CN109492945A - Business risk identifies monitoring method, device, equipment and storage medium - Google Patents
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Abstract
The present invention relates to big data analysis process fields, a kind of business risk identification monitoring method, device, equipment and storage medium are disclosed, this method comprises: obtain business data of the Target Enterprise in preset period of time, according to business data determine Target Enterprise belonging to category of employment；Search the corresponding Credit Risk Assessment Model of category of employment；Credit risk characteristic variable is extracted from business data by preset data dimension and is input to Credit Risk Assessment Model, obtains the business risk identification monitored results of Target Enterprise；Determine that the business risk of affiliated enterprise identifies monitored results again；It generates corresponding business risk according to Target Enterprise and the business risk of affiliated enterprise identification monitored results and identifies monitoring report.The present invention is more targeted to business risk identification monitoring, while can also be to there are the enterprises of correlativity to be monitored with Target Enterprise, so that monitoring is more fully, accurately and reliably.
Description
Technical field
The present invention relates to big data analysis processing technology field more particularly to a kind of business risk identification monitoring methods, dress
It sets, equipment and storage medium.
Background technique
Currently, part financial institution all uses all enterprises same or similar when carrying out risk monitoring and control to enterprise
Early-warning Model carry out, Early-warning Model is single, fails for objective group to be finely divided targetedly to carry out risk prevention knowledge
Not.But the difference of the actually affiliated industry of enterprise, the business data such as management mode, the scope of business, Asset Allocation are different from,
The general business data by different enterprises, which is input to the same assessment models, which carries out assessing credit risks, will lead to finally obtain
The case where assessment result accuracy obtained is lower, even will appear estimation error when serious.Therefore, how accurately and effectively to not
Same enterprise carries out risk monitoring and control, is a urgent problem to be solved.
Above content is only used to facilitate the understanding of the technical scheme, and is not represented and is recognized that above content is existing skill
Art.
Summary of the invention
The main purpose of the present invention is to provide a kind of business risk identification monitoring method, device, equipment and storages to be situated between
Matter, it is intended to solve the technical issues of prior art accurately and effectively can not carry out risk monitoring and control to different enterprises.
To achieve the above object, the present invention provides a kind of business risks to identify monitoring method, which comprises
Business data of the Target Enterprise in preset period of time is obtained, the Target Enterprise is determined according to the business data
Affiliated category of employment；
The corresponding risk warning model of the category of employment is searched in the mapping relations constructed in advance；
Credit risk characteristic variable, the credit risk that will be extracted are extracted from the business data by preset data dimension
Characteristic variable is input to the risk warning model, obtains business risk and identifies monitored results；
The incidence relation of the Target Enterprise is excavated according to the business data, and according to default risk transfer weight and mesh
The business risk identification monitored results of mark enterprise determine the business risk identification monitored results of affiliated enterprise；
Corresponding business risk identification prison is generated according to Target Enterprise and the business risk of affiliated enterprise identification monitored results
Control report.
Preferably, the risk warning model includes multiple assessing credit risks submodels；
It is described that credit risk characteristic variable, the credit that will be extracted are extracted from the business data by preset data dimension
Feature of risk variable is input to the risk warning model, obtains the step of business risk identifies monitored results and includes:
Credit risk characteristic variable is extracted from the business data by preset data dimension；
The credit risk characteristic variable is divided according to the assessing credit risks submodel corresponding model classification
Class obtains sorted credit risk characteristic variable；
The sorted credit risk characteristic variable is separately input into corresponding assessing credit risks submodel；
The credit scoring for obtaining each assessing credit risks submodel output, is looked forward to according to the credit scoring
Industry risk identification monitored results.
Preferably, the credit scoring for obtaining each assessing credit risks submodel output, according to the credit wind
Danger scoring obtains the step of business risk identification monitored results, comprising:
The credit scoring of each assessing credit risks submodel output is obtained, and inquires each credit risk in the database
Assess the corresponding default weighted value of submodel；
According to the default weighted value, summation is weighted to the credit scoring by following formula and obtains summation knot
Fruit, and monitored results are identified using the summed result as business risk；
In formula, S is summed result, YiFor the credit scoring of assessing credit risks submodel output, XiFor credit risk
Assess the corresponding default weighted value of submodel.
Preferably, the business data for obtaining Target Enterprise in preset period of time, is determined according to the business data
Before the step of category of employment belonging to the Target Enterprise, the method also includes:
The company information for obtaining several enterprises classifies to the company information by default category of employment, obtains each
The corresponding trade information sample of category of employment；
Credit risk characteristic variable is extracted from the trade information sample by preset data dimension, and according to extracting
Credit risk characteristic variable establish the corresponding risk warning model of every profession and trade classification.
It is preferably, described to extract credit risk characteristic variable from the trade information sample by preset data dimension,
And the step of corresponding risk warning model of every profession and trade classification is established according to the credit risk characteristic variable extracted, comprising:
Credit risk characteristic variable is extracted from the trade information sample by preset data dimension；
It carries out discretization to the credit risk characteristic variable that extracts and decomposes to obtain Variable Factors, and by the Variable Factors
It is input to default neural network model and carries out model training, obtain the corresponding risk warning model of every profession and trade classification.
Preferably, after the step of acquisition every profession and trade classification corresponding risk warning model, the method also includes:
Preset model decomposition rule is read from database, according to the model decomposition rule to every profession and trade classification
Corresponding risk warning model carries out model fractionation, obtains the corresponding assessing credit risks submodel of each risk warning model；
Weight configuration is carried out to the corresponding assessing credit risks submodel of each risk warning model respectively, obtains each credit wind
The corresponding default weighted value of danger assessment submodel.
Preferably, the incidence relation that the Target Enterprise is excavated according to the business data, and according to default risk
Transfer weight determines the business risk identification monitored results of affiliated enterprise, comprising:
The incidence relation of the Target Enterprise is excavated according to the business data, wherein incidence relation includes that equity chain closes
System, family's chain relation, supply chain relationship and bond chain；
The impact factor coefficient of incidence relation is determined according to default risk transfer weight；
Identify that monitored results and impact factor coefficient determine enterprise's wind of affiliated enterprise according to the business risk of Target Enterprise
Danger identification monitored results.
To achieve the above object, the present invention also proposes a kind of business risk identification monitoring device, and described device includes:
Industry determining module, for obtaining business data of the Target Enterprise in preset period of time, according to the business data
Determine category of employment belonging to the Target Enterprise；
Model searching module, for searching the corresponding Risk-warning of the category of employment in the mapping relations constructed in advance
Model；
Risk evaluation module, for extracting credit risk characteristic variable from the business data by preset data dimension,
The credit risk characteristic variable extracted is input to the risk warning model, business risk is obtained and identifies monitored results；
Incidence relation excavates module, for excavating the incidence relation of the Target Enterprise, and root according to the business data
The business risk identification of affiliated enterprise is determined according to the business risk of default risk transfer weight and Target Enterprise identification monitored results
Monitored results；
Report generation module is corresponded to for identifying that monitored results generate according to the business risk of Target Enterprise and affiliated enterprise
Business risk identify monitoring report.
To achieve the above object, the present invention also proposes a kind of business risk identification monitoring device, the business risk identification
Monitoring device includes: memory, processor and is stored in the enterprise's wind that can be run on the memory and on the processor
Danger identification monitoring programme, the business risk identification monitoring programme are arranged for carrying out business risk identification monitoring side as described above
The step of method.
To achieve the above object, the present invention also proposes a kind of storage medium, is stored with business risk on the storage medium
Identify that monitoring programme, the business risk identification monitoring programme realize business risk identification as described above when being executed by processor
The step of monitoring method.
The present invention determines that target is looked forward to by obtaining business data of the Target Enterprise in preset period of time, according to business data
Category of employment belonging to industry；The corresponding Credit Risk Assessment Model of category of employment is searched in the mapping relations constructed in advance；It presses
Preset data dimension extracts credit risk characteristic variable from business data, and the credit risk characteristic variable extracted is input to
Credit Risk Assessment Model obtains business risk and identifies monitored results；The Target Enterprise is excavated further according to the business data
Incidence relation, and according to the business risk of default risk transfer weight and Target Enterprise identify monitored results determine affiliated enterprise
Business risk identify monitored results；It is generated according to Target Enterprise and the business risk of affiliated enterprise identification monitored results corresponding
Business risk identifies monitoring report, as be enterprise first determined according to business data belonging to category of employment, then according to determination
Category of employment out searches corresponding Credit Risk Assessment Model, then credit risk characteristic variable is extracted from business data, and
The credit risk characteristic variable extracted is input to Credit Risk Assessment Model and carries out assessing credit risks, so that enterprise
Risk identification monitoring is more targeted.The present invention can also be to there are the enterprises of correlativity to supervise with Target Enterprise simultaneously
Control, so that monitoring is more fully, accurately and reliably.
Detailed description of the invention
Fig. 1 is that the structure of the business risk identification monitoring device for the hardware running environment that the embodiment of the present invention is related to is shown
It is intended to；
Fig. 2 is the flow diagram that business risk of the present invention identifies monitoring method first embodiment；
Fig. 3 is the flow diagram that business risk of the present invention identifies monitoring method second embodiment；
Fig. 4 is the flow diagram that business risk of the present invention identifies monitoring method 3rd embodiment；
Fig. 5 is the structural block diagram that business risk of the present invention identifies monitoring device first embodiment.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that described herein, specific examples are only used to explain the present invention, is not intended to limit the present invention.
Referring to Fig.1, Fig. 1 is that the business risk for the hardware running environment that the embodiment of the present invention is related to identifies monitoring device
Structural schematic diagram.
As shown in Figure 1, business risk identification monitoring device may include: processor 1001, such as central processing unit
(Central Processing Unit, CPU), communication bus 1002, user interface 1003, network interface 1004, memory
1005.Wherein, communication bus 1002 is for realizing the connection communication between these components.User interface 1003 may include display
Shield (Display), input unit such as keyboard (Keyboard), optional user interface 1003 can also include that the wired of standard connects
Mouth, wireless interface.Network interface 1004 optionally may include standard wireline interface and wireless interface (such as Wireless Fidelity
(WIreless-FIdelity, WI-FI) interface).Memory 1005 can be the random access memory (Random of high speed
Access Memory, RAM) memory, be also possible to stable nonvolatile memory (Non-Volatile Memory,
), such as magnetic disk storage NVM.Memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.
It will be understood by those skilled in the art that structure shown in Fig. 1, which is not constituted, identifies monitoring device to business risk
Restriction, may include perhaps combining certain components or different component layouts than illustrating more or fewer components.
As shown in Figure 1, as may include operating system, network communication mould in a kind of memory 1005 of storage medium
Block, Subscriber Interface Module SIM and business risk identify monitoring programme.
In business risk identification monitoring device shown in Fig. 1, network interface 1004 be mainly used for and network server into
Row data communication；User interface 1003 is mainly used for carrying out data interaction with user；Business risk of the present invention identifies monitoring device
In processor 1001, memory 1005 can be set in business risk identification monitoring device, business risk identification prison
Control equipment calls the business risk stored in memory 1005 to identify monitoring programme by processor 1001, and executes of the invention real
The business risk identification monitoring method of example offer is provided.
The embodiment of the invention provides a kind of business risks to identify monitoring method, is enterprise's wind of the present invention referring to Fig. 2, Fig. 2
The flow diagram of danger identification monitoring method first embodiment.
In the present embodiment, business risk identification monitoring method the following steps are included:
Step S10: obtaining business data of the Target Enterprise in preset period of time, is determined according to the business data described
Category of employment belonging to Target Enterprise；
It should be noted that the executing subject of the present embodiment method can be with network communication, data processing and journey
The calculating service equipment of sort run function, such as mobile phone, tablet computer, PC, server (are below to execute with server
Main body is illustrated).The Target Enterprise is the enterprise for needing to carry out business risk identification monitoring, and the preset time period can be with
It is the self defined time section pre-entered, such as 30 days June -2018 years on the 30th June in 2015, the business data includes enterprise
The administration of justice, public sentiment, industry and commerce, finance, reference, the data of management etc..Correspondingly, category of employment described in the present embodiment
Including but not limited to real estate, non-silver, banking, service trade, manufacturing industry and other industries.
Wherein, judicial data can obtain the jurisdictional information of enterprise by justice system, and therefrom extract in judgement document
It relates to and tells whether the amount of money, enterprise are related to the data informations such as great economy class dispute；Public sentiment data can pass through internet channel searching enterprise
Public sentiment socially, filtering out the public in one period is positive, intermediate or passive feelings for the public sentiment that the enterprise occurs
Thread；Industrial and commercial data can by industrial and commercial system obtain enterprise registered capital whether be reduced, the situation of change of shareholder's shareholding ratio
Equal data informations；Financial data can be obtained by enterprise financial report, and the level of profitability, the payment of debts water of enterprise are analyzed with this
Flat, growth etc.；Collage-credit data can obtain enterprise's sign by the credit information basic database at People's Bank of China's reference center
Believe situation；Experience management data can be obtained by enterprise's investigation report.
In the concrete realization, server can obtain business data of the Target Enterprise in preset period of time, according to business data
In the information such as the type of business, enterprise name, trade mark determine category of employment belonging to the Target Enterprise.
Step S20: the corresponding Credit Risk Assessment Model of the category of employment is searched in the mapping relations constructed in advance；
It should be noted that before executing this step, needs first to be constructed according to the business data of different industries enterprise and be used for
Carry out (industry) Credit Risk Assessment Model of assessing credit risks to the enterprises of different industries, and server build it is each
After the corresponding Credit Risk Assessment Model of industry, a category of employment and the corresponding assessing credit risks of category of employment can be also established
Mapping relations or corresponding relationship between model, in order to which server can determine category of employment belonging to Target Enterprise
Afterwards, it realizes to the quick determining of target Credit Risk Assessment Model and obtains according to the mapping relations.In the mapping relations
In, mapping end source is category of employment, and target side source is Credit Risk Assessment Model.
In the concrete realization, server is after being determined to category of employment belonging to Target Enterprise, can construct in advance
The corresponding Credit Risk Assessment Model of the category of employment is searched in mapping relations.
Step S30: credit risk characteristic variable is extracted from the business data by preset data dimension, by what is extracted
Credit risk characteristic variable is input to the Credit Risk Assessment Model, obtains business risk and identifies monitored results；
It should be noted that the preset data dimension includes but is not limited to law, public sentiment, industrial and commercial information, financial information
And/or the data such as people's row reference extract dimension.
It should be understood that the credit risk characteristic variable is the letter extracted from business data by the default dimension
It ceases data (including quantized data and non-quantized data).It, can be according to default quantitative criteria to its amount of progress for non-quantized data
It is re-used as feature of risk variable after change, such as enterprise can be obtained by internet channel socially for the public sentiment data of enterprise
Then public sentiment carries out just negative marking to public sentiment according to preset scoring criterion (for example, advantage, empty profit, neutrality etc.), then
The quantity of the corresponding great just negative public sentiment of enterprise in a period of time is counted, to realize the quantization to public sentiment data.
In the concrete realization, server can be according to numbers such as law, public sentiment, industrial and commercial information, financial information and/or people's row references
Credit risk characteristic variable is extracted from business data according to dimension is extracted, and then inputs the credit risk characteristic variable extracted
To the Credit Risk Assessment Model constructed in advance, the business risk for obtaining Credit Risk Assessment Model output identifies monitored results.
S40, the incidence relation that the Target Enterprise is excavated according to the business data, and according to default risk transfer weight
And the business risk identification monitored results of Target Enterprise determine the business risk identification monitored results of affiliated enterprise.
S50: it generates corresponding business risk according to Target Enterprise and the business risk of affiliated enterprise identification monitored results and knows
Other monitoring report.
In the concrete realization, server identifies monitored results in the business risk for getting Credit Risk Assessment Model output
Afterwards, the corresponding business risk of Target Enterprise group can be generated according to the result and identify monitoring report, specifically, server can be by enterprise
The corresponding credit scoring of risk identification monitored results is compared with default risk threshold value, judges mesh according to comparison result
Marking enterprise whether there is credit risk, and or determine the credit scoring according to preset credit risk grade
In which kind of risk class, and then judge Target Enterprise with the presence or absence of credit risk；Credit risk is then from business data if it exists
In filter out the risk data for leading to enterprise there are credit risk, and generate the corresponding enterprise of Target Enterprise according to these risk datas
Industry risk identification monitoring report.
The present invention determines that target is looked forward to by obtaining business data of the Target Enterprise in preset period of time, according to business data
Category of employment belonging to industry；The corresponding Credit Risk Assessment Model of category of employment is searched in the mapping relations constructed in advance；It presses
Preset data dimension extracts credit risk characteristic variable from business data, and the credit risk characteristic variable extracted is input to
Credit Risk Assessment Model obtains business risk and identifies monitored results；The Target Enterprise is excavated further according to the business data
Incidence relation, and according to the business risk of default risk transfer weight and Target Enterprise identify monitored results determine affiliated enterprise
Business risk identify monitored results；It is generated according to Target Enterprise and the business risk of affiliated enterprise identification monitored results corresponding
Business risk identifies monitoring report, as be enterprise first determined according to business data belonging to category of employment, then according to determination
Category of employment out searches corresponding Credit Risk Assessment Model, then credit risk characteristic variable is extracted from business data, and
The credit risk characteristic variable extracted is input to Credit Risk Assessment Model and carries out assessing credit risks, so that enterprise
Risk identification monitoring is more targeted.The present invention can also be right simultaneously.
With reference to Fig. 3, Fig. 3 is the flow diagram that business risk of the present invention identifies monitoring method second embodiment.
Based on above-mentioned first embodiment, in business risk provided in this embodiment identification monitoring method in the step S10
Before, further includes:
Step S01: obtaining the company information of several enterprises, divides by default category of employment the company information
Class obtains the corresponding trade information sample of every profession and trade classification；
It should be noted that several described enterprises can be the enterprise or company of different industries classification, the default row
Industry classification can be real estate, non-silver, banking, six major class of service trade, manufacturing industry and other industries, certainly specific industry class
Other setting the present embodiment is with no restriction.It include enterprise's letter of several industry difference enterprises in the trade information sample
Breath.
In addition, in the present embodiment, server can first be believed according to enterprise after getting the company information of several enterprises
Breath is that each enterprise carries out user's portrait, to obtain the corresponding Credit Risk Assessment of Enterprise portrait of each enterprise；Then further according to described default
Category of employment classifies to Credit Risk Assessment of Enterprise portrait, to realize the classification to enterprise and company information, obtains each row
The corresponding trade information sample of industry classification.
It should be understood that so-called portrait, i.e. user (enterprise) information labels, be exactly by collecting and analysis user society
After the data of the main informations such as attribute, living habit, consumer behavior, the business overall picture of a user is ideally taken out.?
Credit risk portrait is carried out to enterprise in the present embodiment, i.e., in a large amount of business data (such as law, public sentiment, industrial and commercial information, finance letter
Breath and/or people's row reference etc.) comprehensive analysis is carried out to basic condition, behavior pattern of enterprise etc. on the basis of information, it is looked forward to
The credit label of industry.
In the concrete realization, server obtains the company information of several enterprises, by preset real estate, non-silver,
The categorys of employment such as banking, service trade, manufacturing industry and other industries classify to company information, obtain every profession and trade classification pair
The trade information sample answered.
Step S02: credit risk characteristic variable, and root are extracted from the trade information sample by preset data dimension
The corresponding Credit Risk Assessment Model of every profession and trade classification is established according to the credit risk characteristic variable extracted.
In the concrete realization, server, can be by preset data after obtaining the corresponding trade information sample of every profession and trade classification
Dimension extracts credit risk characteristic variable from the trade information sample；Then to the credit risk characteristic variable extracted
It carries out discretization and decomposes acquisition Variable Factors, and the Variable Factors are input to default neural network model and carry out model instruction
Practice, obtains the corresponding Credit Risk Assessment Model of every profession and trade classification.
It is understood that often accuracy is not high for initially training model out, therefore it is necessary to right in practical applications
Initially training model out is optimized to improve the accuracy of model output result.Specifically, can be by improving to credit wind
Precision when dangerous characteristic variable (training data) is extracted comes implementation model optimization, the i.e. accuracy of raising training data or effective
Property.Therefore, in the present embodiment server after obtaining Credit Risk Assessment Model, can also according to Credit Risk Assessment Model come
The efficient combination of each Variable Factors is selected, thus to obtain the incidence relation of high-order between Variable Factors, to improve to credit wind
Precision when dangerous characteristic variable is extracted, lift scheme performance.
Further, it to guarantee the credit risk characteristic variable precision with higher extracted, avoids extracting excessive
To the lesser characteristic variable of assessing credit risks influence degree, the calculation amount of server is caused to increase, server in the present embodiment
The corresponding variable information value of each Variable Factors in the initial credit risk evaluation model can be obtained；According to the variable information value
Screening for credit risk characteristic variable is extracted to described, obtains validity feature variable and the validity feature variable pair
The data type answered；Type extracts target credit risk characteristic variable from the trade information sample based on the data；It will
The target credit risk characteristic variable discretization is input to the recurrent neural networks model and carries out model training after decomposing, obtain
Obtain effective Credit Risk Assessment Model.
Wherein, the variable information value can be the corresponding variation coefficient of each Variable Factors；It is described to be believed according to the variable
The corresponding variable of each Variable Factors is believed specific may is that screen for extracting credit risk characteristic variable by breath value
Breath value is compared with preset threshold, obtains the useful variable factor that variable information value is higher than the preset threshold；Have described
The corresponding credit risk characteristic variable of Variable Factors is imitated as validity feature variable, and it is corresponding to obtain the validity feature variable
Data type.
It should be understood that the different corresponding variation coefficients of Variable Factors is not identical in a model, such as current assets
Debt ratio, the corresponding variation coefficient of the past 30 days this kind of Variable Factors of great negative public sentiment sum are theoretically greater than enterprise staff
Quantity, the corresponding variation coefficient of this kind of Variable Factors of enterprise's shareholder's number, therefore in the present embodiment, server can by each variable because
The corresponding variable information value of son is compared with preset threshold, then according to comparison result reject on model output result influence compared with
Small Variable Factors, to improve model computational efficiency.
In view of the business risk identification monitoring method that the present embodiment proposes needs to become the credit risk feature filtered out
Amount fine or not label corresponding with each credit risk characteristic variable is input in model together to be trained, and each credit risk feature
Variable has certain relevance, is not necessarily mutually exclusive, and neural network model is preset described in the present embodiment and is preferably passed
Return neural network model, (also known as Recognition with Recurrent Neural Network model) (Recurrent Neural Network, RNN).
It should be understood that when carrying out assessing credit risks to enterprise, since the business data being related to is many kinds of, classification
It is many and diverse, it is trained if all business data are converged in a certain single model, is unfavorable for subsequent model optimization behaviour
Make, therefore, business risk identification monitoring method provided in this embodiment is constructing the corresponding assessing credit risks mould of every profession and trade
After type, Credit Risk Assessment Model can also be split according to staff's preconfigured model decomposition rule, be obtained
The corresponding assessing credit risks submodel of each Credit Risk Assessment Model, in order to it is subsequent targetedly to each submodel into
Row optimization improves assessment accuracy rate to realize the global optimization to Credit Risk Assessment Model.
Specifically, server can read preset model decomposition rule from database, according to the model decomposition
Rule carries out model fractionation to the corresponding Credit Risk Assessment Model of every profession and trade classification, and it is corresponding to obtain each Credit Risk Assessment Model
Assessing credit risks submodel；Weight is carried out to the corresponding assessing credit risks submodel of each Credit Risk Assessment Model respectively
Configuration, obtains the corresponding default weighted value of each assessing credit risks submodel.
Wherein, the model decomposition rule can be configured to according to dimensions such as financial category, reference class and other classes to every profession and trade
Credit Risk Assessment Model carry out model fractionation, by Credit Risk Assessment Model be decomposed into financial category Credit Risk Assessment Model,
Three submodels such as reference class Credit Risk Assessment Model and other class Credit Risk Assessment Models, and phase is configured for each submodel
The default weighted value answered.
The present embodiment divides company information by default category of employment by the company information of several enterprises of acquisition
Class obtains the corresponding trade information sample of every profession and trade classification；Credit is extracted from trade information sample by preset data dimension
Feature of risk variable, and the corresponding assessing credit risks mould of every profession and trade classification is established according to the credit risk characteristic variable extracted
Type ensure that the business risk identification monitoring model accuracy with higher of foundation.
With reference to Fig. 4, Fig. 4 is the flow diagram that business risk of the present invention identifies monitoring method 3rd embodiment.
Based on the various embodiments described above, in the present embodiment, the Credit Risk Assessment Model includes that multiple credit risks are commented
Estimate submodel.
Correspondingly, step S30 described in business risk identification monitoring method provided in this embodiment may particularly include:
Step S301: credit risk characteristic variable is extracted from the business data by preset data dimension；
In the concrete realization, server can be according to numbers such as law, public sentiment, industrial and commercial information, financial information and/or people's row references
Credit risk characteristic variable is extracted from business data according to dimension is extracted.
Step S302: the credit risk feature is become according to the assessing credit risks submodel corresponding model classification
Amount is classified, and sorted credit risk characteristic variable is obtained；
It should be understood that the model classification as belonging to each assessing credit risks submodel is different, server from
When extracting credit risk characteristic variable in business data, need corresponding according to the assessing credit risks submodel split in advance
Model classification classify to credit risk characteristic variable, obtain sorted credit risk characteristic variable, such as will be industrial and commercial
The credit risk characteristic variable of the dimensions such as information, financial information is classified as financial category Credit Risk Assessment Model, by law, people's row
The credit risk characteristic variable of the dimensions such as reference is classified as reference class Credit Risk Assessment Model, by the credit risk of public sentiment dimension
Characteristic variable is classified as other class Credit Risk Assessment Models etc..
Step S303: the sorted credit risk characteristic variable is separately input into corresponding assessing credit risks
Model；
In the concrete realization, server, can be by sorted letter after completing to the classification of credit risk characteristic variable
Corresponding assessing credit risks submodel, which is separately input into, with feature of risk variable carries out assessing credit risks.
Step S304: the credit scoring of each assessing credit risks submodel output is obtained, according to the credit risk
Scoring obtains business risk and identifies monitored results.
In the concrete realization, server can by obtain the credit scoring of each assessing credit risks submodel output come
The business risk for obtaining Target Enterprise identifies monitored results.
Specifically, the credit scoring that server can be exported by obtaining each assessing credit risks submodel, and in number
According to inquiring the corresponding default weighted value of each assessing credit risks submodel in library；Then according to the default weighted value, under
Formula is weighted summation to the credit scoring and obtains summed result, and identifies the summed result as business risk
Monitored results；
In formula, S is summed result, YiFor the credit scoring of assessing credit risks submodel output, XiFor credit risk
Assess the corresponding default weighted value of submodel.
For example, if the corresponding default weighted value of financial category Credit Risk Assessment Model is 0.5, reference class assessing credit risks
The corresponding default weighted value of model is 0.35, and the corresponding default weighted value of other class Credit Risk Assessment Models is 0.15, then
When the credit scoring of financial category Credit Risk Assessment Model output is the letter that 80, reference class Credit Risk Assessment Model exports
Be 75 with risk score, the credit scoring of other class Credit Risk Assessment Models output is when being 90, to credit scoring
The summed result for being weighted summation is then 80*0.5+75*0.35+90*0.15=79.75.
In practice, enterprise there are the relationships of countless ties with other enterprises in the market, when the warp of some enterprise
When battalion's situation deteriorates, relative enterprise is just probably fed through to.For example, being generally included below a group company
There are multiple subsidiaries, the correlation comparison between multiple subsidiaries is close.When one of subsidiary occur profit margin decline or
When generation burst accident, it is possible to involve the financial situation of other subsidiaries.It is described according in the present embodiment
Business data excavates the incidence relation of the Target Enterprise, and enterprise's wind of affiliated enterprise is determined according to default risk transfer weight
Danger identification monitored results, comprising:
The incidence relation of the Target Enterprise is excavated according to the business data, wherein incidence relation includes that equity chain closes
System, family's chain relation, supply chain relationship and bond chain.Business connection in the present embodiment can be the enterprise between Liang Ge enterprise
Relationship is also possible to the relationship constituted via one or more third party enterprise.More days relation chains are thus constituted, are in
Relation chain is interrelated enterprise.
The impact factor coefficient of incidence relation is determined according to default risk transfer weight.The risk of different business connections turns
Weight is moved to be different.Based on practical experience, equity chain relation, family's chain relation, supply chain relationship and bond chain are for enterprise
Between venture influence it is larger.The impact factor of equity chain relation, family's chain relation, supply chain relationship and bond chain can successively be assigned
Coefficient is 0.5,0.3,0.1 and 0.1.
Identify that monitored results and impact factor coefficient determine enterprise's wind of affiliated enterprise according to the business risk of Target Enterprise
Danger identification monitored results.It assigns the business risk identification monitored results of Target Enterprise to impact factor coefficient, association can be obtained
The business risk of enterprise identifies monitored results.
Then known according to affiliated enterprise's risk identification monitored results and the corresponding business risk of the Target Enterprise
Other monitored results generate business risk and identify monitoring report.
The embodiment of the present invention as be enterprise first determined according to business data belonging to category of employment, then according to determining
Category of employment search corresponding Credit Risk Assessment Model, then credit risk characteristic variable is extracted from business data, and will
The credit risk characteristic variable extracted is input to Credit Risk Assessment Model and carries out assessing credit risks, so that enterprise's wind
Danger identification monitoring is more targeted.The present invention can also be to there are the enterprises of correlativity to supervise with Target Enterprise simultaneously
Control, so that monitoring is more fully, accurately and reliably.
In addition, the embodiment of the present invention also proposes a kind of storage medium, business risk identification is stored on the storage medium
Monitoring programme, the business risk identification monitoring programme realize business risk identification prison as described above when being executed by processor
The step of prosecutor method.
It is the structural block diagram that business risk of the present invention identifies monitoring device first embodiment referring to Fig. 5, Fig. 5.
As shown in figure 5, the business risk identification monitoring device that the embodiment of the present invention proposes includes:
Industry determining module 100, for obtaining business data of the Target Enterprise in preset period of time, according to enterprise's number
According to determining category of employment belonging to the Target Enterprise；
Model searching module 200, for searching the corresponding risk of the category of employment in the mapping relations constructed in advance
Early-warning Model；
Risk evaluation module 300, for extracting the change of credit risk feature from the business data by preset data dimension
The credit risk characteristic variable extracted is input to the risk warning model by amount, is obtained business risk and is identified monitored results；
Incidence relation excavates module 400, for excavating the incidence relation of the Target Enterprise according to the business data, and
Identify that monitored results determine that the business risk of affiliated enterprise is known according to the business risk of default risk transfer weight and Target Enterprise
Other monitored results；
Report generation module 500, for identifying that monitored results generate according to the business risk of Target Enterprise and affiliated enterprise
Corresponding business risk identifies monitoring report.
The present invention determines that target is looked forward to by obtaining business data of the Target Enterprise in preset period of time, according to business data
Category of employment belonging to industry；The corresponding Credit Risk Assessment Model of category of employment is searched in the mapping relations constructed in advance；It presses
Preset data dimension extracts credit risk characteristic variable from business data, and the credit risk characteristic variable extracted is input to
Credit Risk Assessment Model obtains business risk and identifies monitored results；The Target Enterprise is excavated further according to the business data
Incidence relation, and according to the business risk of default risk transfer weight and Target Enterprise identify monitored results determine affiliated enterprise
Business risk identify monitored results；It is generated according to Target Enterprise and the business risk of affiliated enterprise identification monitored results corresponding
Business risk identifies monitoring report, as be enterprise first determined according to business data belonging to category of employment, then according to determination
Category of employment out searches corresponding Credit Risk Assessment Model, then credit risk characteristic variable is extracted from business data, and
The credit risk characteristic variable extracted is input to Credit Risk Assessment Model and carries out assessing credit risks, so that enterprise
Risk identification monitoring is more targeted.The present invention can also be to there are the enterprises of correlativity to supervise with Target Enterprise simultaneously
Control, so that monitoring is more fully, accurately and reliably.
Monitoring device first embodiment is identified based on the above-mentioned business risk of the present invention, proposes business risk identification prison of the present invention
Control the second embodiment of device.
In the present embodiment, the risk evaluation module 300 is also used to by preset data dimension from the business data
Extract credit risk characteristic variable；It is special to the credit risk according to the corresponding model classification of the assessing credit risks submodel
Sign variable is classified, and sorted credit risk characteristic variable is obtained；By the sorted credit risk characteristic variable point
It is not input to corresponding assessing credit risks submodel；The credit scoring of each assessing credit risks submodel output is obtained,
Business risk, which is obtained, according to the credit scoring identifies monitored results.
Further, the risk evaluation module 300 is also used to obtain the credit of each assessing credit risks submodel output
Risk score, and the corresponding default weighted value of each assessing credit risks submodel is inquired in the database；According to the default power
Weight values, by following formula to the credit scoring be weighted summation obtain summed result, and using the summed result as
Business risk identifies monitored results；
In formula, S is summed result, YiFor the credit scoring of assessing credit risks submodel output, XiFor credit risk
Assess the corresponding default weighted value of submodel.
Further, the present embodiment business risk identification monitoring device further includes model building module, the model foundation
Module classifies to the company information by default category of employment for obtaining the company information of several enterprises, obtains each
The corresponding trade information sample of category of employment；Credit risk spy is extracted from the trade information sample by preset data dimension
Variable is levied, and the corresponding Credit Risk Assessment Model of every profession and trade classification is established according to the credit risk characteristic variable extracted.
Further, the model building module is also used to mention from the trade information sample by preset data dimension
Take out credit risk characteristic variable；Discretization is carried out to the credit risk characteristic variable extracted and decomposes acquisition Variable Factors, and
The Variable Factors are input to default neural network model and carry out model training, obtain the corresponding credit risk of every profession and trade classification
Assessment models.
Further, the model building module is also used to read preset model decomposition rule from database,
Model fractionation is carried out to the corresponding Credit Risk Assessment Model of every profession and trade classification according to the model decomposition rule, obtains each credit
The corresponding risk assessment submodel of risk evaluation model；Respectively to the corresponding risk assessment submodel of each Credit Risk Assessment Model
Weight configuration is carried out, the corresponding default weighted value of each assessing credit risks submodel is obtained.
Further, the incidence relation excavates module 400, and the pass of the Target Enterprise is excavated according to the business data
Connection relationship, wherein incidence relation includes equity chain relation, family's chain relation, supply chain relationship and bond chain；According to default risk
Transfer weight determines the impact factor coefficient of incidence relation；According to the business risk of Target Enterprise identify monitored results and influence because
Subsystem number determines the business risk identification monitored results of affiliated enterprise.
The other embodiments or specific implementation of business risk identification monitoring device of the present invention can refer to above-mentioned each method
Embodiment, details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the system that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as read-only memory/random access memory, magnetic disk, CD), including some instructions are used so that a terminal device (can
To be mobile phone, computer, server, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of business risk identifies monitoring method, which is characterized in that the described method includes:
Obtain business data of the Target Enterprise in preset period of time, the Target Enterprise is determined according to the business data belonging to
Category of employment；
The corresponding risk warning model of the category of employment is searched in the mapping relations constructed in advance；
Credit risk characteristic variable is extracted from the business data by preset data dimension, the credit risk feature that will be extracted
Variable is input to the risk warning model, obtains business risk and identifies monitored results；
The incidence relation of the Target Enterprise is excavated according to the business data, and is looked forward to according to default risk transfer weight and target
The business risk identification monitored results of industry determine the business risk identification monitored results of affiliated enterprise；
Corresponding business risk identification monitoring report is generated according to Target Enterprise and the business risk of affiliated enterprise identification monitored results
It accuses.
2. the method as described in claim 1, which is characterized in that the risk warning model includes multiple assessing credit risks
Model；
It is described that credit risk characteristic variable, the credit risk that will be extracted are extracted from the business data by preset data dimension
Characteristic variable is input to the risk warning model, obtains the step of business risk identifies monitored results and includes:
Credit risk characteristic variable is extracted from the business data by preset data dimension；
Classified according to the corresponding model classification of the assessing credit risks submodel to the credit risk characteristic variable, is obtained
Obtain sorted credit risk characteristic variable；
The sorted credit risk characteristic variable is separately input into corresponding assessing credit risks submodel；
The credit scoring for obtaining each assessing credit risks submodel output, obtains enterprise's wind according to the credit scoring
Danger identification monitored results.
3. method according to claim 2, which is characterized in that the credit for obtaining each assessing credit risks submodel output
Risk score obtains the step of business risk identifies monitored results according to the credit scoring, comprising:
The credit scoring of each assessing credit risks submodel output is obtained, and inquires each assessing credit risks in the database
The corresponding default weighted value of submodel；
According to the default weighted value, summation is weighted to the credit scoring by following formula and obtains summed result, and
Monitored results are identified using the summed result as business risk；
In formula, S is summed result, YiFor the credit scoring of assessing credit risks submodel output, XiFor assessing credit risks
The corresponding default weighted value of submodel.
4. the method as described in claim 1, which is characterized in that the enterprise's number for obtaining Target Enterprise in preset period of time
According to, according to the business data determine the Target Enterprise belonging to category of employment the step of before, the method also includes:
The company information for obtaining several enterprises classifies to the company information by default category of employment, obtains every profession and trade
The corresponding trade information sample of classification；
Credit risk characteristic variable is extracted from the trade information sample by preset data dimension, and according to the letter extracted
The corresponding risk warning model of every profession and trade classification is established with feature of risk variable.
5. method as claimed in claim 4, which is characterized in that the preset data dimension of pressing is from the trade information sample
Credit risk characteristic variable is extracted, and the corresponding risk of every profession and trade classification is established according to the credit risk characteristic variable extracted
The step of Early-warning Model, comprising:
Credit risk characteristic variable is extracted from the trade information sample by preset data dimension；
Discretization is carried out to the credit risk characteristic variable extracted and decomposes acquisition Variable Factors, and the Variable Factors are inputted
Model training is carried out to default neural network model, obtains the corresponding risk warning model of every profession and trade classification.
6. method as claimed in claim 5, which is characterized in that the every profession and trade classification corresponding risk warning model of obtaining
After step, the method also includes:
Preset model decomposition rule is read from database, it is corresponding to every profession and trade classification according to the model decomposition rule
Risk warning model carry out model fractionation, obtain the corresponding assessing credit risks submodel of each risk warning model；
Weight configuration is carried out to the corresponding assessing credit risks submodel of each risk warning model respectively, each credit risk is obtained and comments
Estimate the corresponding default weighted value of submodel.
7. such as method of any of claims 1-6, which is characterized in that described according to business data excavation
The incidence relation of Target Enterprise, and determine that the business risk of affiliated enterprise identifies monitored results according to default risk transfer weight,
Include:
The incidence relation of the Target Enterprise is excavated according to the business data, wherein incidence relation includes equity chain relation, family
Race's chain relation, supply chain relationship and bond chain relation；
The impact factor coefficient of incidence relation is determined according to default risk transfer weight；
Identify that monitored results and impact factor coefficient determine that the business risk of affiliated enterprise is known according to the business risk of Target Enterprise
Other monitored results.
8. a kind of business risk identifies monitoring device, which is characterized in that described device includes:
Industry determining module is determined for obtaining business data of the Target Enterprise in preset period of time according to the business data
Category of employment belonging to the Target Enterprise out；
Model searching module, for searching the corresponding Risk-warning mould of the category of employment in the mapping relations constructed in advance
Type；
Risk evaluation module will be mentioned for extracting credit risk characteristic variable from the business data by preset data dimension
The credit risk characteristic variable got is input to the risk warning model, obtains business risk and identifies monitored results；
Incidence relation excavates module, for excavating the incidence relation of the Target Enterprise according to the business data, and according to pre-
If the business risk of risk transfer weight and Target Enterprise identification monitored results determine the business risk identification monitoring of affiliated enterprise
As a result；
Report generation module, for identifying that monitored results generate corresponding enterprise according to the business risk of Target Enterprise and affiliated enterprise
Industry risk identification monitoring report.
9. a kind of business risk identifies monitoring device, which is characterized in that the business risk identification monitoring device includes: storage
The business risk identification monitoring programme that device, processor and being stored in can be run on the memory and on the processor, institute
The business risk identification that business risk identification monitoring programme is arranged for carrying out as described in any one of claims 1 to 7 is stated to monitor
The step of method.
10. a kind of storage medium, which is characterized in that business risk identification monitoring programme is stored on the storage medium, it is described
Business risk identification monitoring programme realizes business risk identification as described in any one of claim 1 to 7 when being executed by processor
The step of monitoring method.
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