CN110390587A - A kind of customer evaluation method and system - Google Patents
A kind of customer evaluation method and system Download PDFInfo
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- CN110390587A CN110390587A CN201810347755.7A CN201810347755A CN110390587A CN 110390587 A CN110390587 A CN 110390587A CN 201810347755 A CN201810347755 A CN 201810347755A CN 110390587 A CN110390587 A CN 110390587A
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- 238000011156 evaluation Methods 0.000 title claims abstract description 24
- 238000013210 evaluation model Methods 0.000 claims abstract description 86
- 239000011159 matrix material Substances 0.000 claims description 79
- 238000000034 method Methods 0.000 claims description 29
- 238000012216 screening Methods 0.000 claims description 16
- 238000011161 development Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 7
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
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- 238000004900 laundering Methods 0.000 description 14
- 238000005406 washing Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000012954 risk control Methods 0.000 description 5
- 230000004913 activation Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013499 data model Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention discloses a kind of customer evaluation method and system, automatic realize assesses the transaction risk of client, improves assessment efficiency and assessment accuracy.The customer evaluation method, comprising: building evaluation model；Obtain customer transactional data；The transaction data includes the customer historical transaction data being stored in transaction data base；The customer transactional data is inputted in the evaluation model, obtains customer risk grade, and the customer risk grade is stored in customer database.
Description
Technical field
The invention belongs to big data technical fields, it particularly relates to a kind of customer evaluation method and system.
Background technique
Money laundering is the great economic crime of harmfulness.It is illegal point more and more with the fast development of internet finance
Son begins through Internet channel and carries out money-laundering.China established anti money washing system, Chinese people's silver comprehensively since 2003
3 command of row is even more to have done more specific requirement to anti money washing.Internet financial company needs to undertake more specifically responsibility and justice
Business.
It is the important content for strengthening Anti-Money Laundering that the assessment of client's money laundering risks and grade separation, which divide work,.In order to avoid
Money-laundering prevents money laundering risks, carries out money laundering risks assessment to each internet finance user and is very necessary.It is existing
In technology, risk assessment is carried out to client using manual type.Since user data is huge, client is carried out using manual type
Risk assessment, low efficiency, accuracy be not high.
Summary of the invention
The embodiment of the present invention provides a kind of customer evaluation method and system, and automatic realize comments the transaction risk of client
Estimate, improves assessment efficiency and assessment accuracy.
In order to solve the above technical problems, the embodiment of the present invention uses following technical scheme:
In a first aspect, the embodiment of the present invention provides a kind of customer evaluation method, comprising:
Construct evaluation model；
Obtain customer transactional data；The transaction data includes the customer historical number of deals being stored in transaction data base
According to；
The customer transactional data is inputted in the evaluation model, obtains customer risk grade, and by client's wind
Dangerous grade is stored in customer database.
With reference to first aspect, as the first achievable scheme, the building evaluation model includes:
Determine evaluation model variable；
Client's essential information data are acquired, and according to client's essential information data, screening and assessment model variable；
The weight coefficient of the evaluation model variable of calculating sifting；
According to the weight coefficient of the evaluation model variable of screening and evaluation model variable, evaluation model is established.
The achievable scheme of with reference to first aspect the first, as second of achievable scheme, the client is basic
Information data includes member's essential information, member's essential attribute, member's property of value, member's trading activity, individual member's label
Information.
The achievable scheme of with reference to first aspect the first, as the third achievable scheme, the calculating sifting
Evaluation model variable weight coefficient, comprising:
Development of judgment matrix, between the variable and its dependent variable in element representation evaluation model in the judgment matrix
The relative importance compared two-by-two；
Consistency check is carried out to the judgment matrix；If the judgment matrix is with uniformity, pass through the judgement
The weight coefficient of matrix Calculation Estimation model variable.
The third achievable scheme with reference to first aspect, it is described to sentence to described as the 4th kind of achievable scheme
Disconnected matrix carries out consistency check, comprising:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I.；
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistent
Property；If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
The achievable scheme of with reference to first aspect the first, as the 5th kind of achievable scheme, the evaluation model
As shown in formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient
Meet:
With reference to first aspect, described that the customer transactional data is inputted into institute's commentary as the 6th kind of achievable scheme
In valence model, customer risk grade is obtained, comprising:
The customer transactional data is inputted in the evaluation model, customer risk scoring is obtained；
Criterion score is converted by customer risk scoring；The criterion score is located in the scoring section of setting；
According to customer risk classification standard and the criterion score, customer risk grade is obtained.
Second aspect, the embodiment of the present invention also provide a kind of customer evaluation system, the system comprises:
Construct module: for constructing evaluation model；
Transaction data obtains module: for obtaining customer transactional data；The transaction data includes being stored in transaction data
Customer historical transaction data in library；
Risk class obtains module: for inputting the customer transactional data in the evaluation model, obtaining client's wind
Dangerous grade, and the customer risk grade is stored in customer database.
In conjunction with second aspect, as the first achievable scheme, the building module includes:
Determination unit: for determining evaluation model variable；
Screening unit: for acquiring client's essential information data, and according to client's essential information data, screening and assessment
Model variable；
Computing unit: the weight coefficient of the evaluation model variable for calculating sifting；
Establish unit: for establishing evaluation according to the evaluation model variable of screening and the weight coefficient of evaluation model variable
Model.
In conjunction with the first achievable scheme of second aspect, as second of achievable scheme, the computing unit
Include:
It constructs subelement: being used for development of judgment matrix, the change in element representation evaluation model in the judgment matrix
The relative importance compared two-by-two between amount and its dependent variable；
Examine subelement: for carrying out consistency check to the judgment matrix；If the judgment matrix is with uniformity,
Then pass through the weight coefficient of the judgment matrix Calculation Estimation model variable.
In conjunction with second of achievable scheme of second aspect, as the third achievable scheme, the syndrome list
Member includes:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I.；
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistent
Property；If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
In conjunction with second of achievable scheme of second aspect, as the 4th kind of achievable scheme, the evaluation model
As shown in formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient
Meet:
In conjunction with the first achievable scheme of second aspect, as the 5th kind of achievable scheme, the risk class
Obtaining module includes:
Risk score acquiring unit: for inputting the customer transactional data in the evaluation model, client's wind is obtained
Danger scoring；
Conversion unit: for converting criterion score for customer risk scoring；
Risk class acquiring unit: for obtaining client's wind according to customer risk classification standard and the criterion score
Dangerous grade.
Compared with prior art, the customer evaluation method and system of the embodiment of the present invention can realize the friendship to client automatically
Easy risk is assessed, and assessment efficiency and assessment accuracy are improved.The method of the embodiment of the present invention is by the client trading number
According to inputting in the evaluation model, customer risk grade is obtained, and the customer risk grade is stored in customer database.
By the evaluation model of building, customer transactional data is analyzed, obtains customer risk grade.Risk class is higher, client
Transaction risk it is bigger.This method Utilization assessment model and transaction data, the transaction wind of more objective and comprehensive reflection client
Danger, improves assessment accuracy.Meanwhile this method also substantially increases assessment efficiency.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to needed in the embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the system architecture diagram of the embodiment of the present invention；
Fig. 2 is the method flow block diagram of the embodiment of the present invention；
Fig. 3 is the flow diagram of S10 in the method for the embodiment of the present invention；
Fig. 4 is evaluation model variable schematic diagram in the embodiment of the present invention；
Fig. 5 is the system structure diagram of the embodiment of the present invention.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, with reference to the accompanying drawing and specific embodiment party
Present invention is further described in detail for formula.Embodiments of the present invention are described in more detail below, the embodiment is shown
Example is shown in the accompanying drawings, and in which the same or similar labels are throughly indicated same or similar element or has identical or class
Like the element of function.It is exemplary below with reference to the embodiment of attached drawing description, for explaining only the invention, and cannot
It is construed to limitation of the present invention.Those skilled in the art of the present technique are appreciated that unless expressly stated, odd number shape used herein
Formula " one ", "one", " described " and "the" may also comprise plural form.It is to be further understood that specification of the invention
Used in wording " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that
In the presence of or add other one or more features, integer, step, operation, element, component and/or their group.It should be understood that
When we say that an element is " connected " or " coupled " to another element, it can be directly connected or coupled to other elements, or
There may also be intermediary elements.In addition, " connection " used herein or " coupling " may include being wirelessly connected or coupling.Here make
Wording "and/or" includes one or more associated any cells for listing item and all combinations.The art
Technical staff is appreciated that unless otherwise defined all terms (including technical terms and scientific terms) used herein have
Meaning identical with the general understanding of the those of ordinary skill in fields of the present invention.It should also be understood that such as general
Those terms, which should be understood that, defined in dictionary has a meaning that is consistent with the meaning in the context of the prior art, and
Unless defined as here, it will not be explained in an idealized or overly formal meaning.
The method of the embodiment of the present invention can be applied in system architecture as shown in Figure 1.The system includes evaluation service
Device, trading information data library and customer information database.Evaluation server obtains transaction data from trading information data library, and
Utilization assessment model calculates trading information data, obtains customer risk grade, and customer risk grade is stored in visitor
In the information database of family.
Evaluation server can be individual server apparatus, such as: rack, blade, tower or cabinet-type clothes
Business device equipment can also have stronger computing capability hardware device using work station, mainframe computer etc.；It is also possible to by multiple
The server cluster of server apparatus composition.Evaluation model is stored in evaluation server.
Trading information data library is for storing, managing transaction data, including exchange hour, transaction item, transaction amount, friendship
The information such as easy people.Customer information database for store, managing customer data, the risk class data including client.Database
It specifically can be and be individually made, the server apparatus of management, storage for data is also possible to by multiple server apparatus
The server cluster of composition.The database that corresponding server equipment is run on the hardware device of Database Systems, for managing
And the data of storage server equipment.Common network database (Network Database), relationship number can specifically be used
According to library (Relational Database), tree shaped data library (Hierarchical Database), object-oriented database
(Object-oriented Database) and big data system architecture of new generation.
A kind of customer evaluation method provided in an embodiment of the present invention, as shown in Figure 2, comprising:
S10 constructs evaluation model；
S20 obtains customer transactional data；
S30 inputs the customer transactional data in the evaluation model, obtains customer risk grade, and by the client
Risk class is stored in customer database.
The method of above-described embodiment is derived from the transaction data of client to the determination of customer risk grade.By by client trading
Data input in the evaluation model, obtain customer risk grade.It is different according to customer risk grade, different journeys are carried out to client
The monitoring of degree.Transaction data and evaluation model of this method based on client determine customer risk grade, more objective appraisal visitor
Family is conducive to transaction risk control.The method of above-described embodiment can be applied in anti money washing financial transaction risk control.By right
The confirmation of customer risk grade is monitored the financial transaction of key customer.
In the method for above-described embodiment, evaluation model is related to the standard of evaluation as the basis for determining customer risk grade
True property.As preference, as shown in figure 3, building evaluation model specifically includes:
S101 determines evaluation model variable；
S102 acquires client's essential information data, and according to client's essential information data, screening and assessment model variable；
The weight coefficient of the evaluation model variable of S103 calculating sifting；
S104 establishes evaluation model according to the evaluation model variable of screening and the weight coefficient of evaluation model variable.
In the method for above-mentioned building evaluation model, S102) in, client's essential information data include that member believes substantially
Breath, member's essential attribute, member's property of value, member's trading activity, individual member's label information.As technology develops, and
The variation of trading activity, the content that client's essential information data include also change therewith.Corresponding evaluation model variable, can also change
Become.
By taking anti money washing Financial Risk Control as an example, appraisement system includes client characteristics, region, business (containing financial product, gold
Melt service), industry (containing occupation) four class fundamentals.In combination with financing corporation's industry characteristic, type of service, scale of operation,
The actual conditions such as customer range decomposite the risk subitem that fundamental is included.Since the information in different data sources table is anti-
The different feature of client is reflected, from four class fundamentals, carries out the derivative and exploitation of variable.As shown in figure 4, evaluation model is total
Including 13 variables.Client characteristics dimension includes real-name authentication state, complete data degree, activation number of days and client age four changes
Amount.Wherein, real-name authentication state: for investigating client identity authentication state, for being vacancy for certification or authentication state
Member, award high marks number.Complete data degree: activation element for investigating customer accounting code it is whether complete (name, the birthday, gender,
Identification card number, binding cell-phone number, address, E-mail address etc.), for the client that input data is not complete, award high marks number.Activation
Number of days: the length of time to Activate Account for investigating client in platform, activation date distance is closer, then score is relatively higher.Visitor
The family age: for investigating the age of client enrollment account, the age surmounts the client of normal range (NR), and score is higher.Region dimension packet
The variable of counties and cities three where city, identity card where including account ownership place, identity card.Wherein, account ownership place: for investigating visitor
Whether the address of family account is in high risk zone, belongs to, and award high marks number.City where identity card: for investigating client's body
Whether part card institute possession is in high risk city, belongs to, award high marks number.Counties and cities where identity card: for investigating client identity
Whether card institute possession is in high risk zone, belongs to, award high marks number.Transaction risk dimension includes frequently transaction, huge friendship
Easily, three variables of malicious registration and ox.Wherein, frequently transaction: using a period of time as the time limit, the transaction of most people is counted
Frequency, for exceeding the client of arm's length dealing frequency, then award high marks number.Block trade: using a period of time as the time limit, monitoring visitor
Transaction amount situation of the family account on platform, for the account for exceeding the arm's length dealing amount of money, award high marks number.Malicious registration and
Ox: based on the preliminary analysis to client, it is confirmed as the client of malicious registration, ox, their trading activity is given height
Score.Row (duty) industry risk includes high risk industry, three high risk occupation, business and the cash degree of correlation variables.Wherein,
High risk industry: in money laundering case over the years, it is acknowledged as the industry with high risk, belongs to the visitor of high risk industry
Family, award high marks number.Such as entertain also, trust sectors etc..High risk occupation: in money laundering case over the years, what is counted is high-risk
Occupation, if client has similar professional background, award high marks number.Such as lottery industry, performer etc..Business is related to cash
Degree: the client on platform, the client for having substantial contribution to pass in and out in a short time, award high marks number.
Certainly, with the development of technology and the variation of trading activity, above-mentioned 13 variables are adjustable, modification.
As preference, step S103 includes:
S1031 development of judgment matrix, a variable in element representation evaluation model and other changes in the judgment matrix
The relative importance compared two-by-two between amount.Judgment matrix is as follows:
Wherein, aijIndicate AiWith AjCompared to when quantized value.AiIndicate i-th of variable in evaluation model, AjIndicate evaluation mould
J-th of variable in type, 1≤i≤n, 1≤j≤n.Quantized value can be determined according to table 1.The number of first row in table 1, i.e., 1,3,
5,7,9 be quantized value when comparing two variable importances, that is to say scale value.
Table 1
| 1 | It indicates that two elements are compared, there is no less important |
| 3 | Indicate that two elements are compared, element ratio another element is slightly important |
| 5 | Indicate that two elements are compared, element ratio another element is obviously important |
| 7 | Indicate that two elements are compared, element ratio another element is strongly important |
| 9 | Indicate that two elements are compared, element ratio another element is extremely important |
S1032 carries out consistency check to the judgment matrix；If the judgment matrix is with uniformity, by described
Judgment matrix, the weight coefficient of Calculation Estimation model variable.
In above-mentioned steps, consistency check is carried out to the judgment matrix, comprising:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I..Index R.I. is to be repeated several times to carry out random judgment matrix characteristic value
It calculates, takes arithmetic average.As an example, following table provides the mean random one that 1~10 dimension judgment matrix computes repeatedly 1000 times
Cause property index:
| Dimension | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| R.I. | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 |
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistent
Property；If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
Pass through the judgment matrix, the weight coefficient of Calculation Estimation model variable.
In above-described embodiment, shown in the evaluation model such as formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient
Meet:
As preference, in step S20, the transaction data includes the customer historical transaction being stored in transaction data base
Data.Customer transactional data is transferred from transaction data base.
As preference, the step S30 includes:
S301 inputs the customer transactional data in the evaluation model, obtains customer risk scoring；
Customer risk scoring is converted criterion score by S302；The criterion score is located at the scoring section of setting
It is interior；
S303 obtains customer risk grade according to customer risk classification standard and the criterion score.
In above-described embodiment, scoring section can be 1-1000.Criterion score is converted by customer risk scoring, just
It sorts in client, criterion score is higher, and transaction risk is bigger.
In S303, the customer risk classification standard is carried out according to the historical standard score proportion of all clients
It divides, as shown in the table.
1 risk class tablet of table
It, can be with early warning for the highest risk and high risk in table 1.Specifically, step S30 further include:
Client S304 high for customer risk grade transfers from transaction data base and shows the transaction letter of the client
Breath, transfers from customer database and shows the essential information of the client.
To the client of high-risk grade, system transfers pertinent customer information and historical transactional information, and and monitor in time
Member consults relevant information.
The method of above-described embodiment combines big data technology and financial transaction service, realizes to customer evaluation, with pre-
The progress of the illegal financial transaction such as anti-money laundering.The method of above-described embodiment is scientific, objective in the application of financial anti-money laundering field
It sees, the money laundering risks of comprehensive reaction client provide method.This method is quoted in financial anti-money laundering field, can be made up in backwash
Blank in terms of money client risk evaluation facilitates anti money washing business personnel with reference to risk class, fast and accurately identifies anti money washing
Client improves the efficiency of anti money washing analysis.
The method of above-described embodiment can quickly filter out suspicious high risk client in the application of financial anti-money laundering field,
Efficiency is improved for anti money washing client's screening operation, avoids the inefficient work for the formula of looking for a needle in a haystack.In addition, in evaluation model
Variable can be arranged with the variation of technology development and trading activity, and evaluation model is enabled accurately to calculate customer risk etc.
Grade.
As shown in figure 5, the embodiment of the present invention also provides a kind of customer evaluation system, the system comprises:
Construct module: for constructing evaluation model；
Transaction data obtains module: for obtaining customer transactional data；The transaction data includes being stored in transaction data
Customer historical transaction data in library；
Risk class obtains module: for inputting the customer transactional data in the evaluation model, obtaining client's wind
Dangerous grade, and the customer risk grade is stored in customer database.
The system of above-described embodiment is derived from the transaction data of client to the determination of customer risk grade.By by client trading
Data input in the evaluation model, obtain customer risk grade.It is different according to customer risk grade, different journeys are carried out to client
The monitoring of degree.Transaction data and evaluation model of the system based on client determine customer risk grade, more objective appraisal visitor
Family is conducive to transaction risk control.The system of above-described embodiment can be applied in anti money washing financial transaction risk control.By right
The confirmation of customer risk grade is monitored the financial transaction of key customer.
As preference, the building module includes:
Determination unit: for determining evaluation model variable；
Screening unit: for acquiring client's essential information data, and according to client's essential information data, screening and assessment
Model variable；
Computing unit: the weight coefficient of the evaluation model variable for calculating sifting；
Establish unit: for establishing evaluation according to the evaluation model variable of screening and the weight coefficient of evaluation model variable
Model.
As preference, the computing unit includes:
It constructs subelement: being used for development of judgment matrix, the change in element representation evaluation model in the judgment matrix
The relative importance compared two-by-two between amount and its dependent variable；
Examine subelement: for carrying out consistency check to the judgment matrix；If the judgment matrix is with uniformity,
Then pass through the weight coefficient of the judgment matrix Calculation Estimation model variable.
Wherein, subelement is examined to specifically include:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I.；
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistent
Property；If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
Shown in the evaluation model such as formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient
Meet:
As preference, the risk class obtains module and includes:
Risk score acquiring unit: for inputting the customer transactional data in the evaluation model, client's wind is obtained
Danger scoring；
Conversion unit: for converting criterion score for customer risk scoring；
Risk class acquiring unit: for obtaining client's wind according to customer risk classification standard and the criterion score
Dangerous grade.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for equipment reality
For applying example, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to embodiment of the method
Part explanation.
Those skilled in the art should know, realize the method or system of above-described embodiment, can pass through computer journey
Sequence instructs to realize.The computer program instructions are loaded into programmable data processing device, such as computer, thus that can compile
Corresponding instruction is executed on journey data processing equipment, for realizing the function of method or the system realization of above-described embodiment.
Those skilled in the art can carry out non-creative technological improvement according to above-described embodiment to the application, without
It is detached from Spirit Essence of the invention.These improvement still should be regarded as within the protection scope of the claim of this application.
Claims (13)
1. a kind of customer evaluation method, which is characterized in that the described method includes:
Construct evaluation model；
Obtain customer transactional data；The transaction data includes the customer historical transaction data being stored in transaction data base；
The customer transactional data is inputted in the evaluation model, obtains customer risk grade, and by described customer risk etc.
Grade is stored in customer database.
2. the method according to claim 1, wherein the building evaluation model includes:
Determine evaluation model variable；
Client's essential information data are acquired, and according to client's essential information data, screening and assessment model variable；
The weight coefficient of the evaluation model variable of calculating sifting；
According to the weight coefficient of the evaluation model variable of screening and evaluation model variable, evaluation model is established.
3. according to the method described in claim 2, it is characterized in that, client's essential information data include that member believes substantially
Breath, member's essential attribute, member's property of value, member's trading activity, individual member's label information.
4. according to the method described in claim 2, it is characterized in that, the weight system of the evaluation model variable of the calculating sifting
Number, comprising:
Development of judgment matrix, between the variable and its dependent variable in element representation evaluation model in the judgment matrix two-by-two
The relative importance compared；
Consistency check is carried out to the judgment matrix；If the judgment matrix is with uniformity, pass through the judgment matrix
The weight coefficient of Calculation Estimation model variable.
5. according to the method described in claim 4, it is characterized in that, described carry out consistency check, packet to the judgment matrix
It includes:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I.；
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistency；
If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
6. according to the method described in claim 2, it is characterized in that, shown in the evaluation model such as formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient meets:
7. the method according to claim 1, wherein described input the evaluation mould for the customer transactional data
In type, customer risk grade is obtained, comprising:
The customer transactional data is inputted in the evaluation model, customer risk scoring is obtained；
Criterion score is converted by customer risk scoring；The criterion score is located in the scoring section of setting；
According to customer risk classification standard and the criterion score, customer risk grade is obtained.
8. a kind of customer evaluation system, which is characterized in that the system comprises:
Construct module: for constructing evaluation model；
Transaction data obtains module: for obtaining customer transactional data；The transaction data includes being stored in transaction data base
Customer historical transaction data；
Risk class obtains module: for inputting the customer transactional data in the evaluation model, obtaining customer risk etc.
Grade, and the customer risk grade is stored in customer database.
9. system according to claim 8, which is characterized in that the building module includes:
Determination unit: for determining evaluation model variable；
Screening unit: for acquiring client's essential information data, and according to client's essential information data, screening and assessment model
Variable；
Computing unit: the weight coefficient of the evaluation model variable for calculating sifting；
Establish unit: for establishing evaluation model according to the evaluation model variable of screening and the weight coefficient of evaluation model variable.
10. system according to claim 9, which is characterized in that the computing unit includes:
Construct subelement: being used for development of judgment matrix, a variable in element representation evaluation model in the judgment matrix and
The relative importance compared two-by-two between its dependent variable；
Examine subelement: for carrying out consistency check to the judgment matrix；If the judgment matrix is with uniformity, lead to
Cross the weight coefficient of the judgment matrix Calculation Estimation model variable.
11. system according to claim 10, which is characterized in that the inspection subelement includes:
Coincident indicator C.I. is calculated according to formula (1):
Wherein, λmaxIndicate that the Maximum characteristic root of judgment matrix, n indicate judgment matrix order；
Calculate Aver-age Random Consistency Index R.I.；
Consistency ration C.R. is calculated according to formula (2):
C.R.=C.I./R.I. formula (2)
As C.R. < 0.1, then judgment matrix is with uniformity；As C.R. >=0.1, then judgment matrix does not have consistency；
If judgment matrix does not have consistency, the parameter in judgment matrix is adjusted, until judgment matrix is with uniformity.
12. system according to claim 10, which is characterized in that shown in the evaluation model such as formula (3):
Y=w1A1+w2A2+…+wnAnFormula (3)
A1、A2、…、AnIndicate the variable of evaluation model；w1、w2、…、wnIndicate the weight coefficient of each variable；Weight coefficient meets:
13. system according to claim 9, which is characterized in that the risk class obtains module and includes:
Risk score acquiring unit: it for inputting the customer transactional data in the evaluation model, obtains customer risk and comments
Point；
Conversion unit: for converting criterion score for customer risk scoring；
Risk class acquiring unit: for obtaining customer risk etc. according to customer risk classification standard and the criterion score
Grade.
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