CN107292509A - A kind of enterprise's credit risk early-warning monitoring method - Google Patents
A kind of enterprise's credit risk early-warning monitoring method Download PDFInfo
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Abstract
The invention discloses a kind of enterprise's credit risk early-warning monitoring method, including：The corporate client list provided according to system user, collects multiparty data source, and the associated data that these are extracted is merged into duplicate removal mode using deletion invalid data and data carries out data cleansing, generates data correlation relation figure, and repeat to be associated excavation；According to the multiple data sources got, the scoring snap gauge provided with reference to related service personnel judges to meet the factor of risk trigger condition, calculates the risk score value of main enterprise itself；The association factor of influence of affiliated enterprise is calculated, sets different risk transfer weights to weigh the transfer ratio of different incidence relations, according to the risk score value for calculating affiliated enterprise；The risk score value of the affiliated enterprise calculated is weighted in the risk score value of main enterprise, final risk score value is drawn；Mechanics graph model is used to draw incidence relation figure finally according to the risk score value of calculating.Present invention optimizes the form that shows of incidence relation figure, bandwagon effect is more preferable.
Description
Technical field
The present invention relates to credit risk management and control, Risk-warning field, more particularly to a kind of enterprise's credit risk early warning
Monitoring method.
Background technology
Under the overall background of current banking " mortgaging credulous use again ", enterprise's credit risk will not only consider enterprise itself
The factors such as management state, credit situation, it is contemplated that the risk status of the affiliated enterprise of enterprise, more particularly to assures relation
Enterprise.And the risk of affiliated enterprise included to the product that overall risk considers is also seldom at present, existing product is only drawn mostly
Associated diagram, the risk without calculating affiliated enterprise.
Different incidence relations is not answered identical, it is necessary to treat with a certain discrimination for the transmission of business risk, sets different influences
The factor calculates the risk score value of conduction, uses mechanics figure displaying incidence relation figure more, and simple mechanics graph model is easily led
Together with cause point mixes with side, visual effect is poor.
The content of the invention
For deficiencies of the prior art, the invention provides a kind of enterprise's credit risk early-warning monitoring method,
The comprehensive each side data source of the present invention, includes a variety of incidence relations, and employs a kind of new algorithm based on graph of a relation, counts
Calculate venture influence coefficient of each affiliated enterprise to main enterprise；The placement algorithm of incidence relation figure is improved, can " exhibition automatically
Open ", an optimization visuality overlapping with side is reduced as far as possible.
To achieve the above object, the present invention is realized according to following technical scheme：
A kind of enterprise's credit risk early-warning monitoring method, it is characterised in that comprise the following steps：
Step S1:The corporate client list provided according to system user, collects multiparty data source, the pass that these are extracted
Connection data carry out data cleansing by the way of invalid data and data merging duplicate removal is deleted, and data correlation relation is generated afterwards
Figure, and repeat to be associated excavation；
Step S2:According to the information of the multiple data sources got, the scoring snap gauge provided with reference to related service personnel is sentenced
The disconnected factor for meeting risk trigger condition, calculates the risk score value of main enterprise itself；
Step S3:The association factor of influence of affiliated enterprise is calculated, sets different risk transfer weights to weigh different passes
The transfer ratio of connection relation, and according to the risk score value of above-mentioned steps calculating affiliated enterprise；
Step S4:The risk score value of the affiliated enterprise calculated is weighted in the risk score value of main enterprise, drawn final
Risk score value；
Step S5：Mechanics graph model is used to draw incidence relation figure finally according to the risk score value of calculating.
In above-mentioned technical proposal, in the step S1, the data source calculates the credit risk situation of enterprise, and carries
Take out the attribute of the affiliated enterprise of enterprise.
In above-mentioned technical proposal, the attribute of the affiliated enterprise includes the legal person, the shareholder of enterprise, the investment of legal person of enterprise
Enterprise, the guarantee of enterprise and by guarantor enterprise.
In above-mentioned technical proposal, in step s3, enterprise is defined or personal for main body S, main body SiWith main body SjBetween pass
It is for R (Si, Sj), the definition of the associated path P comprising n main body is P=S1R(S1, S2)S2R(S2, S3)S3……Sn, wherein S1
For main enterprise, then R (P)=R (S1, Sn)；
SiApart from S1Compare Si+1Closer to, from business implication, with main enterprise closer to incidence relation influence it is bigger, i.e.,
R(Si, Si+1) than R (Si+1, Si+2) to S1Influence it is bigger, design a weighting function so that apart from S1Farther relation
Influence coefficient smaller, by test of many times, selection weighting function isThis function is in x=1 to x=6 interval
Value is respectively [0.9526,0.8808,0.7311,0.5,0.2689,0.1192], by adding after value standardization during x=1
Weight function isTherefore：
Final calculation formula is as follows：
In above-mentioned technical proposal, in step S3, for affiliated enterprise Sk, it is first obtained with depth-first traversal to main enterprise
S1All associated paths, it is assumed that a total of m bars associated path Pk1, Pk2..., Pkm, then shadow of the affiliated enterprise to main enterprise
It is exactly maximum factor of influence in this m bar associated path to ring the factor, i.e.,：
R(S1, Sk)=max { R (Pk1), R (Pk2) ..., R (Pkm)}。
The present invention compared with prior art, has the advantages that：
The present invention is directed to different incidence relations, sets different factors of influence, is closed while devising a kind of suitable association
It is the association factor of influence calculation of figure so that it is more accurate that the risk of enterprise is considered；Present invention optimizes incidence relation figure
Show form so that final bandwagon effect is more preferable.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of schematic diagram of enterprise's credit risk early-warning monitoring method of the present invention；
Fig. 2 is the final effect figure of the monitoring method by the present invention.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than whole embodiments.
Fig. 1 is a kind of schematic diagram of enterprise's credit risk early-warning monitoring method of the present invention.As shown in figure 1, the present invention
A kind of enterprise's credit risk early-warning monitoring method, it is characterised in that comprise the following steps：
Step S1:The corporate client list provided according to system user, collects multiparty data source, the pass that these are extracted
Connection data carry out data cleansing by the way of invalid data and data merging duplicate removal is deleted, and data correlation relation is generated afterwards
Figure, and repeat to be associated excavation；
Wherein specifically, provide a collection of corporate client list by the user of system, system using reptile, purchase data source,
With reference to modes such as industry internal datas, multiparty data source is obtained.Data source calculates the credit risk situation of enterprise, and extracts enterprise
The attribute of the affiliated enterprise of industry.Legal person of the attribute of affiliated enterprise including enterprise, the shareholder of enterprise, the investment enterprise of legal person, enterprise
The guarantee of industry and by guarantor enterprise.The associated data for extracting these is needed to carry out data cleansing, the means of use afterwards
Including：Delete invalid data and data merge duplicate removal.It is due to that partial data source is second-rate to delete invalid data, some data
Data distortion can be produced during transmission, storage or displaying, causes data unavailable；Data merging duplicate removal is then related to multiple
The problem of data of data source merge, for example, A data sources and B data source all extract the relation of legal person, due to the Enterprise Law simultaneously
People one-man in theory, then if A is different with B legal person, it is necessary to according to the degree of reliability of different data sources, selection
Most believable data are used as final result；Same situation is just not quite alike for shareholder, because shareholder may have many people,
Therefore the shareholder by A and B is needed to seek union.And having special processing when judging whether main body is identical, enterprise may bear the same name
Situation it is few, therefore it is less problematic with enterprise name to sentence weight, but people with regard to different, it is necessary to sentence weight with identification card number.But
Some data sources do not provide identification card number again, then whether the shareholder Zhang San of A data sources and the shareholder Zhang San in B data source are same
Individual can not just determine, therefore can only assume that the associate people of same enterprise will not bear the same name, it is believed that they are same persons, then
Merge the information of supplement Zhang San.So by after data cleansing, incidence relation figure can be constituted, the figure can update at any time and need through
Often carry out data cleansing.
Carry out after such affiliated enterprise excavates, can expand initial enterprise's list, therefore can be to excavating
Affiliated enterprise, which repeats, is associated excavation, as long as initial list is more than enough in theory, can it is exhaustive go out all enterprises in the whole nation.
But in the comprehensive consideration to amount of calculation and information value, three layers of incidence relation are excavated in the system selection, that is, excavate third layer
Do not continue to be associated excavation after affiliated enterprise.
Step S2:According to the information of the multiple data sources got, the scoring snap gauge provided with reference to related service personnel is sentenced
The disconnected factor for meeting risk trigger condition, calculates the risk score value of main enterprise itself；
Step S3:The association factor of influence of affiliated enterprise is calculated, sets different risk transfer weights to weigh different passes
The transfer ratio of connection relation, and according to the risk score value of above-mentioned steps calculating affiliated enterprise；
First, incidence relation and associated path are defined, wherein incidence relation refers to the relation between the enterprise of two associations, the two
There may be direct incidence relation, it is also possible to constitute incidence relation via one or more third party enterprises；Associated path refers to
From an enterprise to the relation chain of another affiliated enterprise, relation chain not only inclusion relation, also comprising Enterprise Object, such as A-
Guarantee-B-investment-C, and A-guarantee-D-investment-E associated paths that to be two different, but associating between AE between AC
Relation is the same.
Different incidence relations be for the influence of enterprise it is different, at present the maximum relation of influence may be exactly guarantee and
Tender guarantee relation, for example：There is risk status and causes the feelings such as overdue loan, debit interest in the affiliated enterprise that enterprise tenders guarantee
Condition, then corresponding risk will be married again with the enterprise；And some administrative staff of weaker relation, such as enterprise throw
The affiliated enterprise of money, even if such affiliated enterprise is out of joint, because it with the enterprise does not have direct fund, interests past
Come, therefore the risk married again is just very little.
Therefore, for several incidence relations extracted at present, by business personnel with business experience, different risks are set to turn
Weight is moved, is subsequently set again by data check, adjusting parameter, optimization score value, the transfer of different incidence relations is weighed with this
Coefficient.
Step S4:The risk score value of the affiliated enterprise calculated is weighted in the risk score value of main enterprise, drawn final
Risk score value；
Step S5：Mechanics graph model is used to draw incidence relation figure finally according to the risk score value of calculating.
In step sl, enterprise is defined or personal for main body S, main body SiWith main body SjBetween relation be R (Si, Sj), bag
The definition of associated path P containing n main body is P=S1R(S1, S2)S2R(S2, S3)S3......Sn, wherein S1For main enterprise, then R
(P)=R (S1, Sn)；
SiApart from S1Compare Si+1Closer to, from business implication, with main enterprise closer to incidence relation influence it is bigger, i.e.,
R(Si, Si+1) than R (Si+1, Si+2) to S1Influence it is bigger, design a weighting function so that apart from S1Farther relation
Influence coefficient smaller, by test of many times, selection weighting function isThis function is in x=1 to x=6 interval
Value is respectively [0.9526,0.8808,0.7311,0.5,0.2689,0.1192], by adding after value standardization during x=1
Weight function isTherefore：
Final calculation formula is as follows：
In step S3, for affiliated enterprise Sk, it is first obtained with depth-first traversal to main enterprise S1The relevant road of institute
Footpath, it is assumed that a total of m bars associated path Pk1, Pk2..., Pkm, then the factor of influence of affiliated enterprise to main enterprise is exactly this m bar
Maximum factor of influence in associated path, i.e.,：
R(S1, Sk)=max { R (Pk1), R (Pk2) ..., R (Pkm)}。
But because the incidence relation between enterprise may be a lot, such as A and B is that guarantee relation is investment relation, B and C again
Shareholder, investment, guarantee relation, then can have altogether between A and C 6 kinds of incidence relations (guarantee × shareholder, guarantee × investment,
Guarantee × guarantee, investment × shareholder, investment × investment, investment × guarantee).Assuming that only considering three layers of incidence relation, then final
Just up to tens of kinds of relation between the enterprise associated within three layers of any two, a pair of these relations set weights to be unrealistic
, also it is unfavorable for the expansion of down-stream due to reasons such as computational efficiency raisings, the association number of plies is improved to 4 layers, it is therefore desirable to
A kind of method of the transfer factor for the incidence relation that can calculate multilayer of design.
Simultaneously because although incidence relation has the direction in business implication, but from relational angle be it is nondirectional, because
This, which can regard incidence relation figure as non-directed graph, has all associated paths between ring figure, two such enterprise will be exponentially
Increase, tens associated paths are likely to have between Liang Ge enterprises in the case of reality, in addition it is also necessary to determine how and calculate finally
Influence coefficient.
Step S5 is unrelated with Risk-warning calculating, is directed primarily to carry out data exhibiting in page-end.Many associations at present
The displaying of graph of a relation all uses mechanics graph model, and its principle is the gravitation of analog physical mechanics and the relation of repulsion, any two
Repulsion is all there is between point, and gravitation is then there is between the point on relevant side, by the position for constantly iterating to calculate adjustment point
Put, be finally reached the balance of gravitation and repulsion.
The method that the present invention is used is that all points overlap at the beginning, then starts interative computation.Due to point
Between repulsion and gravitation relation, can each flick, shrink, be finally reached the balance of power.But such way has larger
Defect, exactly final state is the poised state of power, do not account for side it is overlapping the problem of, so causing arrangement to be compared
In a jumble, many sides are mixed together, for example accompanying drawing 1.The present invention optimizes on the model of mechanics figure, and final effect is as schemed
2, it can be seen that clear much in the level arrangement of point, visual effect is preferable.But, in the incidence relation figure of the system
In, it is contemplated that the association points for crossing multilayer are too many, influence real experiences, therefore only show point and the side of most two layers of association.
First, plan all ranges of pressing to be layered from the beeline of main enterprise, plan first layer point be arranged in
Main enterprise is that the second layer is come on twice of radius R circle, and such bandwagon effect relatively has layer on the center of circle, half as much again footpath R circle
Secondary sense.And our optimization aim, a preferably arrangement mode is exactly determined to the point on same circle so that what side intersected
Situation is as few as possible.
First consider a simplified model --- only one layer of relation, and all points are only relevant with main enterprise.That
As long as it will be apparent that all points are come on circle at equal intervals with random order, it is ensured that intersecting without side.
Remove a qualifications, institute a little all may be relevant with other points, then cannot arbitrarily arrange, otherwise
Three point ABC on the situation for side intersection occur, such as one section circular arc are just easy to, if relevant side between AC, and A and C
With the equal onrelevant sides of B, then the order arrangement according to ABC just has intersection and become.Obviously only need to adjust order, be changed into
ACB or BAC, it is possible to the situation for avoiding side from intersecting.Therefore, corresponding optimization method is：Main enterprise is taken away from figure, counted
The connected subgraph situation of residual graph is calculated, the point of same connected subgraph is come together, can so try one's best the feelings for intersecting side
Condition is reduced.
The situation of two layers of relation is considered further that, because condition above has determined that only two layers incidence relation, then
The point of the second layer only can be relevant with the point of first layer, and three layers of association otherwise just occur, as long as therefore arranging the point of the second layer
Cloth is in the sector region of associated first layer point.For example, the angle of the point of first layer be α, then the point of the second layer just etc.
The interval for being arranged in [α-γ, α+γ] of spacing is interior, and wherein γ is a fixed parameter.It should be noted that being likely to occur
Do not interconnected between the point of first layer, but the situation of some point connection via the second layer, therefore, calculating connection of first layer
During figure, the point of the second layer is also contemplated for into.
Finally, two larger connected subgraphs are avoided to come together in order to try one's best so that whole figure " top-heavy ", ratio
It is uncoordinated, it is considered to " acnode " is assigned between different connected subgraphs, such as accompanying drawing 2.
However, this optimization simply optimizes the initial arrangement of mechanics figure, the follow-up gravitation and reprimand for still passing through mechanics figure
The position of the balanced adjustment point of power, but be exactly because optimization calculates initial position so that final effect has greatly
Improve.
The Optimizing Flow of the present invention mainly includes：All relating dots are calculated to the beeline of main enterprise, according to most short distance
From layering；Main enterprise is taken away from figure, the connected subgraph of residual graph is calculated；By the point of first layer according to connected subgraph sub-clustering,
Dividing in same cluster in same connected subgraph, while " acnode " is found out；Between different clusters, namely non-" acnode " it
Between, " acnode " is equably assigned, so that first layer point puts in order.
By the point of the second layer according to the incidence relation with the point of first layer, corresponding cluster is assigned to.Second layer point is uniformly arranged
Cloth is at interval [α-γ, β+γ], and wherein α and β are the minimum and maximum of first layer point angle in the cluster respectively, need exist for note
Meaning is across 0 degree of special circumstances, if the span between 330 degree to 30 degree is 60 degree, rather than 300 degree.
The specific embodiment of the present invention is described above.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make a variety of changes or change within the scope of the claims, this not shadow
Ring the substantive content of the present invention.In the case where not conflicting, feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (5)
1. a kind of enterprise's credit risk early-warning monitoring method, it is characterised in that comprise the following steps：
Step S1:The corporate client list provided according to system user, collects multiparty data source, the incidence number that these are extracted
Data cleansing is carried out according to by the way of invalid data and data merging duplicate removal is deleted, data correlation relation figure is generated afterwards, and
Repeat association mining；
Step S2:According to the information of the multiple data sources got, the scoring snap gauge provided with reference to related service personnel judges full
The factor of sufficient risk trigger condition, calculates the risk score value of main enterprise itself；
Step S3:The association factor of influence of affiliated enterprise is calculated, sets different risk transfer weights to weigh different associations and closes
The transfer ratio of system, and according to the risk score value of above-mentioned steps calculating affiliated enterprise；
Step S4:The risk score value of the affiliated enterprise calculated is weighted in the risk score value of main enterprise, final wind is drawn
Dangerous score value；
Step S5：Mechanics graph model is used to draw incidence relation figure finally according to the risk score value of calculating.
2. a kind of enterprise's credit risk early-warning monitoring method according to claim 1, it is characterised in that the step S1
In, the data source calculates the credit risk situation of enterprise, and extracts the attribute of the affiliated enterprise of enterprise.
3. a kind of enterprise's credit risk early-warning monitoring method according to claim 2, it is characterised in that the affiliated enterprise
The legal person of attribute including enterprise, the shareholder of enterprise, the investment enterprise of legal person, the guarantee of enterprise and by guarantor enterprise.
4. a kind of enterprise's credit risk early-warning monitoring method according to claim 1, it is characterised in that in the step S3
In, define enterprise or personal for main body S, main body SiWith main body SjBetween relation be R (Si, Sj), include the association of n main body
The definition of path P is P=S1R(S1, S2)S2R(S2, S3)S3......Sn, wherein S1For main enterprise, then R (P)=R (S1, Sn)；
SiApart from S1Compare Si+1Closer to, from business implication, with main enterprise closer to incidence relation influence it is bigger, i.e. R
(Si, Si+1) than R (Si+1, Si+2) to S1Influence it is bigger, design a weighting function so that apart from S1The shadow of farther relation
Ring coefficient smaller, by test of many times, selection weighting function isThis function takes in x=1 to x=6 interval
Value is respectively [0.9526,0.8808,0.7311,0.5,0.2689,0.1192], by the weighting after value standardization during x=1
Function isTherefore：
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5. a kind of enterprise's credit risk early-warning monitoring method according to claim 4, it is characterised in that in step s3,
For affiliated enterprise Sk, it is first obtained with depth-first traversal to main enterprise S1All associated paths, it is assumed that a total of m bars are closed
Join path Pk1, Pk2..., Pkm, then the factor of influence of affiliated enterprise to main enterprise is exactly maximum shadow in this m bar associated path
The factor is rung, i.e.,：
R(S1, Sk)=max { R (Pk1), R (Pk2) ..., R (Pkm)}。
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| CN201710459415.9A CN107292509A (en) | 2017-06-16 | 2017-06-16 | A kind of enterprise's credit risk early-warning monitoring method |
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| CN201710459415.9A CN107292509A (en) | 2017-06-16 | 2017-06-16 | A kind of enterprise's credit risk early-warning monitoring method |
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| CN201710459415.9A Pending CN107292509A (en) | 2017-06-16 | 2017-06-16 | A kind of enterprise's credit risk early-warning monitoring method |
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| CN109492945A (en) * | 2018-12-14 | 2019-03-19 | 深圳壹账通智能科技有限公司 | Business risk identifies monitoring method, device, equipment and storage medium |
| CN109740865A (en) * | 2018-12-13 | 2019-05-10 | 平安科技（深圳）有限公司 | Methods of risk assessment, system, equipment and storage medium |
| CN109829034A (en) * | 2018-08-24 | 2019-05-31 | 长威信息科技发展股份有限公司 | A kind of enterprise's tree spectrogram methods of exhibiting based on main market players's credit data |
| CN109934701A (en) * | 2019-03-12 | 2019-06-25 | 武汉灯塔之光科技有限公司 | A kind of methods of risk assessment and device of equity pledge |
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| CN110246032A (en) * | 2019-06-21 | 2019-09-17 | 深圳前海微众银行股份有限公司 | Risk monitoring and control method, apparatus and computer readable storage medium after loan |
| CN111191853A (en) * | 2020-01-06 | 2020-05-22 | 支付宝(杭州)信息技术有限公司 | Risk prediction method and device and risk query method and device |
| CN111241161A (en) * | 2020-01-16 | 2020-06-05 | 深圳壹账通智能科技有限公司 | Invoice information mining method and device, computer equipment and storage medium |
| CN111340611A (en) * | 2020-02-20 | 2020-06-26 | 中国建设银行股份有限公司 | Risk early warning method and device |
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| CN109829034A (en) * | 2018-08-24 | 2019-05-31 | 长威信息科技发展股份有限公司 | A kind of enterprise's tree spectrogram methods of exhibiting based on main market players's credit data |
| CN109740865A (en) * | 2018-12-13 | 2019-05-10 | 平安科技（深圳）有限公司 | Methods of risk assessment, system, equipment and storage medium |
| CN109492945A (en) * | 2018-12-14 | 2019-03-19 | 深圳壹账通智能科技有限公司 | Business risk identifies monitoring method, device, equipment and storage medium |
| CN109934701B (en) * | 2019-03-12 | 2021-02-19 | 武汉灯塔之光科技有限公司 | Risk assessment method and device for share right pledge |
| CN109934701A (en) * | 2019-03-12 | 2019-06-25 | 武汉灯塔之光科技有限公司 | A kind of methods of risk assessment and device of equity pledge |
| 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 |
| CN110246032A (en) * | 2019-06-21 | 2019-09-17 | 深圳前海微众银行股份有限公司 | Risk monitoring and control method, apparatus and computer readable storage medium after loan |
| CN111191853A (en) * | 2020-01-06 | 2020-05-22 | 支付宝(杭州)信息技术有限公司 | Risk prediction method and device and risk query method and device |
| CN111241161A (en) * | 2020-01-16 | 2020-06-05 | 深圳壹账通智能科技有限公司 | Invoice information mining method and device, computer equipment and storage medium |
| CN111340611A (en) * | 2020-02-20 | 2020-06-26 | 中国建设银行股份有限公司 | Risk early warning method and device |
| CN111340611B (en) * | 2020-02-20 | 2024-03-08 | 中国建设银行股份有限公司 | Risk early warning method and device |
| CN111553786A (en) * | 2020-04-24 | 2020-08-18 | 中金汇安(北京)科技有限公司 | Bank shareholder loan association transaction mining method and system based on graphic database |
| CN113780694A (en) * | 2020-06-10 | 2021-12-10 | 阿里巴巴集团控股有限公司 | Risk assessment method and device and electronic equipment |
| CN111784508A (en) * | 2020-07-01 | 2020-10-16 | 北京知因智慧科技有限公司 | Enterprise risk assessment method and device and electronic equipment |
| CN111858639A (en) * | 2020-07-30 | 2020-10-30 | 重庆富民银行股份有限公司 | External data management system and method for wind control management |
| CN111858639B (en) * | 2020-07-30 | 2023-06-09 | 重庆富民银行股份有限公司 | External data management system and method for wind control management |
| CN113283806A (en) * | 2021-06-22 | 2021-08-20 | 中国平安财产保险股份有限公司 | Enterprise information evaluation method and device, computer equipment and storage medium |
| CN115170271A (en) * | 2022-09-09 | 2022-10-11 | 中证数智科技(深圳)有限公司 | Clustering method, device, equipment and storage medium for risk associated enterprises |
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