CN109711981A - The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence - Google Patents
The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence Download PDFInfo
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Abstract
The embodiment of present disclosure discloses a kind of method for determining the accrediting amount based on artificial intelligence, which is characterized in that the described method includes: receiving user's history data；The first user's history data are extracted from the historical data depending at least on data stability and coverage rate index and remaining user's history data is stored as second user historical data, wherein, do not include in the second user historical data and be in debt than associated data；Based on the second user historical data and credit risk-rating model is established using xgboost method；The associated basic amount information of each risk rating corresponding with the credit risk-rating model is determined using linear programming method according to predefined threshold value；And the accrediting amount is determined according at least to the basic amount information.
Description
Technical field
Present disclosure belongs to information technology field more particularly to a kind of side that the accrediting amount is determined based on artificial intelligence
Method, the device that the accrediting amount is determined based on artificial intelligence and a kind of corresponding computer readable storage medium.
Background technique
Artificial intelligence (Artificial Intelligence), english abbreviation AI.It is research, develop for simulating,
Extend and the theory of the intelligence of extension people, method, a new technological sciences of technology and application system.
The requirements for sale of traditional bank micro-credit is high, and borrower's reference is needed to have stable income etc., institute without overdue
Relatively simple with air control strategy, the accrediting amount also takes in user related, and that bank access request is largely not achieved to it
The accrediting amount of bank can not just be obtained.
Summary of the invention
The embodiment of present disclosure, which provides, a kind of determines the method for the accrediting amount, based on artificial intelligence based on artificial intelligence
The device and corresponding computer readable storage medium that can determine that the accrediting amount, make it possible on the basis for not reducing air control ability
On, requirements for sale is reduced, so as to allow more users to obtain the accrediting amount.
For this purpose, the first aspect of the embodiment of present disclosure proposes and a kind of determines the accrediting amount based on artificial intelligence
Method, which comprises
Receive user's history data；
The first user's history data are extracted from the historical data simultaneously depending at least on data stability and coverage rate index
And remaining user's history data is stored as second user historical data, wherein do not include in the second user historical data
With debt than associated data；
Based on the second user historical data and credit risk-rating model is established using xgboost method；
It is determining corresponding with the credit risk-rating model each using linear programming method according to predefined threshold value
The associated basic amount information of a risk rating；And
The accrediting amount is determined according at least to the basic amount information.
The second aspect of the embodiment of present disclosure proposes a kind of device that the accrediting amount is determined based on artificial intelligence,
It is characterised by comprising:
Processor；And
Memory makes the processor execute following operation when executed for storing instruction:
Receive user's history data；
The first user's history data are extracted from the historical data simultaneously depending at least on data stability and coverage rate index
And remaining user's history data is stored as second user historical data, wherein do not include in the second user historical data
With debt than associated data；
Based on the second user historical data and credit risk-rating model is established using xgboost method；
It is determining corresponding with the credit risk-rating model each using linear programming method according to predefined threshold value
The associated basic amount information of a risk rating；And
The accrediting amount is determined according at least to the basic amount information.
The third aspect of the embodiment of present disclosure proposes a kind of computer readable storage medium, including computer can
It executes instruction, the computer executable instructions make described device execute the reality according to present disclosure when running in a device
Apply the method that the accrediting amount is determined based on artificial intelligence described in the first aspect of example.
The method for determining the accrediting amount based on artificial intelligence of embodiment according to present disclosure can not reduce wind
On the basis of control ability, requirements for sale is reduced, so as to allow more users to obtain the accrediting amount.If user is in basic volume
Spend it is ungratified under the premise of, other information can be submitted to promote amount, one, which carrys out user, can enjoy higher amount, two
It also can the clearer understanding of portrait to user.
Other advantages of present disclosure will be explained further below.
Detailed description of the invention
It refers to the following detailed description in conjunction with the accompanying drawings, the feature, advantage and other aspects of each embodiment of present disclosure
It will be apparent, several embodiments of present disclosure are shown by way of example rather than limitation herein, attached
In figure:
Fig. 1 shows the process of the method 100 that the accrediting amount is determined based on artificial intelligence of the embodiment of present disclosure
Figure.
Fig. 2 shows the devices 200 for determining the accrediting amount based on artificial intelligence according to the embodiment of present disclosure
Schematic diagram.
Specific embodiment
Below with reference to each exemplary embodiment of attached drawing detailed description present disclosure.Flow chart and block diagram in attached drawing
Show the architecture, function and operation in the cards of the method and system of the various embodiments according to present disclosure.
It should be noted that each of flowchart or block diagram box can represent a part of a module, program segment or code, institute
The a part for stating module, program segment or code may include one or more patrolling for realizing defined in each embodiment
Collect the executable instruction of function.It should also be noted that in some alternative implementations, function marked in the box can also be with
Occur according to the sequence for being different from being marked in attached drawing.For example, two boxes succeedingly indicated can actually be substantially parallel
Ground executes or they can also be executed in a reverse order sometimes, this depends on related function.It is also noted that
, the combination of the box in each of flowchart and or block diagram box and flowchart and or block diagram can be used
The dedicated hardware based system of defined functions or operations is executed to realize, or specialized hardware and computer can be used
The combination of instruction is realized.
Term as used herein "include", "comprise" and similar terms are open terms, i.e., " including/include but
It is not limited to ", expression can also include other content.Term "based" is " being based at least partially on ".Term " one embodiment "
It indicates " at least one embodiment "；Term " another embodiment " expression " at least one other embodiment " etc..
Technology, method and apparatus known to person of ordinary skill in the relevant may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as part of specification.For the company between each unit in attached drawing
Line, it is only for convenient for explanation, indicate that the unit at least line both ends is in communication with each other, it is not intended that the non-line of limitation
Unit between can not communicate.
For ease of description, some terms occurred in present disclosure are illustrated below, it should be understood that the disclosure
Term used in content should be interpreted that have and its context of this specification and in relation to the meaning in field it is consistent
Meaning.
Term " user " or " client " in present disclosure refer to for meet production, personal consumption and need buy and
The user group for the service that the product or acceptance agencies provided using mechanism is provided.
Term " first ", " second " in present disclosure are only used for description reference, purpose or certain specific things, and cannot
It is interpreted as indication or suggestion relative importance, or implicitly indicates the quantity of indicated technical characteristic.
Term " multiple " in present disclosure refers to two or more.
Technology, method and apparatus known to person of ordinary skill in the relevant may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as part of specification.For the company between each unit in attached drawing
Line, it is only for convenient for explanation, indicate that the unit at least line both ends is in communication with each other, it is not intended that the non-line of limitation
Unit between can not communicate.
Before introducing the solution that present disclosure is proposed, applicant introduces the current prior art first
The inventive concept of the inventor of status and present disclosure.
The requirements for sale of traditional bank micro-credit is high, and borrower's reference is needed to have stable income etc., institute without overdue
Relatively simple with air control strategy, the accrediting amount also takes in user related, and that bank access request is largely not achieved to it
The accrediting amount of bank can not just be obtained.
This method combination multidimensional data (reference, operator, bull etc.) gives institute using the methods of statistical rules, machine learning
There is micro-credit application user's accrediting amount.
Fig. 1 shows the method 100 that the accrediting amount is determined based on artificial intelligence of the embodiment according to present disclosure
Flow chart.It can be seen from the figure that this method 100 includes at least following five steps, i.e., received in method and step 110 first
User's history data；Then, in method and step 120 depending at least on data stability and coverage rate index from the historical data
The first user's history data of middle extraction and remaining user's history data is stored as second user historical data, wherein described
Do not include in second user historical data and is in debt than associated data；Next, based on described the in method and step 130
Two user's history data simultaneously establish credit risk-rating model using xgboost method；Next the root in method and step 140
Each risk rating corresponding with the credit risk-rating model is determined using linear programming method according to predefined threshold value
Associated basis amount information, and finally credit is determined according at least to the basic amount information in method and step 150
Amount.
Thus with respect to the small amount credit strategy of traditional bank, technical solution according to the present invention can not consider debt ratio
In the case where, and on the basis of not reducing air control ability, requirements for sale is reduced, so as to allow more users to be awarded
Believe amount.If user under the premise of basic amount is ungratified, can submit other information to promote amount, one user
Higher amount can be enjoyed, the two also can the clearer understanding of portrait to user.
In one embodiment according to present disclosure, the method also includes: according to the first user's history number
Volume information is proposed according to determining basis；And volume information is proposed also according to the basis and determines the accrediting amount.Thus not only can
Basic amount is provided the user with, and can determine that basis proposes volume information according to the additional information of user, thus not reducing wind
On the basis of control ability, requirements for sale is reduced, so as to allow more users to obtain the accrediting amount.
In one embodiment according to present disclosure, the method also includes: it receives and the user's history data
Third party's data information of associated user, wherein third party's data information includes at least social security information, common reserve fund is believed
Breath, information of vehicles and/or policy information；It is determined according to third party's data information and secondary proposes volume information；And also according to institute
It states the secondary volume information that proposes and determines the accrediting amount.Thus, it is possible to mention volume in basic amount and basis to be unsatisfactory for the case where requiring
Under, by provide additional third party's data achieve the purpose that it is secondary mention volume, and then on the basis of not reducing air control ability, drop
Low requirements for sale, so as to allow more users to obtain the accrediting amounts.
In one embodiment according to present disclosure, the predefined threshold value includes at least one in the following terms
:
Loan limit total amount；
Loan application number；
Examine percent of pass；
Every application approval amount；And/or
Estimate bad credit rate.
In one embodiment according to present disclosure, the second user historical data method includes the basic of user
Overdue information under information, the amount information of user and current amount.
In one embodiment according to present disclosure, the first user's history data include credit card information, live
Housing loan information and other credit informations.
Specifically, the method for determining the accrediting amount based on artificial intelligence according to the present invention includes the following steps, it may be assumed that
I. the whole qualification for estimating the assets that need to commence business sets Asset Allocation target, such as total amount of making loans, application
Equal, EL of number, percent of pass, part etc.；
Ii. for statistical analysis to user's history data, stable, coverage rate height is selected in conjunction with business and to credit volume
Significant feature is spent, determines the importance of these features using step analysis according to specific business, basis of formation mentions volume strategy；
Iii. the history multidimensional data based on user (not including the feature used in ii) establishes credit wind using xgboost
Dangerous rating model；
Iv. the basic amount of each risk class is determined using linear programming method according to the goal-setting of setting；
The information such as other tripartite's data such as social security common reserve fund, information of vehicles, policy information v. provided according to user carry out
It is secondary to mention volume；And
Vi. the final accrediting amount=basis amount+basis mentions volume+secondary and mentions volume.
Thus, it is possible to the small amount credit strategy of opposite traditional bank, this method is not on the basis of reducing air control ability, drop
Low requirements for sale, so as to allow more users to obtain the accrediting amounts.If user under the premise of basic amount is ungratified,
Other information can be submitted to promote amount, one user can enjoy higher amount, and the two also can be to the portrait of user
Clearer understanding.
Basic amount citing:
The credit strategy of traditional bank is foundation income, the accrediting amount that debt compares or he manages it, but is most importantly born
Debt ratio.Different from traditional credit strategy, this method establishes credit using various dimensions feature in the case where not considering to be in debt ratio
Rating model is analyzed, it is specific as follows；
Data source is as follows:
The essential information (age, occupational information, reference information etc.) of client
The amount information (application amount, granted amount, signing situation etc.) of client
Overdue situation (amount, overdue situation etc.) under current amount
It establishes and establishes credit risk-rating model using xgboost, PD, EL of each grade and basic amount range are such as
Shown in table 1:
Table 1
Make loans total amount m, application total number of persons n, percent of pass c, part equal p, EL of the range of basic amount according to setting, utilize
The method calculating of linear programming finds out basic amount bgi, number of degrees g differs d, the number point of each grade between each grade
Cloth ni, it is specific as follows:
m≤n*c*p
∑ni=n
∑bgi*ni*ci≤m
bgi+1=bgi+d
Amount to calculate between the basic amount of each grade and grade is poor.
Basis proposes the citing of volume strategy:
In basic amount, the feature selecting with specific giving credit meaning in user data is analyzed, is reported with reference
He manage it for the accrediting amount, analysis credit card, housing loan and other loan coverage rates, amount distribution etc., concrete analysis
Such as the following table 2:
| Feature | Coverage rate | Amount distribution | Average quantum | Maximum amount |
| Credit card | 98% | 0-10000 | 5000 | 10000 |
| Housing loan | 20% | 1000-1000000 | 480000 | 1000000 |
| Other loans | 95% | 0-100000 | 60000 | 100000 |
Table 2
The importance that these features are determined according to analytic hierarchy process (AHP) calculates the importance of feature；
Such as:
It is analyzed according to the statistical nature of credit card amount, it is assumed that the importance of each feature is maximum amount > average
Amount > median amount, the importance of assignment judgment matrix calculate the weight (being shown in Table 3) between feature.
| Judgment matrix | Average quantum | Maximum amount | Median amount |
| Average quantum | 1 | 3 | 1/2 |
| Maximum amount | 1/3 | 1 | 1/2 |
| Median amount | 2 | 2 | 1 |
Table 3
Additionally or alternatively, the above method can be by computer program product, i.e. computer readable storage medium is real
It is existing.Computer program product may include computer readable storage medium, containing for executing each of present disclosure
The computer-readable program instructions of aspect.Computer readable storage medium, which can be, can keep and store by instruction execution equipment
The tangible device of the instruction used.Computer readable storage medium for example can be but not limited to storage device electric, magnetic storage is set
Standby, light storage device, electric magnetic storage apparatus, semiconductor memory apparatus or above-mentioned any appropriate combination.It is computer-readable
The more specific example (non exhaustive list) of storage medium includes: portable computer diskette, hard disk, random access memory
(RAM), read-only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory
(SRAM), Portable compressed disk read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding
Equipment, the punch card for being for example stored thereon with instruction or groove internal projection structure and above-mentioned any appropriate combination.Here
Used computer readable storage medium is not interpreted as instantaneous signal itself, such as radio wave or other Free propagations
Electromagnetic wave, the electromagnetic wave (for example, the light pulse for passing through fiber optic cables) propagated by waveguide or other transmission mediums or pass through
The electric signal of electric wire transmission.
Fig. 2 shows the devices 200 for determining the accrediting amount based on artificial intelligence according to the embodiment of present disclosure
Schematic diagram.It should be appreciated that the function of the method 100 for determining the accrediting amount based on artificial intelligence in Fig. 1 may be implemented in device 200
Energy.As can be seen from Figure 2 determine that the device 200 of the accrediting amount includes processor 201 and memory 202 based on artificial intelligence.
Processor 201 can be central processing unit (CPU), microcontroller, specific integrated circuit (ASIC), digital signal processor
(DSP), it field programmable gate array (FPGA) or other programmable logic device or is configured as realizing present disclosure
One or more integrated circuits of embodiment.Memory 202 may include volatile memory, also may include non-volatile deposit
Reservoir, ROM, RAM, mobile disk, disk, CD and USB flash disk etc..Make when storing the instruction execution in memory 202
Processor 201 executes following operation:
In one embodiment according to present disclosure, execute the processor with
Lower operation:
Receive user's history data；
The first user's history data are extracted from the historical data simultaneously depending at least on data stability and coverage rate index
And remaining user's history data is stored as second user historical data, wherein do not include in the second user historical data
With debt than associated data；
Based on the second user historical data and credit risk-rating model is established using xgboost method；
It is determining corresponding with the credit risk-rating model each using linear programming method according to predefined threshold value
The associated basic amount information of a risk rating；And
The accrediting amount is determined according at least to the basic amount information.
In one embodiment according to present disclosure, execute the processor
It operates below:
Determine that basis proposes volume information according to the first user's history data；And
Volume information, which is proposed, also according to the basis determines the accrediting amount.
In one embodiment according to present disclosure, execute the processor
It operates below:
Receive third party's data information of user associated with the user's history data, wherein the third number formulary
It is believed that breath includes at least social security information, common reserve fund information, information of vehicles and/or policy information；
It is determined according to third party's data information and secondary proposes volume information；And
The accrediting amount is determined also according to the secondary volume information that mentions.
In one embodiment according to present disclosure, the predefined threshold value includes at least one in the following terms
:
Loan limit total amount；
Loan application number；
Examine percent of pass；
Every application approval amount；And/or
Estimate bad credit rate.
In one embodiment according to present disclosure, the second user historical data method includes the basic of user
Overdue information under information, the amount information of user and current amount.
In one embodiment according to present disclosure, the first user's history data include credit card information, live
Housing loan information and other credit informations.
In general, the various example embodiments of present disclosure can in hardware or special circuit, software, firmware, patrol
Volume, or any combination thereof in implement.Some aspects can be implemented within hardware, and other aspect can by controller,
Implement in the firmware or software that microprocessor or other calculating equipment execute.When the various aspects diagram of the embodiment of present disclosure
Or when being described as block diagram, flow chart or using other certain graphical representations, it will be understood that box described herein, device, system, skill
Art or method can be used as unrestricted example in hardware, software, firmware, special circuit or logic, common hardware or control
Implement in device or other calculating equipment or its certain combination.
It should be noted that although being referred to several modules or unit of device in the detailed description above, this stroke
It point is only exemplary rather than enforceable.In fact, according to the embodiment of present disclosure, it is above-described two or more
The feature and function of module can embody in a module.Conversely, the feature and function of an above-described module can
It is to be embodied by multiple modules with further division.
The foregoing is merely the embodiment alternative embodiments of present disclosure, are not limited to the implementation of present disclosure
Example, for those skilled in the art, the embodiment of present disclosure can have various modifications and variations.It is all in the disclosure
Within the spirit and principle of the embodiment of content, made any modification, equivalence replacement, improvement etc. should be included in the disclosure
Within the protection scope of the embodiment of content.
Although describing the embodiment of present disclosure by reference to several specific embodiments, it should be appreciated that, this public affairs
The embodiment for opening content is not limited to disclosed specific embodiment.The embodiment of present disclosure is intended to cover in appended right
It is required that spirit and scope in included various modifications and equivalent arrangements.Scope of the appended claims meet broadest solution
It releases, to include all such modifications and equivalent structure and function.
Claims (13)
1. a kind of method for determining the accrediting amount based on artificial intelligence, which is characterized in that the described method includes:
Receive user's history data；
The first user's history data are extracted from the historical data depending at least on data stability and coverage rate index and are incited somebody to action
Remaining user's history data is stored as second user historical data, wherein does not include in the second user historical data and bears
Debt is than associated data；
Based on the second user historical data and credit risk-rating model is established using xgboost method；
Each wind corresponding with the credit risk-rating model is determined using linear programming method according to predefined threshold value
The associated basic amount information of danger grading；And
The accrediting amount is determined according at least to the basic amount information.
2. the method according to claim 1, wherein the method also includes:
Determine that basis proposes volume information according to the first user's history data；And
Volume information, which is proposed, also according to the basis determines the accrediting amount.
3. method according to claim 1 or 2, which is characterized in that the method also includes:
Receive third party's data information of user associated with the user's history data, wherein third party's data letter
Breath includes at least social security information, common reserve fund information, information of vehicles and/or policy information；
It is determined according to third party's data information and secondary proposes volume information；And
The accrediting amount is determined also according to the secondary volume information that mentions.
4. the method according to claim 1, wherein the predefined threshold value include in the following terms at least
One:
Loan limit total amount；
Loan application number；
Examine percent of pass；
Every application approval amount；And/or
Estimate bad credit rate.
5. the method according to claim 1, wherein the second user historical data method includes the base of user
Overdue information under this information, the amount information of user and current amount.
6. the method according to claim 1, wherein the first user's history data include credit card information,
Housing loan information and other credit informations.
7. a kind of device for determining the accrediting amount based on artificial intelligence characterized by comprising
Processor；And
Memory makes the processor execute following operation when executed for storing instruction:
Receive user's history data；
The first user's history data are extracted from the historical data depending at least on data stability and coverage rate index and are incited somebody to action
Remaining user's history data is stored as second user historical data, wherein does not include in the second user historical data and bears
Debt is than associated data；
Based on the second user historical data and credit risk-rating model is established using xgboost method；
Each wind corresponding with the credit risk-rating model is determined using linear programming method according to predefined threshold value
The associated basic amount information of danger grading；And
The accrediting amount is determined according at least to the basic amount information.
8. device according to claim 7, which is characterized in that hold the processor when executed
The following operation of row:
Determine that basis proposes volume information according to the first user's history data；And
Volume information, which is proposed, also according to the basis determines the accrediting amount.
9. device according to claim 7 or 8, which is characterized in that also make the processing when executed
Device executes following operation:
Receive third party's data information of user associated with the user's history data, wherein third party's data letter
Breath includes at least social security information, common reserve fund information, information of vehicles and/or policy information；
It is determined according to third party's data information and secondary proposes volume information；And
The accrediting amount is determined also according to the secondary volume information that mentions.
10. device according to claim 7, which is characterized in that the predefined threshold value include in the following terms extremely
One item missing:
Loan limit total amount；
Loan application number；
Examine percent of pass；
Every application approval amount；And/or
Estimate bad credit rate.
11. device according to claim 7, which is characterized in that the second user historical data method includes user's
Overdue information under essential information, the amount information of user and current amount.
12. device according to claim 7, which is characterized in that the first user's history data include credit card information,
Housing loan information and other credit informations.
13. a kind of computer readable storage medium, including computer executable instructions, the computer executable instructions are in device
Make described device execution is according to any one of claim 1 to 6 to determine credit volume based on artificial intelligence when middle operation
The method of degree.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110210963A (en) * | 2019-06-04 | 2019-09-06 | 中投摩根信息技术（北京）有限责任公司 | Air control rating system |
| CN110349016A (en) * | 2019-07-22 | 2019-10-18 | 中国农业银行股份有限公司 | Client's accrediting amount measuring method and system |
| CN110659984A (en) * | 2019-09-30 | 2020-01-07 | 上海淇玥信息技术有限公司 | Credit limit management method and device based on user life cycle prediction and electronic equipment |
| CN111275542A (en) * | 2020-01-17 | 2020-06-12 | 中国建设银行股份有限公司 | Loan request processing method, device and system |
| CN111353872A (en) * | 2019-12-20 | 2020-06-30 | 上海淇玥信息技术有限公司 | Credit granting processing method and device based on financial performance value and electronic equipment |
| CN111967543A (en) * | 2020-10-23 | 2020-11-20 | 北京淇瑀信息科技有限公司 | User resource quota determining method and device and electronic equipment |
| CN112598500A (en) * | 2020-12-21 | 2021-04-02 | 中国建设银行股份有限公司 | Credit processing method and system for non-limit client |
| WO2021107905A1 (en) * | 2019-11-26 | 2021-06-03 | M.B.I.S Bilgisayar Otomasyon Danismanlik Ve Egitim Hizmetleri Sanayi Ticaret A. S. | Computer-applied method and system for evaluating customer orders |
| CN113781198A (en) * | 2020-06-09 | 2021-12-10 | 台北富邦商业银行股份有限公司 | Enterprise loan application evaluation system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787796A (en) * | 2016-05-23 | 2016-07-20 | 中国农业银行股份有限公司 | Credit line processing method and device |
| CN107220890A (en) * | 2017-05-25 | 2017-09-29 | 重庆小雨点小额贷款有限公司 | Line of credit determines method and device |
| CN107424070A (en) * | 2017-03-29 | 2017-12-01 | 广州汇融易互联网金融信息服务有限公司 | A kind of loan user credit ranking method and system based on machine learning |
| CN107492033A (en) * | 2017-08-30 | 2017-12-19 | 广东信基蜂巢科技有限责任公司 | A kind of credit estimation method and device based on air control model |
| CN107590734A (en) * | 2017-08-21 | 2018-01-16 | 中国建设银行股份有限公司 | Determine method and device, terminal device and the computer-readable storage medium of the accrediting amount |
| CN108564286A (en) * | 2018-04-19 | 2018-09-21 | 天合泽泰（厦门）征信服务有限公司 | A kind of artificial intelligence finance air control credit assessment method and system based on big data reference |
-
2018
- 2018-12-28 CN CN201811623805.6A patent/CN109711981A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787796A (en) * | 2016-05-23 | 2016-07-20 | 中国农业银行股份有限公司 | Credit line processing method and device |
| CN107424070A (en) * | 2017-03-29 | 2017-12-01 | 广州汇融易互联网金融信息服务有限公司 | A kind of loan user credit ranking method and system based on machine learning |
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