CN107993144A - Customer risk grade determines method, apparatus, equipment and readable storage medium storing program for executing - Google Patents
Customer risk grade determines method, apparatus, equipment and readable storage medium storing program for executing Download PDFInfo
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- CN107993144A CN107993144A CN201711257684.3A CN201711257684A CN107993144A CN 107993144 A CN107993144 A CN 107993144A CN 201711257684 A CN201711257684 A CN 201711257684A CN 107993144 A CN107993144 A CN 107993144A
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Abstract
The present invention discloses a kind of customer risk grade and determines method, apparatus, equipment and readable storage medium storing program for executing, and the customer risk grade determines that method includes：When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine corresponding each risk class；Each risk class is compared, determines the highest level of each risk class risk grade, the corresponding risk class of highest level is determined as customer risk grade.The risk class that this programme passes through the attribute information of definite client, separate feature and integration information respectively, highest risk class in three risk class is determined as customer risk grade, make risk class evaluation more accurate, be conducive to financial institution and carry out comprehensively and accurately anatomy and the identification of risk to holding client.
Description
Technical field
The invention mainly relates to financial air control systems technology field, specifically, is related to a kind of customer risk grade and determines
Method, apparatus, equipment and readable storage medium storing program for executing.
Background technology
At present, financial institution passes through the directly qualitative client of the basic information of client when carrying out customer risk ranking
Risk class, the information for evaluating consumer's risk grade is single, grading means it is excessively rough.It cannot reflect that client is real
Risk situation, risk class evaluation is inaccurate, is unfavorable for financial institution and carries out comprehensively and accurately anatomy and risk to holding client
Identification.
The content of the invention
The main object of the present invention is to provide a kind of customer risk grade and determines method, apparatus, equipment and readable storage medium
Matter, it is intended to solve the problems, such as that customer risk ranking cannot accurately reflect customer risk situation in the prior art.
To achieve the above object, the present invention provides a kind of customer risk grade and determines method, and the customer risk grade is true
The method of determining comprises the following steps：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine to correspond to
Each risk class；
Each risk class is compared, the highest level of each risk class risk grade is determined, by highest level pair
The risk class answered is determined as customer risk grade.
Preferably, the reference information includes attribute reference information, things reference information and integrated reference information,
It is described to contrast the attribute information, separate feature and integration information with corresponding reference information respectively, determine
The step of corresponding each risk class, includes：
The attribute information and attribute reference information are contrasted, determine attribute risk class；
The separate feature and things reference information are contrasted, determine things risk class；
The integration information and integrated reference information are contrasted, determine integration risk class.
Preferably, it is described to contrast the integration information and integrated reference information, determine to wrap the step of integrating risk class
Include：
Determine multiple grading dimensions of client, with the corresponding multiple risk subitems of each grading dimension, with each risk
The corresponding multiple rating elements of item；
According to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, the integration of client is determined
Information；
The integration information and integrated reference information are contrasted, determine integration risk class.
Preferably, it is described according to multiple grading dimension, corresponding risk subitem and the corresponding rating elements, determine
The step of integration information of client, includes：
The scoring of rating element in each risk subitem is obtained, and highest grading of scoring in definite each risk subitem is wanted
Element, the score using the scoring corresponding to the highest rating element of scoring as risk subitem；
According to the score of each risk subitem corresponding with grading dimension, the score of grading dimension is determined, and comment according to each
The score of level dimension, determines the integration information of client.
Preferably, the score of the basis each risk subitem corresponding with grading dimension, determines the score of grading dimension, and
According to the score of each grading dimension, the step of integration information for determining client, includes：
Obtain the first weight of each grading dimension and the second weight of each risk subitem；
According to the second weight of each risk subitem and risk subitem score corresponding with the second weight, grading dimension is determined
Score；
According to the first weight of each grading dimension and grading dimension scores corresponding with the first weight, the product of client is determined
Divide information.
Preferably, it is described to contrast the integration information and integrated reference information, determine to wrap the step of integrating risk class
Include：
The integrating range of integration information and integrated reference information is contrasted, determines the integrating range where integration information；
According to the integrating range at place, integration risk class corresponding with the integrating range at place is determined.
Preferably, it is described when detecting up to default opportunity the step of before include：
Judge whether client has historical risk grade, when client has historical risk grade, according to historical risk etc.
Level determines default opportunity, and when client does not have historical risk grade, client is set on the opportunity of presetting.
In addition, to achieve the above object, the present invention also proposes a kind of customer risk grade determining device, the customer risk
Grade determining device includes：
Acquisition module, for when detecting up to default opportunity, obtaining the attribute information, separate feature and integration of client
Information；
Contrast module, for by the attribute information, separate feature and integration information respectively with corresponding reference information
Contrast, determines corresponding each risk class；
Determining module, for each risk class to be compared, determines the highest level of each risk class risk grade,
The corresponding risk class of highest level is determined as customer risk grade.
In addition, to achieve the above object, the present invention also proposes that a kind of customer risk grade determines equipment, the customer risk
Grade determines that equipment includes：Memory, processor, communication bus and the customer risk grade that is stored on the memory are true
Determine program；
The communication bus is used for realization the connection communication between processor and memory；
The processor determines program for performing the customer risk grade, to realize following steps：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine to correspond to
Each risk class；
Each risk class is compared, the highest level of each risk class risk grade is determined, by highest level pair
The risk class answered is determined as customer risk grade.
In addition, to achieve the above object, the present invention also provides a kind of readable storage medium storing program for executing, the readable storage medium storing program for executing storage
Have one either more than one program the one or more programs can be held by one or more than one processor
Row for：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine to correspond to
Each risk class；
Each risk class is compared, the highest level of each risk class risk grade is determined, by highest level pair
The risk class answered is determined as customer risk grade.
The customer risk grade of the present embodiment determines method, when detecting up to default opportunity, obtains the attribute letter of client
Breath, separate feature and integration information；And by the reference corresponding with its respectively of this attribute information, separate feature and integration information
Information contrasts, and determines each risk class of attribute information, separate feature and integration information；Each risk class is compared,
Determine the highest level of each risk class risk grade, the corresponding risk class of this highest level is determined as customer risk etc.
Level.This programme by determining the risk class of the attribute information of client, separate feature and integration information respectively, by three risks
Highest risk class is determined as customer risk grade in grade, makes risk class evaluation more accurate, is conducive to financial institution pair
Hold client and carry out comprehensively and accurately anatomy and the identification of risk.
Brief description of the drawings
Fig. 1 is that the customer risk grade of the present invention determines the flow diagram of method first embodiment；
Fig. 2 is that the customer risk grade of the present invention determines the flow diagram of method second embodiment；
Fig. 3 is the high-level schematic functional block diagram of the customer risk grade determining device first embodiment of the present invention；
Fig. 4 is the device structure schematic diagram for the hardware running environment that present invention method is related to；
Fig. 5 is that the customer risk grade of the present invention determines the schematic diagram data for being used to determine integration information of method；
Fig. 6 is that the customer risk grade of the present invention determines the schematic diagram data for being used to calculate grading dimension of method；
Fig. 7 is that the customer risk grade of the present invention determines that the changeable weight method of method calculates schematic diagram.
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.
Embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present invention provides a kind of customer risk grade and determines method.
Fig. 1 is refer to, Fig. 1 is the flow diagram that customer risk grade of the present invention determines method first embodiment.At this
In embodiment, the customer risk grade determines that method includes：
Step S10, when detecting up to default opportunity, obtains the attribute information, separate feature and integration information of client；
The customer risk grade of the present embodiment determines that be used for financial institution carries out risk class evaluation to its transacting customer, really
The risk class of its fixed transacting customer.When detecting up to default opportunity, attribute information, the separate feature of client in acquisition system
And integration information.Advance opportunity be pre-set need to carry out client in financial institution system risk class evaluation when
Between；Client attribute information characterization client belonging to attribute type, attribute type include blacklist attribute, risk case attribute,
Excessive risk event in Red List attribute, gray list attribute and white list attribute, wherein blacklist attribute, risk case attribute
And gray list attribute is excessive risk type, i.e., when client attribute information for this three classes attribute type for the moment, the wind of client
Dangerous grade is high-risk grade.Excessive risk event includes：Client is inquired about by public security organ, the tax authority or customs, freezes, detaining and draw
The situation of deposit；Identifying data or company's documentary evidence that client provides have the trace of forgery；The business that client claims has bright
Aobvious irrationality, there are indications that client may be engaged in improper or unlawful activities and the client investigated by the anti money washing of people's row
Deng.The dangerous things type of transaction or trading activity that separate feature is engaged in by client, peril type include
Transaction class, early warning class and report type, class of such as merchandising are received to public account private client in the revolution easy middle or short term Nei Xinkai of personal friendship
Accumulating sum of transferring accounts is more than 10,000,000, and the significantly suspicious early warning number of the property of clearing account in a short time of early warning class is 2 times
(clearing account defines above：Remaining sum is relatively low, concentrates generation substantial contribution to flow in and out, the fund residence time is short, inflow and outflow
The amount of money remains basically stable, and does not stay remaining sum or is produced after leaving certain proportion remaining sum, transition nature is obvious), report type short-term
An interior suspicious report early warning number is more than 1 grade.When client separate feature for this peril type for the moment, the wind of client
Dangerous grade is high-risk grade.Integration information calculates gained for the basic data according to user, transaction data etc. and is used to characterize use
The integrated value of family risk class.
In addition the attribute information of the present embodiment, separate feature are also determined by the basic data of user, transaction data, for true
This fixed attribute information, the basic data of separate feature and integrated value, transaction data may be from area data, name odd number
According to, business datum, anti money washing derivative data etc..Area data includes：Relevant department of State Council, the sanction of mechanism issue, embargo
Countries and regions, terroristic organization or support terrorist activity countries and regions；The United Nations issue sanction, embargo country and
Area, terroristic organization or the countries and regions for supporting terrorist activity；Lack country and the ground of anti money washing law and anti money washing supervision
Area, such as non-financial action ad hoc working group (FATF) member state；Traffic in drugs, corruption or other serious crime activity wildnesses country and
Area；The country of special financial supervision, such as tax avoidance type offshore financial centre.List data includes：Related portion of State Council
Door, the terroristic organization of mechanism issue, terrorist's list；The wanted circular criminal of judicial authority's issue；State Administration of Foreign Exchange issues
Blacklist；The sanction list of the United Nations's issue；People's Bank of China requires the list of concern；Foreign dignitary list etc..Business
Data refer to client open an account in a bank, transacting business when the client's essential information, account, the business datum such as flowing water that are occurred.
Anti money washing derivative data refers to the suspicious transaction reporting data of wholesale of anti money washing reporting system.
Step S20, the attribute information, separate feature and integration information are contrasted with corresponding reference information respectively,
Determine corresponding each risk class；
Further, for customer risk for being embodied to the attribute information, separate feature and integration information of client etc.
Level is determined, and the present embodiment is provided with reference information corresponding with attribute information, separate feature, integration information, objective obtaining
After the attribute information at family, separate feature and integration information, by this attribute information, separate feature, integration information and corresponding ginseng
Examine information to be contrasted, obtain comparing result, each risk class is determined according to comparing result.Wherein reference information includes attribute
Reference information, things reference information and integrated reference information；By the attribute information, separate feature and integration information respectively and
The contrast of corresponding reference information, the step of determining corresponding each risk class, include：
Step S21, the attribute information and attribute reference information are contrasted, determine attribute risk class；
Step S22, the separate feature and things reference information are contrasted, determine things risk class；
Step S23, the integration information and integrated reference information are contrasted, and determine integration risk class.
Specifically, set a property attribute information reference information, sets things reference information to separate feature, integration is believed
Breath sets integrated reference information；Attribute information and attribute reference information are contrasted, determine attribute risk class；By separate feature and
Things reference information contrasts, and determines things risk class；Integration information and integrated reference information are contrasted, determine integration risk etc.
Level.Risk class is divided into five ranks by the present embodiment：Low-risk, compared with low-risk, risk, high risk, excessive risk.It is low
Risk represents to open an account complete data, and the complete data that transacting business provides is detailed, funds transaction and client identity, financial situation,
Management functions is consistent, temporarily without reporting suspicious transaction.Complete data of opening an account is represented compared with low-risk, and the data that transacting business provides is complete
Whole, there are risk factors or client trading abnormal conditions occurs for customer data, temporarily without reporting suspicious transaction.Risk represents to open
Family complete data, the complete data that transacting business provides, customer data and transaction there are risk factors or reported and submitted suspicious transaction
Report.High risk represents that the identity information of client, business activities degree of risk are higher, funds transaction and client identity, finance
Situation, management functions are not consistent, and report and submit suspicious transaction reporting.Excessive risk represents the identity information of client, business activities risk journey
Degree is higher, and funds transaction is not substantially inconsistent with client identity, financial situation, management functions, suspicious transaction frequently occurs or by people
The regulators such as people bank or judicial authority carry out anti money washing investigation or pipe off.For example, attribute reference information is set
For blacklist and white list, excessive risk and low-risk are corresponded to respectively；Things reference information is arranged to early warning class and transaction class, right
Answer excessive risk；Integrated reference information is arranged to different integrating ranges, corresponding different risk class.By the attribute information of client
Contrasted with attribute reference information, if its attribute information is matched with blacklist, can determine that attribute risk class is excessive risk；To
Separate feature and things the reference information contrast of client, if its separate feature is matched with early warning class, can determine that things risk etc.
Level is excessive risk；Contrasted by the integration information of client and integrated reference information, if the integrated area of its integration information and low-risk
Between match, then can determine that integration risk class be low-risk.
Step S30, each risk class is compared, and determines the highest level of each risk class risk grade, will most
High-level corresponding risk class is determined as customer risk grade.
Further, after the risk class of attribute information, separate feature and integration information is determined, by each risk etc.
Level is compared, and determines the highest level of each risk class risk grade, and the corresponding risk class of this highest level is true
It is set to customer risk grade.If the corresponding attribute risk class of attribute information is low-risk, the corresponding things risk of separate feature
Grade is risk, and the corresponding integration risk class of integration information is high risk, corresponding risk of integration information etc. in three
Level rank highest, so that this integration risk class is determined as customer risk grade, i.e. the risk class of client is high risk.
In addition it is excessive risk that can directly be defined the level according to the transaction or trading activity of client in view of attribute information and separate feature, such as
When client has blacklist attribute, then directly deciding grade and level is excessive risk.It can determine that so as to work as by attribute information or separate feature
When the risk class of client is excessive risk, then the risk class for directly judging client is high-risk grade, and without other wind
Dangerous grade determines.When by attribute information, to determine client be high-risk grade, it is not necessary to carry out separate feature and integration is believed
The risk class of breath determines, saves and determines flow, accelerates the definite efficiency of customer risk grade.
The customer risk grade of the present embodiment determines method, when detecting up to default opportunity, obtains the attribute letter of client
Breath, separate feature and integration information；And by the reference corresponding with its respectively of this attribute information, separate feature and integration information
Information contrasts, and determines each risk class of attribute information, separate feature and integration information；Each risk class is compared,
Determine the highest level of each risk class risk grade, the corresponding risk class of this highest level is determined as customer risk etc.
Level.This programme by determining the risk class of the attribute information of client, separate feature and integration information respectively, by three risks
Highest risk class is determined as customer risk grade in grade, makes risk class evaluation more accurate, is conducive to financial institution pair
Hold client and carry out comprehensively and accurately anatomy and the identification of risk.
Further, it is described to believe the integration in customer risk grade of the present invention determines another embodiment of method
Breath and the contrast of integrated reference information, determine to include the step of integrating risk class：
Step S231, determine multiple grading dimensions of client, with the corresponding multiple risk subitems of each grading dimension, with it is each
The corresponding multiple rating elements of a risk subitem；
Further, determine integration risk class when, it is necessary to first determine client integration information, and the integration of client believe
Breath is determined by multiple dimensions.Fig. 5 is refer to, this is included the much information of client by the present embodiment, and it is different to embody client comprehensively
For the dimension of feature as grading dimension, it includes client characteristics, region risk, financial business and industry four.Each grading dimension
Degree includes multiple risk subitems again, as client characteristics include six risk subitems, respectively customer information extent of disclosure and effective
Property, with client establish or maintain the channels of business relations, client identity, client age, customer risk event and anti money washing to merchandise
Monitoring record.Each risk subitem includes multiple rating elements again, such as customer information extent of disclosure and the risk subitem of validity
Including two rating elements, respectively whether belong to non-resident and certificate expired time.Possible different financial institution, or it is right
In the different clients of finance, grading dimension differs.This grading dimension, corresponding risk subitem and corresponding grading can be wanted
Element is arranged to template, when it needs to be determined that integrating risk class, this three information is read from template, determine that the multiple of client comment
Level dimension, with each corresponding multiple risk subitems of dimension and multiple rating elements corresponding with each risk subitem of grading.
Step S232, according to multiple grading dimension, corresponding risk subitem and the corresponding rating elements, determines visitor
The integration information at family；
Understandably, because rating element is related to the essential information and behavioural information of client, so as to according to different basic
Information, behavioural information set different score values, the risk size of client are characterized with different score values, such as rating element：It is
It is no to belong to non-resident one, the non-resident score value different with non-resident setting is not belonging to can be belonged to client, wherein because client belongs to
When non-resident possessed Hazard ratio be not belonging to it is non-resident possessed by risk it is high, set so that non-resident score value will be belonged to
Score value than being not belonging to non-resident is high, big to characterize its risk.By judging the score value of each rating element belonging to client,
Determine the score of corresponding risk subitem and dimension of grading, the integration information of final definite client.Specifically, according to multiple gradings
The step of dimension, corresponding risk subitem and corresponding rating element, the integration information for determining client, includes：
Step q1, obtains the scoring of rating element in each risk subitem, and it is highest to determine to score in each risk subitem
Rating element, the score using the scoring corresponding to the highest rating element of scoring as risk subitem；
Further, it is corresponding with this rating element according to client because each rating element has multiple and different score values
Information, determines the scoring of its this rating element.For different rating elements, because the difference of customer information has different comment
Point, the higher item of scoring then illustrates that client has larger risk in this rating element item.Obtain included in each risk subitem
All essential elements of evaluation scoring, and determine to score highest rating element in each risk subitem, this highest grading of scoring
Key element is to embody the risk of client's maximum, the score using this corresponding scoring of scoring key element as risk subitem, to embody risk
The size of subitem risk.Such as risk subitem：Customer information extent of disclosure and validity, its rating element included：Whether belong to
Non-resident and certificate expired time.Wherein rating element：Whether belong to non-resident, the score value for being takes 5, no score value to take 2；Certificate
Expired time：The score value of 0 month takes the score value of 2,0-3 months to take the score value of more than 4,3 months to take 5.Know from customer information
Client belongs to non-resident, and certificate expired time is 2 months, so that whether rating element, which belongs to non-resident item, is scored at 5, card
Part expired time item is scored at 4.Whether the highest rating element that scores in such risk subitem is belongs to non-resident item, by this
Item 5 score as risk subitem of corresponding scoring, i.e. risk subitem customer information extent of disclosure and validity are scored at 5
Point.Because risk subitem is numerous, step the score of each risk subitem can be determined one by one accordingly.
Step q2, according to the score of each risk subitem corresponding with grading dimension, the score of definite grading dimension, and according to
The score of each grading dimension, determines the integration information of client.
Determine each risk subitem score after, because multiple risk subitems correspond to one grading dimension so that according to this
The score of the corresponding each risk subitem of dimension of grading, it may be determined that the score for dimension of grading, finally according to each grading dimension
Divide, determine the integration information of client.
Step S233, the integration information and integrated reference information are contrasted, and determine integration risk class.
Further, in order to which the customer risk size embodied to integration information is determined, this implementation is provided with integration
Reference information.After the integration information of client is determined, integration information and integrated reference information are contrasted, determine integration risk etc.
Level, its specific steps include：
Step p1, the integrating range of integration information and integrated reference information is contrasted, determines the integration where integration information
Section；
The integrated reference information of the present embodiment exists in the form of integrating range, belongs to the integrated reference of different integrating ranges
Information corresponds to different integration risk class.Such as [0,1] correspondence low risk level, [1,2] are corresponding compared with low risk level, [2,3]
Corresponding risk grade, [3,4] corresponding high risk grade, [4,5] correspond to high-risk grade.Obtained according to each grading dimension
Point, after the integration information for determining client, integration information exists in the form of integrated value.By integrated value and integrated reference information
Integrating range contrast, determine the integrating range where integrated value, with according to integrating range determine integration risk class.
Step p2, according to the integrating range at place, determines integration risk class corresponding with the integrating range at place.
After the integrating range where determining integration information, according to this integrating range, you can determine and this integrating range pair
The integration risk class answered.Such as determine that integration information is 2.5, the integrating range where can determine that this integration information by contrast is
[2,3], therefore the corresponding risk class of integrating range is risk grade, so that it is determined that integration risk class is risk etc.
Level.Risk class setting identification symbol corresponding with its to integrating range can be additionally integrated, as section [0,1] setting identification accords with
1, it corresponds to low risk level；Section [1,2] setting identification symbol 2, it is corresponded to compared with low risk level；Section [2,3] setting identification
Symbol 3, it corresponds to risk grade；Section [3,4] setting identification symbol 4, it corresponds to high risk grade；Section [4,5] setting mark
Know symbol 5, it corresponds to high-risk grade.When definite integration information be 2.5, can determine that the integrated area where this integration information by contrast
Between be [2,3], obtain this section identifier 3, its corresponding risk class, i.e. apoplexy determined further according to this section identifier 3
Dangerous grade.
Further, in customer risk grade of the present invention determines another embodiment of method, the step q2 according to
The score of the corresponding each risk subitem of dimension of grading, determines the score of grading dimension, and according to the score of each grading dimension, really
Determining the integration information of client includes：
Step q2-1, obtains the first weight of each grading dimension and the second weight of each risk subitem；
Understandably, because grading dimension and risk subitem embody the basic information of client, behavioural information equivalent risk size, and
The influence of client different basic information item and behavioural information item to customer risk grade is in different size.In order to embody this influence
The difference of size, this implementation are respectively arranged with grading dimension and risk subitem the first weight and the second weight, difference grading dimension
Degree the first weighted value it is different, the second weighted value of different risk subitems is also different, with embody different basic information items and
Influence size of the behavioural information item to customer risk grade.The weighted value set for the big item of influence is big, and small for influencing
The weighted value that then sets of item it is smaller.The first weight of each grading dimension and the second weight of each risk subitem are obtained, with
Determine the influence of each first weight and the second weight to corresponding grading dimension and risk subitem.
Step q2-2, according to the second weight of each risk subitem and risk subitem score corresponding with the second weight, really
The score of accepted opinion level dimension；
After corresponding all second weights of each risk subitem are got, according to the second weight of this each risk subitem
And the corresponding score value of each risk subitem, determine the score of grading dimension.Specifically, Fig. 6 is refer to, with client characteristics
Exemplified by dimension of grading, its risk subitem includes：Customer information extent of disclosure and validity, establish or maintain business relations with client
Channel, natural person's client age, association situation, from trade area client.And the highest rating element that scores in each risk subitem
Corresponding scoring is respectively 2,3,0,3,1, i.e., customer information extent of disclosure and validity are scored at 2 points, are established with client
Or maintain business relations channel be scored at 3 points, natural person's client age be scored at 0 point, association situation be scored at 3
Divide, be scored at 1 point from trade area client.And the second weight of each risk subitem is respectively 10%, 20%, 20%, 20% and
10%；So as to the second weight according to this risk subitem and corresponding score, the scores of dimensions is calculated.Wherein using power
Weight method is calculated, and formula is：
Wherein a is the score of risk subitem, and p is the weight of risk subitem, and n is the quantity of risk subitem, and x is grading dimension
Score.
In view of the method for weighting when calculating, share weight to the risky subitem of institute, but client not necessarily handle at the same time it is all
Business, i.e., will not meet all risk subitems, when a certain risk subitem fraction of user is higher, and not meet other risk subitems
When, calculated according to the method for weighting, the branch that obtains for dimension of grading is diluted, it is impossible to reflects the real risk situation of client.So as to this reality
Apply example use changeable weight method by be scored at 0 risk subitem weight distribution to other scores not for 0 risk subitem, specifically
Fig. 7 is refer to, the mode of distribution includes two kinds of mean allocation and proportional assignment, so that the score of the grading dimension calculated is more
Accurately.
Step q2-3, according to it is each grading dimension the first weight and grading dimension scores corresponding with the first weight, really
Determine the integration information of client.
After the score of each grading dimension is determined, according to the first weight of each grading dimension and each grading dimension
Corresponding score value, calculates the integration information of client.The changeable weight of its Computational Methods and above-mentioned calculating dimensions score
Method is identical, and this will not be repeated here.The result for calculating gained is the integral result of client, again by changeable weight method by score
Weight distribution for 0 grading dimension is not 0 grading dimension to other scores so that the client's integration information calculated more subject to
Really.
Further, Fig. 2 is refer to, on the basis of customer risk grade of the present invention determines method first embodiment, is carried
Go out the second embodiment of the present invention, in a second embodiment, it is described when detecting up to default opportunity the step of before wrap
Include：
Step S40, judge client whether be with historical risk grade, when client has historical risk grade, according to
Historical risk grade determines default opportunity, makes when client does not have historical risk grade, client's preset time is set.
Understandably, the frequent customer of financial institution, its trading activity are the process of dynamic change, and the change of trading activity can
It can cause the change of customer risk grade, so as to need within a certain period of time to determine the risk class of client again.This
Certain time is determined that, when the current risk class of client is higher, its time determined again gets over by the current risk class of client
It is short.In addition financial institution can also increase client newly, for increasing client newly, then need to set this time.The present embodiment is true
Determine to need to be that frequent customer or new client judge to client before this time, because frequent customer carried out risk class evaluation,
So as to judge whether client has historical risk grade, when client has historical risk grade, then according to its historical risk etc.
Level determines default opportunity；When client does not have historical risk grade, client is set on the opportunity of presetting, this default opportunity is
For the time being determined to the risk class of client.When determine client for do not have historical risk grade new client
When, then set in the default opportunity as 10 working days established business relationship of its risk class；And when for historical risk
During the frequent customer of grade, then default opportunity is determined according to its current risk class, wherein when the current risk grade of frequent customer
For " excessive risk " when, then preset opportunity be apart from last time grade half a year on date；When the current risk grade of frequent customer is " higher wind
During danger ", then it is to grade at 1 year on date apart from last time to preset opportunity；When the current risk grade of frequent customer is " risk ", then
Default opportunity is to grade at 2 years on date apart from last time；When frequent customer current risk grade be " compared with low-risk ", " low-risk " when,
It is to grade at 3 years on date apart from last time then to preset opportunity.In addition when detecting that client changes important identity information, is related to authoritative media
Case report, its under one's name account be accused of money laundering suspicious transaction or receive administration, the judicial inquiry situations such as occur
When, it is triggered to up to default opportunity, carries out the evaluation of customer risk grade, ensures the risk class of definite client in time.
In addition, refer to Fig. 3, the present invention provides a kind of customer risk grade determining device, in customer risk of the present invention etc.
In level determining device first embodiment, the customer risk grade determining device includes：
Acquisition module 10, for when detecting up to default opportunity, obtaining the attribute information, separate feature and product of client
Divide information；
The customer risk grade of the present embodiment determines that be used for financial institution carries out risk class evaluation to its transacting customer, really
The risk class of its fixed transacting customer.When detecting up to default opportunity, the attribute letter of client in 10 acquisition system of acquisition module
Breath, separate feature and integration information.Advance opportunity needs to carry out risk etc. to client in financial institution system to pre-set
The time of level evaluation；Attribute type belonging to the attribute information characterization client of client, attribute type include blacklist attribute, risk
In event attribute, Red List attribute, gray list attribute and white list attribute, wherein blacklist attribute, risk case attribute
Excessive risk event and gray list attribute are excessive risk type, i.e. attribute information one of three classes attribute type for this as client
When, the risk class of client is high-risk grade.Excessive risk event includes：Client is looked into by public security organ, the tax authority or customs
Ask, freeze, detaining the situation for drawing deposit；Identifying data or company's documentary evidence that client provides have the trace of forgery；Client claims
Business there is obvious irrationality, there are indications that client may be engaged in improper or unlawful activities and by people's row anti money washing
Client of investigation etc..The dangerous things type of transaction or trading activity that separate feature is engaged in by client, dangerous thing
Species type includes transaction class, early warning class and report type, and class of such as merchandising receives private client in the revolution easy middle or short term Nei Xinkai of personal friendship
To public account transfer accumulating sum be more than 10,000,000, the significantly suspicious early warning of the property of clearing account in a short time of early warning class
Number is more than 2 times (clearing account definition：Remaining sum is relatively low, concentrates generation substantial contribution to flow in and out, the fund residence time
Short, the inflow and outflow amount of money remains basically stable, and does not stay remaining sum or is produced after leaving certain proportion remaining sum, transition nature is obvious), report
A suspicious report early warning number in a short time for class is more than 1 grade.When separate feature one of peril type for this of client
When, the risk class of client is high-risk grade.Integration information calculates gained for the basic data according to user, transaction data etc.
For characterizing the integrated value of consumer's risk grade.
In addition the attribute information of the present embodiment, separate feature are also determined by the basic data of user, transaction data, for true
This fixed attribute information, the basic data of separate feature and integrated value, transaction data may be from area data, name odd number
According to, business datum, anti money washing derivative data etc..Area data includes：Relevant department of State Council, the sanction of mechanism issue, embargo
Countries and regions, terroristic organization or support terrorist activity countries and regions；The United Nations issue sanction, embargo country and
Area, terroristic organization or the countries and regions for supporting terrorist activity；Lack country and the ground of anti money washing law and anti money washing supervision
Area, such as non-financial action ad hoc working group (FATF) member state；Traffic in drugs, corruption or other serious crime activity wildnesses country and
Area；The country of special financial supervision, such as tax avoidance type offshore financial centre.List data includes：Related portion of State Council
Door, the terroristic organization of mechanism issue, terrorist's list；The wanted circular criminal of judicial authority's issue；State Administration of Foreign Exchange issues
Blacklist；The sanction list of the United Nations's issue；People's Bank of China requires the list of concern；Foreign dignitary list etc..Business
Data refer to client open an account in a bank, transacting business when the client's essential information, account, the business datum such as flowing water that are occurred.
Anti money washing derivative data refers to the suspicious transaction reporting data of wholesale of anti money washing reporting system.
Contrast module 20, for the attribute information, separate feature and integration information to be believed with corresponding reference respectively
Breath contrast, determines corresponding each risk class；
Further, for customer risk for being embodied to the attribute information, separate feature and integration information of client etc.
Level is determined, and the present embodiment is provided with reference information corresponding with attribute information, separate feature, integration information, objective obtaining
After the attribute information at family, separate feature and integration information, contrast module 20 is by this attribute information, separate feature, integration information
Contrasted with corresponding reference information, obtain comparing result, each risk class is determined according to comparing result.Wherein with reference to letter
Breath includes attribute reference information, things reference information and integrated reference information；Contrast module 20 to attribute information, separate feature with
And integration information is contrasted with corresponding reference information respectively, the step of determining corresponding each risk class, includes：
The attribute information and attribute reference information are contrasted, determine attribute risk class；
The separate feature and things reference information are contrasted, determine things risk class；
The integration information and integrated reference information are contrasted, determine integration risk class.
Specifically, set a property attribute information reference information, sets things reference information to separate feature, integration is believed
Breath sets integrated reference information；Attribute information and attribute reference information are contrasted, determine attribute risk class；By separate feature and
Things reference information contrasts, and determines things risk class；Integration information and integrated reference information are contrasted, determine integration risk etc.
Level.Risk class is divided into five ranks by the present embodiment：Low-risk, compared with low-risk, risk, high risk, excessive risk.It is low
Risk represents to open an account complete data, and the complete data that transacting business provides is detailed, funds transaction and client identity, financial situation,
Management functions is consistent, temporarily without reporting suspicious transaction.Complete data of opening an account is represented compared with low-risk, and the data that transacting business provides is complete
Whole, there are risk factors or client trading abnormal conditions occurs for customer data, temporarily without reporting suspicious transaction.Risk represents to open
Family complete data, the complete data that transacting business provides, customer data and transaction there are risk factors or reported and submitted suspicious transaction
Report.High risk represents that the identity information of client, business activities degree of risk are higher, funds transaction and client identity, finance
Situation, management functions are not consistent, and report and submit suspicious transaction reporting.Excessive risk represents the identity information of client, business activities risk journey
Degree is higher, and funds transaction is not substantially inconsistent with client identity, financial situation, management functions, suspicious transaction frequently occurs or by people
The regulators such as people bank or judicial authority carry out anti money washing investigation or pipe off.For example, attribute reference information is set
For blacklist and white list, excessive risk and low-risk are corresponded to respectively；Things reference information is arranged to early warning class and transaction class, right
Answer excessive risk；Integrated reference information is arranged to different integrating ranges, corresponding different risk class.By the attribute information of client
Contrasted with attribute reference information, if its attribute information is matched with blacklist, can determine that attribute risk class is excessive risk；To
Separate feature and things the reference information contrast of client, if its separate feature is matched with early warning class, can determine that things risk etc.
Level is excessive risk；Contrasted by the integration information of client and integrated reference information, if the integrated area of its integration information and low-risk
Between match, then can determine that integration risk class be low-risk.
Determining module 30, for each risk class to be compared, determines the superlative degree of each risk class risk grade
Not, the corresponding risk class of highest level is determined as customer risk grade.
Further, after the risk class of attribute information, separate feature and integration information is determined, determining module 30 will
Each risk class is compared, and determines the highest level of each risk class risk grade, this highest level is corresponding
Risk class is determined as customer risk grade.If the corresponding attribute risk class of attribute information is low-risk, separate feature corresponds to
Things risk class be risk, the corresponding integration risk class of integration information is high risk, integration information pair in three
The risk class rank highest answered, so that this integration risk class to be determined as to the risk class of customer risk grade, i.e. client
For high risk.In addition consider that attribute information and separate feature can directly define the level according to the transaction or trading activity of client
For excessive risk, such as when client has blacklist attribute, then directly deciding grade and level is excessive risk.Pass through attribute information or things so as to work as
Information can determine that the risk class of client when being excessive risk, then the risk class for directly judging client is high-risk grade, without
Carry out determining for other risk class.When by attribute information, to determine client be high-risk grade, it is not necessary to carry out things letter
The risk class of breath and integration information determines, saves and determines flow, accelerates the definite efficiency of customer risk grade.
The customer risk grade determining device of the present embodiment, when detecting up to default opportunity, acquisition module 10 obtains visitor
Attribute information, separate feature and the integration information at family；And by contrast module 20 by this attribute information, separate feature and product
Divide information reference information contrast corresponding with its respectively, determine each risk of attribute information, separate feature and integration information etc.
Level；Each risk class is compared by determining module 30, determines the highest level of each risk class risk grade, by this highest
The corresponding risk class of rank is determined as customer risk grade.This programme by determining that the attribute information of client, things are believed respectively
The risk class of breath and integration information, is determined as customer risk grade by highest risk class in three risk class, makes
Risk class evaluation is more accurate, is conducive to financial institution and carries out comprehensively and accurately anatomy and the identification of risk to holding client.
With reference to Fig. 4, Fig. 4 is the device structure schematic diagram for the hardware running environment that present invention method is related to.
Customer risk grade of the embodiment of the present invention determines that equipment can be PC or smart mobile phone, tablet computer, electricity
The terminal devices such as philosophical works reader, pocket computer.
As shown in figure 4, the customer risk grade determines that equipment can include：Processor 1001, such as CPU, memory
1005, communication bus 1002.Wherein, the connection that communication bus 1002 is used for realization between processor 1001 and memory 1005 is led to
Letter.Memory 1005 can be high-speed RAM memory or the memory (non-volatile memory) of stabilization, example
Such as magnetic disk storage.Memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.
Alternatively, which determines that equipment can also include user interface, network interface, camera, RF
(Radio Frequency, radio frequency) circuit, sensor, voicefrequency circuit, WiFi module etc..User interface can include display
Shield (Display), input unit such as keyboard (Keyboard), optional user interface can also include standard wireline interface,
Wave point.Network interface can optionally include standard wireline interface and wireless interface (such as WI-FI interfaces).
It will be understood by those skilled in the art that the customer risk grade shown in Fig. 4 determines that device structure is not formed pair
Customer risk grade determines the restriction of equipment, can include components more more or fewer than diagram, or some components of combination, or
The different component arrangement of person.
As shown in figure 4, it can lead to as in a kind of memory 1005 of computer-readable storage medium including operating system, network
Letter module and customer risk grade determine program.Operating system be management and control customer risk grade determine device hardware and
The program of software resource, supports customer risk grade to determine the operation of program and other softwares and/or program.Network service mould
Block is used for realization the communication between each component in the inside of memory 1005, and determines other hardware in equipment with customer risk grade
Communicate between software.
Customer risk grade shown in Fig. 4 determines in equipment that processor 1001 is used to perform to store in memory 1005
Customer risk grade determine program, realize following steps：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine to correspond to
Each risk class；
Each risk class is compared, the highest level of each risk class risk grade is determined, by highest level pair
The risk class answered is determined as customer risk grade.
Further, the reference information includes attribute reference information, things reference information and integrated reference information,
It is described to contrast the attribute information, separate feature and integration information with corresponding reference information respectively, determine
The step of corresponding each risk class, includes：
The attribute information and attribute reference information are contrasted, determine attribute risk class；
The separate feature and things reference information are contrasted, determine things risk class；
The integration information and integrated reference information are contrasted, determine integration risk class.
Further, the described the step of integration information and integrated reference information are contrasted, determine to integrate risk class
Including：
Determine multiple grading dimensions of client, with the corresponding multiple risk subitems of each grading dimension, with each risk
The corresponding multiple rating elements of item；
According to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, the integration of client is determined
Information；
The integration information and integrated reference information are contrasted, determine integration risk class.
Further, it is described according to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, really
The step of integration information for determining client, includes：
The scoring of rating element in each risk subitem is obtained, and highest grading of scoring in definite each risk subitem is wanted
Element, the score using the scoring corresponding to the highest rating element of scoring as risk subitem；
According to the score of each risk subitem corresponding with grading dimension, the score of grading dimension is determined, and comment according to each
The score of level dimension, determines the integration information of client.
Further, the score of the basis each risk subitem corresponding with grading dimension, determines the score of grading dimension,
And included according to the score of each grading dimension, the step of integration information for determining client：
Obtain the first weight of each grading dimension and the second weight of each risk subitem；
According to the second weight of each risk subitem and risk subitem score corresponding with the second weight, grading dimension is determined
Score；
According to the first weight of each grading dimension and grading dimension scores corresponding with the first weight, the product of client is determined
Divide information.
Further, the described the step of integration information and integrated reference information are contrasted, determine to integrate risk class
Including：
The integrating range of integration information and integrated reference information is contrasted, determines the integrating range where integration information；
According to the integrating range at place, integration risk class corresponding with the integrating range at place is determined.
Further, it is described when detecting up to default opportunity the step of before, processor 1001 is used to perform memory
The customer risk grade stored in 1005 determines program, realizes following steps：
Judge whether client has historical risk grade, when client has historical risk grade, according to historical risk etc.
Level determines default opportunity, and when client does not have historical risk grade, client is set on the opportunity of presetting.
Customer risk grade of the present invention determines that equipment embodiment determines each reality of method with above-mentioned customer risk grade
It is essentially identical to apply example, details are not described herein.
The present invention provides a kind of readable storage medium storing program for executing, the readable storage medium storing program for executing is stored with one or more than one journey
Sequence, the one or more programs can also be performed by one or more than one processor for：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determine to correspond to
Each risk class；
Each risk class is compared, the highest level of each risk class risk grade is determined, by highest level pair
The risk class answered is determined as customer risk grade.
Further, the reference information includes attribute reference information, things reference information and integrated reference information,
It is described to contrast the attribute information, separate feature and integration information with corresponding reference information respectively, determine
The step of corresponding each risk class, includes：
The attribute information and attribute reference information are contrasted, determine attribute risk class；
The separate feature and things reference information are contrasted, determine things risk class；
The integration information and integrated reference information are contrasted, determine integration risk class.
Further, the described the step of integration information and integrated reference information are contrasted, determine to integrate risk class
Including：
Determine multiple grading dimensions of client, with the corresponding multiple risk subitems of each grading dimension, with each risk
The corresponding multiple rating elements of item；
According to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, the integration of client is determined
Information；
The integration information and integrated reference information are contrasted, determine integration risk class.
Further, it is described according to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, really
The step of integration information for determining client, includes：
The scoring of rating element in each risk subitem is obtained, and highest grading of scoring in definite each risk subitem is wanted
Element, the score using the scoring corresponding to the highest rating element of scoring as risk subitem；
According to the score of each risk subitem corresponding with grading dimension, the score of grading dimension is determined, and comment according to each
The score of level dimension, determines the integration information of client.
Further, the score of the basis each risk subitem corresponding with grading dimension, determines the score of grading dimension,
And included according to the score of each grading dimension, the step of integration information for determining client：
Obtain the first weight of each grading dimension and the second weight of each risk subitem；
According to the second weight of each risk subitem and risk subitem score corresponding with the second weight, grading dimension is determined
Score；
According to the first weight of each grading dimension and grading dimension scores corresponding with the first weight, the product of client is determined
Divide information.
Further, the described the step of integration information and integrated reference information are contrasted, determine to integrate risk class
Including：
The integrating range of integration information and integrated reference information is contrasted, determines the integrating range where integration information；
According to the integrating range at place, integration risk class corresponding with the integrating range at place is determined.
Further, it is described when detecting up to default opportunity the step of before, the one or more programs
Can also be performed by one or more than one processor for：
Judge whether client has historical risk grade, when client has historical risk grade, according to historical risk etc.
Level determines default opportunity, and when client does not have historical risk grade, client is set on the opportunity of presetting.
Readable storage medium storing program for executing embodiment of the present invention determines that each embodiment of method is basic with above-mentioned customer risk grade
Identical, details are not described herein.
It should also be noted that, herein, term " comprising ", "comprising" or its any other variant are intended to non-
It is exclusive to include, so that process, method, article or device including a series of elements not only include those key elements,
But also including other elements that are not explicitly listed, or further include solid by this process, method, article or device
Some key elements.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including
Also there are other identical element in the process of the key element, method, article or device.
The embodiments of the present invention are for illustration only, do not represent the quality of embodiment.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on such understanding, technical scheme substantially in other words does the prior art
Going out the part of contribution can be embodied in the form of software product, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disc, CD), including some instructions use so that a station terminal equipment (can be mobile phone,
Computer, server, or network equipment etc.) perform method described in each embodiment of the present invention.
The foregoing is merely the preferred embodiment of the present invention, is not intended to limit the scope of the invention, every at this
Under the design of invention, the equivalent structure transformation made using description of the invention and accompanying drawing content, or directly/it is used in indirectly
His relevant technical field is included in the scope of patent protection of the present invention.
Claims (10)
1. a kind of customer risk grade determines method, it is characterised in that the customer risk grade determines that method includes following step
Suddenly：
When detecting up to default opportunity, the attribute information, separate feature and integration information of client are obtained；
The attribute information, separate feature and integration information are contrasted with corresponding reference information respectively, determined corresponding each
Risk class；
Each risk class is compared, determines the highest level of each risk class risk grade, highest level is corresponding
Risk class is determined as customer risk grade.
2. customer risk grade as claimed in claim 1 determines method, it is characterised in that the reference information is joined including attribute
Information, things reference information and integrated reference information are examined,
It is described to contrast the attribute information, separate feature and integration information with corresponding reference information respectively, determine to correspond to
Each risk class the step of include：
The attribute information and attribute reference information are contrasted, determine attribute risk class；
The separate feature and things reference information are contrasted, determine things risk class；
The integration information and integrated reference information are contrasted, determine integration risk class.
3. customer risk grade as claimed in claim 2 determines method, it is characterised in that described by the integration information and product
The step of dividing reference information contrast, determining integration risk class includes：
Determine multiple grading dimensions of client, with the corresponding multiple risk subitems of each grading dimension, with each risk subitem pair
The multiple rating elements answered；
According to multiple grading dimensions, corresponding risk subitem and corresponding rating elements, the integration information of client is determined；
The integration information and integrated reference information are contrasted, determine integration risk class.
4. customer risk grade as claimed in claim 3 determines method, it is characterised in that described to be tieed up according to multiple gradings
The step of degree, corresponding risk subitem and corresponding rating element, the integration information for determining client, includes：
The scoring of rating element in each risk subitem is obtained, and determines the highest rating element that scores in each risk subitem, will
Score of the scoring as risk subitem corresponding to the highest rating element of scoring；
According to the score of each risk subitem corresponding with grading dimension, the score of grading dimension is determined, and tieed up according to each grading
The score of degree, determines the integration information of client.
5. customer risk grade as claimed in claim 4 determines method, it is characterised in that the basis is corresponding with grading dimension
Each risk subitem score, determine the score of grading dimension, and according to the score of each grading dimension, determine the integration of client
The step of information, includes：
Obtain the first weight of each grading dimension and the second weight of each risk subitem；
According to the second weight of each risk subitem and risk subitem score corresponding with the second weight, obtaining for grading dimension is determined
Point；
According to the first weight of each grading dimension and grading dimension scores corresponding with the first weight, determine that the integration of client is believed
Breath.
6. customer risk grade as claimed in claim 3 determines method, it is characterised in that described by the integration information and product
The step of dividing reference information contrast, determining integration risk class includes：
The integrating range of integration information and integrated reference information is contrasted, determines the integrating range where integration information；
According to the integrating range at place, integration risk class corresponding with the integrating range at place is determined.
7. the customer risk grade as described in claim 1 to 6 any one determines method, it is characterised in that described when detection
Include before the step of when reaching default opportunity：
Judge whether client has historical risk grade, it is true according to historical risk grade when client has historical risk grade
Surely opportunity is preset, when client does not have historical risk grade, client is set on the opportunity of presetting.
8. a kind of customer risk grade determining device, it is characterised in that the customer risk grade determining device includes：
Acquisition module, for when detecting up to default opportunity, obtaining the attribute information, separate feature and integration letter of client
Breath；
Contrast module, for the attribute information, separate feature and integration information to be contrasted with corresponding reference information respectively,
Determine corresponding each risk class；
Determining module, for each risk class to be compared, determines the highest level of each risk class risk grade, will most
High-level corresponding risk class is determined as customer risk grade.
9. a kind of customer risk grade determines equipment, it is characterised in that the customer risk grade determines that equipment includes：Storage
Device, processor, communication bus and the customer risk grade that is stored on the memory determine program；
The communication bus is used for realization the connection communication between processor and memory；
The processor determines program for performing the customer risk grade, to realize such as any one of claim 1-7 institutes
The step of customer risk grade stated determines method.
10. a kind of readable storage medium storing program for executing, it is characterised in that the computer class is read to be stored with customer risk grade on storage medium
Determine program, the customer risk grade determines to realize as any one of claim 1-7 when program is executed by processor
Customer risk grade the step of determining method.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| CN201711257684.3A CN107993144A (en) | 2017-11-30 | 2017-11-30 | Customer risk grade determines method, apparatus, equipment and readable storage medium storing program for executing |
| PCT/CN2018/075210 WO2019104868A1 (en) | 2017-11-30 | 2018-02-05 | Client risk level determining method, device and equipment, and readable storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201711257684.3A CN107993144A (en) | 2017-11-30 | 2017-11-30 | Customer risk grade determines method, apparatus, equipment and readable storage medium storing program for executing |
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| CN109377399A (en) * | 2018-12-17 | 2019-02-22 | 泰康保险集团股份有限公司 | Risk analysis method, medium and electronic equipment for insurance products air control |
| CN109656904A (en) * | 2018-11-13 | 2019-04-19 | 上海百事通信息技术股份有限公司 | A kind of case risk checking method and system |
| CN109949154A (en) * | 2018-12-17 | 2019-06-28 | 深圳平安综合金融服务有限公司 | Customer information classification method, device, computer equipment and storage medium |
| CN110147998A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Client's networking processing method, device, computer equipment and storage medium |
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| CN113191890A (en) * | 2021-05-27 | 2021-07-30 | 中国工商银行股份有限公司 | Client risk determination method, device and equipment |
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Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040267660A1 (en) * | 2003-02-21 | 2004-12-30 | Automated Financial Systems, Inc. | Risk management system |
| US9454785B1 (en) * | 2015-07-30 | 2016-09-27 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
| CN105243479A (en) * | 2015-09-28 | 2016-01-13 | 四川长虹电器股份有限公司 | Risk judgment method and data processing system |
| CN105225151A (en) * | 2015-11-10 | 2016-01-06 | 中国建设银行股份有限公司 | A kind of bank lending risks method for early warning and device |
| CN106897929A (en) * | 2017-02-23 | 2017-06-27 | 上海喆之信息科技有限公司 | A kind of personal finance credit system |
-
2017
- 2017-11-30 CN CN201711257684.3A patent/CN107993144A/en active Pending
-
2018
- 2018-02-05 WO PCT/CN2018/075210 patent/WO2019104868A1/en active Application Filing
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110472803A (en) * | 2018-05-09 | 2019-11-19 | 腾讯科技（深圳）有限公司 | The vehicles take code issued rights configuration method, device and server |
| CN110858342A (en) * | 2018-08-22 | 2020-03-03 | 银联数据服务有限公司 | Method and device for quantifying customer value of credit card |
| CN109656904A (en) * | 2018-11-13 | 2019-04-19 | 上海百事通信息技术股份有限公司 | A kind of case risk checking method and system |
| CN109656904B (en) * | 2018-11-13 | 2023-05-30 | 上海百事通信息技术股份有限公司 | Case risk detection method and system |
| CN109377399A (en) * | 2018-12-17 | 2019-02-22 | 泰康保险集团股份有限公司 | Risk analysis method, medium and electronic equipment for insurance products air control |
| CN109949154A (en) * | 2018-12-17 | 2019-06-28 | 深圳平安综合金融服务有限公司 | Customer information classification method, device, computer equipment and storage medium |
| CN110147998A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Client's networking processing method, device, computer equipment and storage medium |
| CN110197397A (en) * | 2019-05-15 | 2019-09-03 | 无线生活(北京)信息技术有限公司 | The partitioning method and device of node level |
| CN111539003A (en) * | 2020-04-20 | 2020-08-14 | 中安龙源(北京)科技发展有限公司 | Social unit safety information metadata system implementation method |
| CN113191890A (en) * | 2021-05-27 | 2021-07-30 | 中国工商银行股份有限公司 | Client risk determination method, device and equipment |
| CN116012127A (en) * | 2022-12-23 | 2023-04-25 | 深圳市雁联计算系统有限公司 | Risk scoring rating method and system for account |
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|---|---|
| WO2019104868A1 (en) | 2019-06-06 |
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