CN108205783A - A kind of automation credit scoring system in agricultural credit field - Google Patents
A kind of automation credit scoring system in agricultural credit field Download PDFInfo
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Abstract
The invention discloses a kind of automation credit scoring system in agricultural credit field, including information acquisition module, hierarchy Model structure module and credit rating module；Described information acquisition module is used to acquire the information of user, including acquisition personal attribute, family's attribute, credit history, Assets, debt situation and business circumstance；The information architecture hierarchy Model that the hierarchy Model structure module is acquired according to information acquisition module, establishes recursive hierarchy structure；The credit rating module grades to the credit of user according to hierarchy Model, generation user credit grading report.
Description
Technical field
The present invention relates to a kind of automation credit scoring systems in agricultural credit field.
Background technology
Personal credit rating is the important evidence source that the basis of social credit system and enterprise carry out credit operation.
Past agricultural credit service because peasant household's agriculture management stability is poor, peasant household's property status can not Scientific evaluation, on the whole for,
Peasant household is poor in the credit field ability to ward off risks.China's agricultural is just gradually moving towards scale, intensive, this development trend, long
Can not only be solved from the point of view of phase agriculture decentralized management resource dispersion, industry chain (supply chain) is short, Market Orientation is low and peasant household is believing
The restriction problem of breath, fund, technology etc. can also break through the limitation of agriculture small market, promote the development in the big market of agricultural.I
State's Rural Land Circulation is more and more, and good land policy has expedited the emergence of large quantities of agricultural planting rich anies influential family, compared with common peasant household,
The ability to ward off risks of rich and influential family greatly improves.China's agricultural finance demand is larger, and traditional Commercial Bank Risk evaluation system is because of it
It adjusts complicated flowage structure, examination ＆ approval period length, credit requirements high to the greatest extent, the credit requirement of vast common agricultural rich and influential family can not be met, because
This, a set of credit scoring model easy to operate, convenient, highly practical is urgently studied to instruct agricultural credit by agricultural credit enterprise
Work.
Invention content
In view of the deficiencies of the prior art, the present invention provides a kind of automation credit scoring system in agricultural credit field,
Including information acquisition module, hierarchy Model structure module and credit rating module；
Described information acquisition module is used to acquire the information of user, is gone through including acquisition personal attribute, family's attribute, credit
History, Assets, debt situation and business circumstance (present invention is suitable for agriculture field, and business circumstance refers to agriculture management situation)；
The information architecture hierarchy Model that the hierarchy Model structure module is acquired according to information acquisition module, builds
Vertical recursive hierarchy structure；
The credit rating module grades to the credit of user according to hierarchy Model, generation user credit grading
Report.
The personal attribute includes age, educational background, health condition, marital status and gender；Family's attribute includes matching
Even work, children's educational background, the household resident time limit, kinsfolk's health status and visit to the parents of schoolchildren or young workers environment；The credit history includes moral standing
Side tune, main strategies, our company's returned money record, law court performs and Central Bank's reference；The Assets include manage total assets,
Vehicle is worth and house property value, vehicle value refer to present worth；The debt situation includes debt ratio, and the business circumstance includes agricultural
Operation experience and business income, agriculture management experience, which refers to, manages the time.
Hierarchy Model structure module performs following steps：
Step 1, two tuple of first class index is built：Credit history (A1, W1), Assets (A2, W2) including client, family
Front yard attribute (A3, W3), business circumstance (A4, W4), debt situation (A5, W5) and personal attribute (A6, W6) build first class index square
Battle array A, as shown in table 1：
Influence between 1 each rule layer of table
Wherein, A1 represents the credit history of client；A2 represents the Assets of client；A3 represents family's attribute of client；
A4 represents client (in agriculture field) business circumstance；A5 represents the debt situation of client；A6 represents the personal attribute of client；
Aij represents column element Aj opposing rows elements A i significance levels, using the improved 5/5-9/l scaling laws of modified version, refers to table
2：
2 improved 5/5-9/l scaling laws of table
Wherein, i is row element serial number；J is column element serial number；
Step 2, the feature vector of calculating matrix A and maximum eigenvalue λmax：；
Step 3, consistency check is carried out to matrix A, A1, A2, A3, A4, A5, A6 institute is calculated respectively by matrix A
Weight W1, W2, W3, W4, W5, the W6 accounted for；Two-level index is built, and two-level index matrix is built using the same method of step 1,
Calculate two-level index Bij；
Step 4, first class index and two-level index weight are converted into flexible strategy.It is i.e. mutually multiplied by weight and this credit gross score
To table 3：
The basis of 3 score-system of table is formed
Step 2 includes：
Element in matrix A is multiplied, i.e., by step 2-1 by rowGained product opens n times side, i.e.,
Root vector normalization is obtained into characteristic vector W：That is W=(W1, W2, W3, W4, W5, W6), W1 is for A1 in formula
Weight shared by customers ' credit history, uijRepresent the K factorials of judgment matrix A, uiIt is that will determine that the K ranks of matrix A row element are put down
Mean, n represent the exponent number of judgment matrix, and i is row serial number, and j is row serial number；
Step 2-2 calculates maximum eigenvalue λ by equation belowmax：Wherein (AW)iRepresent to
Measure the i-th row component of AW.
Step 3 includes：
Step 3-1, two tuple of two-level index includes our company's returned money record (B11, W11), Central Bank seeks to borrow (B12, W12),
Third party seeks to borrow (B13, W13), law court perform (B14, W14), moral standing survey adjust (B15, W15), manage total assets (B21, W21),
House property value (B22, W22), vehicle value (B23, W23), kinsfolk's health status (B31, W31), children educational background (B32,
W32), spouse's work (B33, W33), visit to the parents of schoolchildren or young workers environment (B34, W34), the household resident time limit (B35, W35), agriculture management experience
(B41, W41), agriculture management income (B42, W42), asset-liability ratio (B5, W5), health condition (B61, W61), educational background (B62,
W62), marital status (B63, W63), age (B64, W64), gender (B65, W65), B11 represent our company's returned money record, B12
Represent that Central Bank seeks to borrow, B13 represents that third party seeks to borrow, and B14 represents that law court performs, and B15 represents moral standing side tune, and B21 represents to manage total
Assets, B22 represent house property value, and B23 represents vehicle value, and B31 represents kinsfolk's health status, and B32 represents children's educational background,
B33 represents spouse's work, and B34 represents visit to the parents of schoolchildren or young workers environment, and B35 represents the household resident time limit, and B41 represents agriculture management experience, B42 tables
Showing that agriculture management is taken in, B5 represents asset-liability ratio, and B61 represents health condition, and B62 represents educational background, and B63 represents marital status,
B64 represents the age, and B65 represents gender, and wij represents the weight shared by two-level index Bij, and wij is represented shared by two-level index Bij
Weight；
Consistency check is carried out to matrix A：
The consistency check index CI of calculating matrix A：
When judgment matrix A have crash consistency when, CI=0, CI is bigger, and the consistency of judgment matrix A is poorer, by CI with
Average homogeneity RI shown in table 4 is compared, and is obtained random consistency ration CR, CR=CI/RI, is worked as CR<When 0.10, judge
Matrix has satisfied consistency：
The random index RI value tables of 4 5/5-9/l scale values of table
| Exponent number n | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.1690 | 0.2598 | 0.3287 | 0.3694 | 0.4007 | 0.4167 | 0.4370 |
Step 3-2 calculates combining weights of each hierarchical elements relative to credit total score：
After the relative weighting for obtaining each index of same level, it is necessary to calculate each sub-goal relative to general objective
That is the weight of credit total score defines Tij as weight of the Bij indexs under Ai dimensions with respect to general objective, as shown in table 5, calculation formula
For：Tij=Wi*Wij；
Table 5
Step 3-3 carries out consistency check of the two-level index weight relative to general objective weight, and method is the same as step 3-1, meter
Calculating formula is：
Calculate random consistency ration CRij, CRij=CIij/RIijIf meet CRij<0.1, what representational level always sorted
The result is that meet coherence request, CI hereinijFor coincident indicator of the two-level index with respect to credit total score Z, RIijIt is two
Grade index is with respect to the random index value of credit total score Z, TiIt is the power that the opposite credit total score Z of two-level index row element adds up
Weight, CIiIt is the sum of the coincident indicator of two-level index row element with respect to credit total score Z, RIiFor the opposite letter of two-level index row element
The sum of randomness coincident indicator with total score Z.
Step 4 includes：
Each index weights are converted into flexible strategy：Credit full marks are set as 600 points, by by each level index weights and total score
It is multiplied, and appropriate fine tuning is carried out to the result of mixed decimal, mode is as shown in table 6, obtains the flexible strategy of each level index, i.e., this refers to
Mark full marks value (Score, abbreviation S)：
The flexible strategy S of first class indexiCalculation formula is：Si=Wi*600
The flexible strategy S of two-level indexijCalculation formula is：Sij=Wij*Wi*600。
Table 6
The credit rating module grades to the credit of user according to hierarchy Model, generation user credit grading
Report, specifically includes：
Customer portrait is generated, the form of expression is radar map；According to the flexible strategy that credit scoring and step 4 obtain, user is calculated
Client is carried out grading and is divided into 5 grades by credit total score, respectively poor (automatic rejection), poor (suggesting rejection), generally
(manual intervention), good (it is recommended that passing through), outstanding (automatically by), and different amounts is corresponded under different credit scenes, from
And realize automatic examination ＆ approval.
Advantageous effect：Present system with converge the net of justice, with departments such as shield science and technology have interface, convenient for internal rating and
Solve the latest tendency of client；Present system is with strong points, holds in close confidence, has used the customer data of enterprise's former years magnanimity, raw
Into credit report comparatively have certain authority, can only be used to air control related personnel objective credit rating to client,
It cannot be revealed at business end under line, prevent customer manager from beautifying the operational risks such as customer data wantonly.Invention mathematical logic is strong,
It is comprehensive to investigate dimension, and does not lose the characteristics of simple, convenient, suitable for ordinary size farmer.The achievable calculating at any time of the present invention,
And structure optimization adjustment is carried out according to data statistics result.
Description of the drawings
The present invention is done with reference to the accompanying drawings and detailed description and is further illustrated, it is of the invention above-mentioned or
Otherwise advantage will become apparent.
Fig. 1 is the citation form schematic diagram of recursive hierarchy structure.
Fig. 2 is the interface schematic diagram of points-scoring system of the present invention test.
Fig. 3 is Rating Model schematic diagram.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
The client tested through the present invention, a customer credit ratings report of points-scoring system generation, report summary go out client's
Credit situation.And according to customers' credit scoring come calculate suggestion reply amount work be by industry big data analysis for many years with
And this system autonomous learning technology obtains.
600 points in total of this score-system, altogether comprising personal essential information, family information, customers ' credit history information, client
6 big module of assets information, client's liability information and client's agriculture management information.It is illustrated in figure 2 the test of this points-scoring system
Interface.
The first step selects corresponding module tag according to client's essential information data first, this module accounting is minimum, comprising
The natural quality information of client in itself, such as the gender of client, age, health status, schooling and client marital shape
Condition information scores according to corresponding project, the automatic accumulation calculating client basic information module total score of system.
Second step, customer manager select each scoring item label according to family's stability information of customer survey, as family into
Member's health status, because big data analysis shows that this submodule influences the loan repayment capacity of borrower, so accounting highest, score item
Label is：Member's health, the small disease of member, member's major disease three, the corresponding label of customer manager's objective selection, system is certainly
Dynamic distribution score value.This module other than kinsfolk's health and fitness information submodule, also spouse's working condition, client children educational background,
The household resident time limit, visit to the parents of schoolchildren or young workers 4 submodules of circumstance state information.
Third walks, and credit history information investigation, this module score value accounting highest includes moral standing side tune, main strategies, remittance
Net of justice information and Central Bank's reference information, in addition to this loyal client of the invention that can also be directed to company, increase the said firm's remittance
Money record sub module.In business procedure, customer manager need to collect the risk report in client each side source, and row label of going forward side by side is commented
Point.
4th step investigates the Assets of client, the mainly existing valency of all vehicles of customer management total assets, client
All house property values of value, client, customer manager carry out labeling marking according to actual conditions.
5th step investigates the liability information of client.The module only has this submodule of asset-liability ratio, is actually doing single mistake
Cheng Zhong, customer manager calculate asset-liability ratio, corresponding different debt ratio section is added up according to customer capital and debt situation
Score value.
6th step investigates the agriculture management situation of client.This module contains agriculture management experience and business income two
Submodule, customer manager select the time limit of peasant household large-scale planting according to the result labeling of most tune, be divided into less than 1 year, one
To 3 years, 3 to five years, 5 years or more four levels, according to agriculture big data analysis, the agriculture large-scale planting time limit was longer, agriculture
Family operation agricultural is more stable, and the ability to ward off risks is stronger.And agriculture management takes in this submodule and then corresponds to client in agricultural warp
Stability and profitability in terms of battalion, be also classified into losing, get a profit within 50,000, between profit 5 ten thousand to ten ten thousand, profit 100,000
To 200,000, more than 200,000 five levels of profit, customer manager selects corresponding labeling according to client's actual conditions.
Reference points-scoring system of the present invention integrates a large amount of peasant household's data informations, with AHP according to agricultural industry experience for many years
Method mathematical modeling and big data analysis produce a set of comprehensive, system customer credit score-system.The most tune of peasant household's information warp,
A variety of methods report to the leadship after accomplishing a task inspection, upcheck after typing, score-system captures relevant information, calculates credit total score, propose pre- automatically
Alert signal, generates dynamic credit report, and be loaded into the credit database of enterprise.Then system is divided according to customers' credit,
With reference to former years traditional agriculture loan and the number of modern the Internet agricultural finance loan pattern and the agriculture field difference intended use of the loan
According to system suggests loan ceiling degree in client's application information Display panel automatically, and further illustrates building for different agriculture scenes
View application amount.
Fig. 1 is the citation form schematic diagram of recursive hierarchy structure.Client's essential information：The natural category of labeling selection client
Property and marital status (social property).
Family's stability information：(more children press highest to the schooling of working condition, borrower children including spouse
Calculate score), borrower's household resident time limit, the health status of borrower kinsfolk and office manager be to practical family
Visit the objective evaluation of environment.
The credit history information of client：Returned money record including artificial indirect investigation information, our company's business, client's third
Fang Zhengxin information, client's remittance net of justice Query Information, client Central Bank reference information.
Customer capital situation：Including customer management assets, all vehicle present worths of client, all house present worth letters of client
Breath.
Client's liability information：Asset-liability ratio.
Client's agriculture management information：Including agriculture management experience, agriculture management Income situation information.
Embodiment
According to a large amount of data verification, client's score is divided into 5 levels, customers' credit score is less than 200 points, risk compared with
Height, points-scoring system are vetoed automatically；For customers' credit score at 200-300 points, Rating Model suggestion rejection, individual case can be examined
Consider manpower intervention；For customers' credit score at 300-400 points, Rating Model suggests manual intervention, flexibly handles；For client
More than 400, Rating Model suggestion passes through CREDIT SCORE.
As shown in figure 3, the customers' credit amount directly vetoed of the present embodiment is 0, credit score 200-350/ visitor
Family, by practical use be divided into the means of agricultural production by stages, agricultural machinery by stages, land rent three scenes by stages, corresponding reply amount is also different, respectively
It is 30,000,30,000,00,000；Credit score 350-500/ client, the corresponding amount of three scenes is respectively 50,000,50,000,3
Ten thousand；Credit score 350-500/ client, the corresponding amount of three scenes is respectively 100,000,50,000,50,000.
All clients by this points-scoring system, related service information can be automatically loaded Enterprise Risk Management system database
In, and form the dynamic credit scoring model of client, risk early-warning system will be improved for future and safeguard the data of points-scoring system.
The present invention provides a kind of automation credit scoring systems in agricultural credit field, implement the technical solution
There are many method and approach, and the above is only the preferred embodiment of the present invention, it is noted that for the common of the art
For technical staff, various improvements and modifications may be made without departing from the principle of the present invention, these are improved and profit
Decorations also should be regarded as protection scope of the present invention.The available prior art of each component part being not known in the present embodiment is subject to reality
It is existing.
Claims (7)
1. a kind of automation credit scoring system in agricultural credit field, which is characterized in that including information acquisition module, level knot
Structure model construction module and credit rating module；
Described information acquisition module is used to acquire the information of user, including acquisition personal attribute, family's attribute, credit history, money
Production situation, debt situation and business circumstance；
The information architecture hierarchy Model that the hierarchy Model structure module is acquired according to information acquisition module, foundation are passed
Rank hierarchical structure；
The credit rating module grades to the credit of user according to hierarchy Model, generation user credit grading report
It accuses.
2. system according to claim 1, which is characterized in that the personal attribute include the age, educational background, health condition,
Marital status and gender；Family's attribute includes spouse's work, children's educational background, the household resident time limit, kinsfolk's health shape
Condition and visit to the parents of schoolchildren or young workers environment；The credit history includes moral standing side tune, main strategies, our company's returned money record, law court's execution and centre
Row reference；The Assets include managing total assets, vehicle value and house property value, and vehicle value refers to present worth；The debt
Situation includes debt ratio, and the business circumstance includes agriculture management experience and business income, and agriculture management experience, which refers to, manages the time.
3. system according to claim 2, which is characterized in that hierarchy Model structure module performs following steps：
Step 1, two tuple of first class index is built：Credit history (A1, W1), Assets (A2, W2) including client, family belong to
Property (A3, W3), business circumstance (A4, W4), debt situation (A5, W5) and personal attribute (A6, W6), build first class index matrix A,
As shown in table 1：
Influence between 1 each rule layer of table
Wherein, A1 represents the credit history of client；A2 represents the Assets of client；A3 represents family's attribute of client；A4 generations
Table customer management situation；A5 represents the debt situation of client；A6 represents the personal attribute of client；Aij represents that column element Aj is opposite
Row element Ai significance levels using the improved 5/5-9/l scaling laws of modified version, refer to table 2：
2 improved 5/5-9/l scaling laws of table
Wherein, i is row element serial number；J is column element serial number；
Step 2, the feature vector of calculating matrix A and maximum eigenvalue λmax：；
Step 3, consistency check is carried out to matrix A, is calculated respectively shared by A1, A2, A3, A4, A5, A6 by matrix A
Weight W1, W2, W3, W4, W5, W6 build two-level index, and using the same method structure two-level index matrix of step 1, calculate
Two-level index Bij；
Step 4, first class index and two-level index weight are converted into flexible strategy.
4. system according to claim 3, which is characterized in that step 2 includes：
Element in matrix A is multiplied, i.e., by step 2-1 by rowGained product opens n times side, i.e., General side
Root vector normalization obtains characteristic vector W：That is W=(W1, W2, W3, W4, W5, W6), W1 is A1, that is, client in formula
Weight shared by credit history, uijRepresent the K factorials of judgment matrix A, uiIt is the K rank averages that will determine that matrix A row element,
N represents the exponent number of judgment matrix, and i is row serial number, and j is row serial number；
Step 2-2 calculates maximum eigenvalue λ by equation belowmax：Wherein (AW)iRepresent vector AW
The i-th row component.
5. system according to claim 4, which is characterized in that step 3 includes：
Step 3-1, two tuple of two-level index includes our company's returned money record (B11, W11), Central Bank seeks to borrow (B12, W12), third
Side seeks to borrow (B13, W13), law court performs (B14, W14), moral standing side tune (B15, W15), manages total assets (B21, W21), house property
Be worth (B22, W22), vehicle value (B23, W23), kinsfolk's health status (B31, W31), children academic (B32, W32),
Spouse work (B33, W33), visit to the parents of schoolchildren or young workers environment (B34, W34), the household resident time limit (B35, W35), agriculture management experience (B41,
W41), agriculture management income (B42, W42), asset-liability ratio (B5, W5), health condition (B61, W61), academic (B62, W62),
Marital status (B63, W63), age (B64, W64), gender (B65, W65), B11 represent our company's returned money record, and B12 represents centre
Row is sought to borrow, and B13 represents that third party seeks to borrow, and B14 represents that law court performs, and B15 represents moral standing side tune, and total assets is managed in B21 expressions,
B22 represents house property value, and B23 represents vehicle value, and B31 represents kinsfolk's health status, and B32 represents children's educational background, B33 tables
Show that spouse works, B34 represents visit to the parents of schoolchildren or young workers environment, and B35 represents the household resident time limit, and B41 represents agriculture management experience, and B42 represents agriculture
Industry business income, B5 represent asset-liability ratio, and B61 represents health condition, and B62 represents educational background, and B63 represents marital status, B64 tables
Show the age, B65 represents gender, and wij represents the weight shared by two-level index Bij；
Consistency check is carried out to matrix A：
The consistency check index CI of calculating matrix A：
When matrix A has crash consistency, CI=0, CI is bigger, and the consistency of matrix A is poorer, will be flat shown in CI and table 4
Equal consistency RI is compared, and is obtained random consistency ration CR, CR=CI/RI, is worked as CR<When 0.10, judgment matrix has full
The consistency of meaning：
The random index RI value tables of 4 5/5-9/l scale values of table
Step 3-2 calculates combining weights of each hierarchical elements relative to credit total score：
After the relative weighting for obtaining each index of same level, it is necessary to calculate each sub-goal and believe relative to general objective
With the weight of total score, it is that Bij indexs are with respect to the weight of general objective under Ai dimensions to define Tij, and as shown in table 5, calculation formula is：
Tij=Wi*Wij；
Table 5
Step 3-3, carries out consistency check of the two-level index weight relative to general objective weight, and method calculates public with step 3-1
Formula is：
Calculate random consistency ration CRij, CRij=CIij/RIijIf meet CRij<0.1, the knot that representational level always sorts
Fruit meets coherence request, herein CIijFor coincident indicator of the two-level index with respect to credit total score Z, RIijRefer to for two level
The random index value of the opposite credit total score Z of mark, TiThe weight added up for two-level index row element with respect to credit total score Z,
CIiIt is the sum of the coincident indicator of two-level index row element with respect to credit total score Z, RIiIt is total with respect to credit for two-level index row element
Divide the sum of randomness coincident indicator of Z.
6. system according to claim 5, which is characterized in that step 4 includes：
Each index weights are converted into flexible strategy：Credit full marks are set as 600 points, by by each level index weights and total split-phase
Multiply, obtain the flexible strategy of each level index,
The flexible strategy S of first class indexiCalculation formula is：Si=Wi* 600,
The flexible strategy S of two-level indexijCalculation formula is：Sij=Wij*Wi*600。
7. system according to claim 6, which is characterized in that the credit rating module according to hierarchy Model to
The credit at family is graded, and generation user credit grading report specifically includes：
According to the flexible strategy that credit scoring and step 4 obtain, user credit total score is calculated, client is carried out grading is divided into 5 etc.
Grade, respectively poor (automatic rejection), poor (suggesting rejection), general (manual intervention), good (it is recommended that passing through), it is outstanding (automatically
Pass through), so as to fulfill automatic examination ＆ approval.
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| CN109102394A (en) * | 2018-08-14 | 2018-12-28 | 深圳市人民政府金融发展服务办公室 | Methods of risk assessment, device and computer readable storage medium |
| CN109214683A (en) * | 2018-09-06 | 2019-01-15 | 平安科技（深圳）有限公司 | A kind of Application of risk decision method and device |
| CN109584037A (en) * | 2018-10-16 | 2019-04-05 | 深圳壹账通智能科技有限公司 | Calculation method, device and the computer equipment that user credit of providing a loan scores |
| WO2020077888A1 (en) * | 2018-10-16 | 2020-04-23 | 深圳壹账通智能科技有限公司 | Method and apparatus for calculating credit score of loan user, and computer device |
| CN109727123A (en) * | 2019-01-04 | 2019-05-07 | 平安科技（深圳）有限公司 | User's collage-credit data construction method, device and computer readable storage medium |
| CN110717819A (en) * | 2019-09-02 | 2020-01-21 | 深圳壹账通智能科技有限公司 | Service processing method, system, computer device and storage medium |
| CN111178744A (en) * | 2019-12-25 | 2020-05-19 | 东方微银科技(北京)有限公司 | Distribution method, device, equipment and storage medium |
| CN111191921A (en) * | 2019-12-27 | 2020-05-22 | 安徽科讯金服科技有限公司 | Small and micro enterprise operation condition wind control analysis system |
| CN111507832A (en) * | 2020-06-30 | 2020-08-07 | 浙江网商银行股份有限公司 | Data processing method and device |
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