbrisk assessment value, V, for associated node bcA risk assessment value for the associated node c;
Wbaattention weight value, W, for associated node b to target node acaThe attention weight value of the associated node c to the target node a is set;
calculating the risk assessment value V of the target node a according to the following formulaa+：
Va+＝Va-+Va；
Wherein, VaAccording to the potential risk value in the node attribute of the target node a.
6. The enterprise risk early warning method according to claim 1, wherein the risk assessment model is a neural network of interest (GAT) model.
7. An enterprise risk early warning device, the device comprising:
the receiving module is used for receiving the early warning instruction; wherein the early warning instruction comprises: information of a plurality of entity objects and incidence relation information among the entity objects;
the drawing module is used for drawing an enterprise association map according to the information of the entity objects and the association relation information among the entity objects; wherein the enterprise association graph comprises: the nodes representing the entity objects and the edges representing the incidence relation between the entity objects;
the calculation module is used for calculating the risk assessment value of each entity object by using a preset risk assessment model based on the enterprise association map;
and the early warning module is used for judging whether the risk assessment value of the entity object is greater than a preset threshold value, and if so, sending a risk early warning message to a designated terminal corresponding to the entity object.
8. The enterprise risk early warning device of claim 7, wherein the mapping module is configured to:
drawing nodes representing all the entity objects, and setting node attributes for the nodes of all the entity objects; wherein the node attributes include: a preset potential risk value;
drawing edges among all nodes according to the association relation information among all the entity objects, and setting edge attributes for each edge to form an enterprise association graph; wherein the edge attribute comprises: and the association vector is used for representing the association relationship type, the association vector is a vector with 1 multiplied by N dimensions, and N is the number of the types of the association relationship type.
9. A computer device, the computer device comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011036277.1A CN112184012B (en) | 2020-09-27 | Enterprise risk early warning method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011036277.1A CN112184012B (en) | 2020-09-27 | Enterprise risk early warning method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112184012A true CN112184012A (en) | 2021-01-05 |
| CN112184012B CN112184012B (en) | 2024-05-31 |
Family
ID=
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112150013A (en) * | 2020-09-27 | 2020-12-29 | 平安资产管理有限责任公司 | Enterprise risk early warning method, device, equipment and readable storage medium |
| CN112965872A (en) * | 2021-02-24 | 2021-06-15 | 刘志欣 | Risk prediction method and device for system cluster nodes, terminal and storage medium |
| CN113094506A (en) * | 2021-04-14 | 2021-07-09 | 每日互动股份有限公司 | Early warning method based on relation map, computer equipment and storage medium |
| CN113379212A (en) * | 2021-05-31 | 2021-09-10 | 交通运输信息安全中心有限公司 | Block chain-based logistics information platform default risk assessment method, device, equipment and medium |
| CN113379211A (en) * | 2021-05-31 | 2021-09-10 | 交通运输信息安全中心有限公司 | Block chain-based logistics information platform default risk management and control system and method |
| CN113642926A (en) * | 2021-08-31 | 2021-11-12 | 上海明略人工智能(集团)有限公司 | Method and device for risk early warning, electronic equipment and storage medium |
| CN113657991A (en) * | 2021-08-12 | 2021-11-16 | 东方微银科技股份有限公司 | Credit risk early warning method, system and storage medium based on graph rule engine |
| CN116187767A (en) * | 2023-04-26 | 2023-05-30 | 北京网智天元大数据科技有限公司 | Enterprise risk conduction prediction method and system based on graph attention mechanism |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040054563A1 (en) * | 2002-09-17 | 2004-03-18 | Douglas William J. | Method for managing enterprise risk |
| US20060100957A1 (en) * | 2002-05-08 | 2006-05-11 | Accenture Global Services Gmbh | Electronic data processing system and method of using an electronic data processing system for automatically determining a risk indicator value |
| CN109559192A (en) * | 2018-10-25 | 2019-04-02 | 深圳壹账通智能科技有限公司 | Risk checking method, device, equipment and storage medium based on association map |
| CN109657917A (en) * | 2018-11-19 | 2019-04-19 | 平安科技（深圳）有限公司 | Assess method for prewarning risk, device, computer equipment and the storage medium of object |
| CN109657918A (en) * | 2018-11-19 | 2019-04-19 | 平安科技（深圳）有限公司 | Method for prewarning risk, device and the computer equipment of association assessment object |
| CN109858740A (en) * | 2018-12-21 | 2019-06-07 | 中化资本有限公司 | Appraisal procedure, device, computer equipment and the storage medium of business risk |
| CN110163413A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Enterprise supervision and method for early warning, device, computer equipment and readable storage medium storing program for executing |
| CN110390465A (en) * | 2019-06-18 | 2019-10-29 | 深圳壹账通智能科技有限公司 | Air control analysis and processing method, device and the computer equipment of business datum |
| CN110738388A (en) * | 2019-09-02 | 2020-01-31 | 深圳壹账通智能科技有限公司 | Method, device, equipment and storage medium for risk conduction of associated map evaluation |
| CN111222681A (en) * | 2019-11-05 | 2020-06-02 | 量子数聚（北京）科技有限公司 | Data processing method, device, equipment and storage medium for enterprise bankruptcy risk prediction |
| 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 |
| CN111709661A (en) * | 2020-06-23 | 2020-09-25 | 中国平安财产保险股份有限公司 | Risk processing method, device and equipment for business data and storage medium |
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060100957A1 (en) * | 2002-05-08 | 2006-05-11 | Accenture Global Services Gmbh | Electronic data processing system and method of using an electronic data processing system for automatically determining a risk indicator value |
| US20040054563A1 (en) * | 2002-09-17 | 2004-03-18 | Douglas William J. | Method for managing enterprise risk |
| CN109559192A (en) * | 2018-10-25 | 2019-04-02 | 深圳壹账通智能科技有限公司 | Risk checking method, device, equipment and storage medium based on association map |
| CN109657917A (en) * | 2018-11-19 | 2019-04-19 | 平安科技（深圳）有限公司 | Assess method for prewarning risk, device, computer equipment and the storage medium of object |
| CN109657918A (en) * | 2018-11-19 | 2019-04-19 | 平安科技（深圳）有限公司 | Method for prewarning risk, device and the computer equipment of association assessment object |
| CN109858740A (en) * | 2018-12-21 | 2019-06-07 | 中化资本有限公司 | Appraisal procedure, device, computer equipment and the storage medium of business risk |
| CN110163413A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Enterprise supervision and method for early warning, device, computer equipment and readable storage medium storing program for executing |
| CN110390465A (en) * | 2019-06-18 | 2019-10-29 | 深圳壹账通智能科技有限公司 | Air control analysis and processing method, device and the computer equipment of business datum |
| CN110738388A (en) * | 2019-09-02 | 2020-01-31 | 深圳壹账通智能科技有限公司 | Method, device, equipment and storage medium for risk conduction of associated map evaluation |
| CN111222681A (en) * | 2019-11-05 | 2020-06-02 | 量子数聚（北京）科技有限公司 | Data processing method, device, equipment and storage medium for enterprise bankruptcy risk prediction |
| 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 |
| CN111709661A (en) * | 2020-06-23 | 2020-09-25 | 中国平安财产保险股份有限公司 | Risk processing method, device and equipment for business data and storage medium |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112150013A (en) * | 2020-09-27 | 2020-12-29 | 平安资产管理有限责任公司 | Enterprise risk early warning method, device, equipment and readable storage medium |
| CN112965872A (en) * | 2021-02-24 | 2021-06-15 | 刘志欣 | Risk prediction method and device for system cluster nodes, terminal and storage medium |
| CN113094506A (en) * | 2021-04-14 | 2021-07-09 | 每日互动股份有限公司 | Early warning method based on relation map, computer equipment and storage medium |
| CN113094506B (en) * | 2021-04-14 | 2023-08-18 | 每日互动股份有限公司 | Early warning method based on relational graph, computer equipment and storage medium |
| CN113379212A (en) * | 2021-05-31 | 2021-09-10 | 交通运输信息安全中心有限公司 | Block chain-based logistics information platform default risk assessment method, device, equipment and medium |
| CN113379211A (en) * | 2021-05-31 | 2021-09-10 | 交通运输信息安全中心有限公司 | Block chain-based logistics information platform default risk management and control system and method |
| CN113657991A (en) * | 2021-08-12 | 2021-11-16 | 东方微银科技股份有限公司 | Credit risk early warning method, system and storage medium based on graph rule engine |
| CN113642926A (en) * | 2021-08-31 | 2021-11-12 | 上海明略人工智能(集团)有限公司 | Method and device for risk early warning, electronic equipment and storage medium |
| CN113642926B (en) * | 2021-08-31 | 2023-08-08 | 上海明略人工智能(集团)有限公司 | Method and device for risk early warning, electronic equipment and storage medium |
| CN116187767A (en) * | 2023-04-26 | 2023-05-30 | 北京网智天元大数据科技有限公司 | Enterprise risk conduction prediction method and system based on graph attention mechanism |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210295447A1 (en) | Platform for live issuance and management of cyber insurance policies | |
| US10937111B2 (en) | Managing energy purchase agreements on a blockchain | |
| US20210264272A1 (en) | Training method and system of neural network model and prediction method and system | |
| Cont et al. | Recovering portfolio default intensities implied by CDO quotes | |
| US20210073915A1 (en) | Event-driven natural catastrophe modeling and model refinement for insurance and risk management | |
| CN112150014A (en) | Enterprise risk early warning method, device, equipment and readable storage medium | |
| CN112150013A (en) | Enterprise risk early warning method, device, equipment and readable storage medium | |
| JP2019522275A (en) | Electronic mortgage brokerage and monitoring | |
| US20200005410A1 (en) | System and Method for Facilitating Legal Review for Commercial Loan Transactions | |
| CN106651303A (en) | Intelligent contract processing method and system based on templates | |
| Keller et al. | A Reference Model to Support Risk Identification in Cloud Networks. | |
| CN113034274A (en) | Supply chain financial service system and method based on block chain and terminal equipment | |
| Poblet et al. | From Athens to the Blockchain: oracles for digital democracy | |
| US11514531B2 (en) | Platform for autonomous risk assessment and quantification for cyber insurance policies | |
| CN112418859B (en) | Block chain consensus method and device, electronic equipment and readable storage medium | |
| US20210398225A1 (en) | Network risk assessment for live issuance and management of cyber insurance policies | |
| WO2019173241A1 (en) | Platform for live issuance and management of cyber insurance policies | |
| Sancetta | Intraday end-of-day volume prediction | |
| Trusova et al. | Cybersecurity of the Banking Sector in the Context of Digitalization of the World’s Economy | |
| US11276046B2 (en) | System for insights on factors influencing payment | |
| CN112184012B (en) | Enterprise risk early warning method, device, equipment and readable storage medium | |
| CN112184012A (en) | Enterprise risk early warning method, device, equipment and readable storage medium | |
| Gudz et al. | Insurance and innovative technologies of risks management of Ukrainian companies in the digital economy | |
| Koutmos et al. | Nowcasting bitcoin’s crash risk with order imbalance | |
| CN115713424A (en) | Risk assessment method, risk assessment device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |