CN110334938A - A kind of investment assetses dynamic match method and device - Google Patents
A kind of investment assetses dynamic match method and device Download PDFInfo
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Abstract
The present invention discloses a kind of investment assetses dynamic match method and device, historic customer feature database, fund side assets feature library and strategy are first preloaded into inner base data/address bus with rule base data by this method and device, by internet real time link technology, the real-time seamless interfacing of multi-to-multi of customer demand and fund side's assets demand is realized；By pre-loaded historic customer feature database, fund side assets feature library and strategy with rule base data to inner base data/address bus, assets anticipation and the processing of judging quota data mart modeling are performed simultaneously using burse mode, it executes clients fund application and fund side's assets is brought together, obtain matching degree score, it files an application from high to low to fund side by matching degree score, clients fund application and fund side's property match efficiency are promoted to greatest extent, promote user experience and selection.
Description
Technical field
The present invention relates to financial field, in particular to a kind of investment assetses dynamic match method and device.
Background technique
With the development of economic technology, capital investment is more and more, pinch of clients fund demand and fund side's assets demand
Conjunction mode is also more and more mature, efficient.Clients fund demand and fund side's assets demand bring mode together by traditional artificial hand
Dynamic operation is changed into and brings program together using assets and bring logic together to realize.In the prior art, assets bring program together in client and money
When dynamic increase and decrease occurs for production side, the case where update not in time there are data information, fail to obtain clients fund demand and money in time
The Optimum Matching of gold side's assets demand as a result, and fail to judge customer demand and fund side's demand comprehensively, reduce matching effect
Rate and success rate.
Summary of the invention
The present invention proposes a kind of investment assetses dynamic match method and device, it is intended to improve clients fund application and fund side
The matching efficiency and success rate of assets.
To achieve the above object, a kind of investment assetses dynamic match method proposed by the present invention, comprising the following steps:
The data of historic customer feature database, fund side assets feature library and strategy and rule base are preloaded by step 1
Inner base data/address bus；
Step 2, client's proposition credit requirement application to real-time input interface bus, meanwhile, the real-time input interface is total
Line obtains the external big data of corresponding client by external big data interface；
Step 3: according to the limitation index in fund side assets feature library, filtering out and meet the several of clients fund demand application
Fund side, meanwhile, the external big data obtained to step 2 is processed, and obtains the corresponding characteristic of the client；
Step 4: according to the corresponding characteristic of the client in step 3, being matched, calculated with several fund sides respectively
Matching degree score between the client and several fund sides；
Step 5: matching degree score obtained in step 4 is sorted from high to low, and according to sequence, successively to fund side into
Row credit requirement application, until success；
Step 6: retaining the data of clients fund demand application generation, and to data sorting and file.
Preferably, the limitation index in fund side's assets feature library includes that amount limits index and flow restriction index,
If the credit requirement application of client meets the amount limitation index of fund side simultaneously and flow restriction index, the fund side meet
The credit requirement application of client.
Preferably, in the step 3, the external big data processing to acquired client includes carrying out cost to the data
Accounting, customer portrait generation, client risk evaluation, the scoring of asset history customer experience.
Preferably, strategy and rule base include access strategy and scoring tactics；
Access strategy can be formulated as follows: basis admittable regulation s1, s2 ... sn, any fund side's wound is first arranged
Several corresponding admittable regulation collection S1, S2 ... Sn are built, wherein each rule set may include more than one of basic admittable regulation,
A corresponding flow nodes are inserted into before each admittable regulation collection, the flow nodes include F1, F2 ... Fn；Score plan
Slightly by constructing grading system in advance, scored using data information of the grading system to acquisition.
A kind of investment assetses dynamic brings device together, comprising:
It is made of, is used in combination with three large database concept of rule base historic customer feature database, fund side assets feature library and strategy
Internal data buffer and interior is loaded into rule base data in fund side assets feature library, historic customer feature database and strategy
The update of portion's data buffer storage；
Real-time input interface bus, for obtaining clients fund demand application information；
External big data input bus, for obtaining external big data information；
Real-time output interface bus, exports for clients fund demand application result；
Execution module, including prejudge module, data mart modeling module, bring execution module together；The anticipation module is used for basis
Limitation index in assets feature library carries out anticipation processing, filters out several fund sides for meeting clients fund demand application；Institute
Data mart modeling module is stated for executing the processing of judging quota data mart modeling, obtains the corresponding characteristic of the client；It is described to bring together
Execution module is used to execute the matching of clients fund demand Yu each fund side's assets.
Preferably, the data mart modeling module includes cost accounting unit, customer portrait generation unit, client risk evaluation
Engine, asset history customer experience scoring unit；
Cost accounting unit is used for according to cost absorbing and benefit item in fund side assets feature library, in conjunction with client's application situation,
Verify the cost and situation of Profit of each assets；
Customer portrait generation unit is used to apply for Shen in conjunction with client according to historic customer feature database and external big data information
It notifies breath, draws customer portrait；
Client risk evaluation engine is used to combine historic customer experience performance data in assets feature library, carries out risk access
Judgement and letter obtain a point judge；
Asset history customer experience scoring unit, for combining strategy and rule base and historic customer experience performance data to obtain
Customer experience score out.
Preferably, the execution module of brining together includes matching degree computing unit, fund application scheduling unit, data filing list
Member；
Matching degree computing unit is calculated for being matched with several fund sides respectively by the corresponding characteristic of client
Matching degree score between the client and several fund sides out；
Fund application scheduling unit, for matching degree score to sort from high to low, and according to sequence, successively to fund side
Credit requirement application is carried out, until success；
Data filing unit, for retaining the data of clients fund demand application generation, and to data sorting and file.
Compared with prior art, the beneficial effects of the present invention are: by internet real time link technology, customer demand is realized
With the real-time seamless interfacing of multi-to-multi of fund side's assets demand；It is special by pre-loaded historic customer feature database, fund side's assets
Library and strategy and rule base data to inner base data/address bus are levied, assets anticipation is performed simultaneously using burse mode and are judged
Achievement data working process, executes clients fund application and fund side's assets are brought together, matching degree score is obtained, by matching degree score
It files an application from high to low to fund side, promotes clients fund application and fund side's property match efficiency to greatest extent, promoted and used
Family experience and selection.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
The structure shown according to these attached drawings obtains other attached drawings.
Fig. 1 is investment assetses dynamic match method flow diagram of the present invention；
Fig. 2 is that investment assetses dynamic of the present invention brings principle of device module map together.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
The present invention proposes a kind of investment assetses dynamic match method, comprising the following steps:
The data of historic customer feature database, fund side assets feature library and strategy and rule base are preloaded by step 1
Inner base data/address bus.
Historic customer feature database includes multiple historic customers, and each historic customer corresponds to each history credit requirement request for data,
Such as: credit requirement, application information, refund performance, life-form structure；Fund side assets feature library includes multiple fund sides, each to provide
Jin Fang corresponds to respective assets protection mode, such as: amount and flow restriction, customer portrait preference, customer risk preference, cost and receipts
Beneficial item etc..
Step 2, client's proposition credit requirement application to real-time input interface bus, meanwhile, the real-time input interface is total
Line obtains the external big data of corresponding client by external big data interface；
Specifically, client proposes credit requirement through input interface bus, is gone through according to the client in historic customer feature database
History request for data is less than stipulated time node for the continuous application time, directly using the data cached after last time application；It is right
It is greater than stipulated time node in the continuous application time, the external big data of corresponding client is obtained by external big data interface,
Wherein, it mainly includes external letter sign data and customer action data, such as client's row letter sign number that external big data, which obtains content,
Sign data, consumer behavior data etc. are believed according to, third party.
Step 3: according to the limitation index in fund side assets feature library, filtering out and meet the several of clients fund demand application
Fund side, meanwhile, the external big data obtained to step 2 is processed, and obtains the corresponding characteristic of the client；
Step 4: according to by the corresponding characteristic of the client in step 3, being matched, calculated with several fund sides respectively
Matching degree score between the client and several fund sides out；
Step 5: matching degree score obtained in step 4 is sorted from high to low, and according to sequence, successively to fund side into
Row credit requirement application, until success；
Step 6: retaining the data of clients fund demand application generation, and to data sorting and file, as subsequent clients
The data basis of application.
This method can support a large amount of different types of clients fund demands and the matching of fund side's asset request, realize client's money
The real-time seamless interfacing of multi-to-multi of golden demand and fund side's asset request saves the response time using burse mode, and promotion is brought together
Preceding execution efficiency.
The limitation index in fund side's assets feature library includes amount limitation index and flow restriction index, if client
Credit requirement application meets the amount limitation index and flow restriction index of fund side simultaneously, then the fund side meets the money of client
Golden demand application.
Specifically, amount limits, i.e., by client when time credit requirement application, history credit requirement application record and fund side
The amount in assets feature library limits Indexes Comparison, such as: the application amount of money and fund side's assets feature library in client's request for data
In amount limitation index-single amount of money compare, it is unavailable to represent assets if being unsatisfactory for, and it is available that satisfaction represents assets.
Flow restriction, i.e., by the application frequency, history application success rate and the fund side's assets feature in client's request for data
The flow restriction Indexes Comparison in library, such as: away from client's last time request for data application time interval, history application time interval
Compared with flow restriction index-application time interval in fund side assets feature library, it is unavailable to represent assets if being unsatisfactory for,
It is available that satisfaction then represents assets.
In step 3, the external big data processing to acquired client includes carrying out cost accounting, Ke Huhua to the data
As generation, client risk evaluation, the scoring of asset history customer experience.
Specifically, cost accounting applies for feelings in conjunction with client that is, according to the cost absorbing and benefit item in fund side assets feature library
Condition verifies the cost and situation of Profit of each assets.
Customer portrait generates, i.e., according to client feature library and external big data information, declares information in conjunction with client, more
The comprehensive drafting customer portrait of dimension.
Client risk evaluation, according to strategy and rule base, in conjunction with historic customer experience performance in fund side assets feature library
Data calculate customer risk score using scorecard and decision engine.
Asset history customer experience scoring, according to strategy and rule base, in conjunction with the history money in fund side assets feature library
Each assets customer experience related data of performance statistics is produced, calculates customer experience score using scorecard and decision engine.
Strategy and rule base include access strategy and scoring tactics；
Access strategy can be formulated as follows: basis admittable regulation s1, s2 ... sn, any fund side's wound is first arranged
Several corresponding admittable regulation collection S1, S2 ... Sn are built, wherein each rule set may include more than one of basic admittable regulation,
A corresponding flow nodes are inserted into before each admittable regulation collection, the flow nodes include F1, F2 ... Fn；Score plan
Slightly by constructing grading system in advance, is scored using data information of the grading system to acquisition, be aligned with reference to grading system
Enter rule to score, the scoring of multiple independent admittable regulations is summed it up up to final score.
Access strategy may include risk access strategy and assets access strategy.For example, risk access strategy is settable
Admittable regulation includes: that people's row letter sign inquiry times cannot be greater than 10, be not allow for bull loan, Asset liability ratio cannot be greater than
50%, and it is successively denoted as s1, s2, s3, setting rule set S1=﹛ s1 ﹜, S2=﹛ s1, s2 ﹜, S3=﹛ s1, s2, s3 ﹜ etc..Into
Flow nodes F1 enters flow nodes F2 as executed rule set S1, the condition A of satisfaction in flow nodes F1, meets condition B entrance
Flow nodes F3, until decision process is completed, decision engine access is by then executing scorecard processing.
Scoring tactics include risk score strategy, customer experience scoring tactics, assets scoring tactics.Such as risk score plan
Slightly settable multinomial risk score rule, wherein a risk score rule be set as within people's row letter sign inquiry times 2 times 10 points,
3-10 times 5 points, 10 times or more 0 point, the score adduction in multinomial risk score rule, as risk score are finally tired out
Adding strictly all rules item score is final score.
A kind of investment assetses dynamic brings device together, comprising:
Inner base data/address bus is preloaded, by historic customer feature database, fund side assets feature library and strategy and rule
Then three large database concept of library forms, for adding fund side assets feature library, historic customer feature database and strategy with rule base data
It is loaded onto the update of internal data buffer and internal data buffer；
Specifically, historic customer feature database, the historic asset performance for historic customer feature is collected and statistics；Fund side
Assets feature library, the assets performance for assets feature is collected and the additions and deletions of statistics and assets feature change maintenance processing；Strategy
Additions and deletions with rule base for decision engine and the scorecard strategy used and rule change maintenance processing.Wherein, historic customer
Feature database includes multiple historic customers, and each historic customer corresponds to each history credit requirement request for data, such as: credit requirement, application
Information, refund performance, life-form structure etc.；Fund side assets feature library includes multiple fund sides, the corresponding respective assets in each fund side
Maintenance mode, such as: amount and flow restriction, customer portrait preference, customer risk preference, cost absorbing and benefit item.
Real-time input interface bus, for obtaining clients fund demand application information.
External big data input bus, for obtaining external big data information.
Specifically, client proposes credit requirement through input interface bus, is gone through according to the client in historic customer feature database
History request for data is less than stipulated time node for the continuous application time, directly using the data cached after last time application；It is right
It is greater than stipulated time node in the continuous application time, the external big data of corresponding client is obtained by external big data interface,
Wherein, it mainly includes external letter sign data and customer action data, such as client's row letter sign number that external big data, which obtains content,
Sign data, consumer behavior data etc. are believed according to, third party.
Real-time output interface bus, exports for clients fund demand application result；
Execution module, including prejudge module, data mart modeling module, bring execution module together；The anticipation module is used for basis
Limitation index in assets feature library carries out anticipation processing, filters out several fund sides for meeting clients fund demand application；Institute
Data mart modeling module is stated for executing the processing of judging quota data mart modeling, obtains the corresponding characteristic of the client；It is described to bring together
Execution module is used to execute the matching of clients fund demand Yu each fund side's assets.
Limitation index includes amount limitation index and flow restriction index, if the credit requirement application of client meets money simultaneously
The amount limitation index and flow restriction index of Jin Fang, then the fund side meets the credit requirement application of client.
Specifically, amount limits, i.e., by client when time credit requirement application, history credit requirement application record and fund side
The amount in assets feature library limits Indexes Comparison, such as: the application amount of money and fund side's assets feature library in client's request for data
In amount limitation index-single amount of money compare, it is unavailable to represent assets if being unsatisfactory for, and it is available that satisfaction represents assets.
Flow restriction, i.e., by the application frequency, history application success rate and the fund side's assets feature in client's request for data
The flow restriction Indexes Comparison in library, such as: away from client's last time request for data application time interval, history application time interval
Compared with flow restriction index-application time interval in fund side assets feature library, it is unavailable to represent assets if being unsatisfactory for,
It is available that satisfaction then represents assets.
The data mart modeling module includes cost accounting unit, customer portrait generation unit, client risk evaluation engine, money
Produce historic customer experience scoring unit；
Cost accounting unit is used for according to cost absorbing and benefit item in fund side assets feature library, in conjunction with client's application situation,
Verify the cost and situation of Profit of each assets；
Customer portrait generation unit is used to apply for Shen in conjunction with client according to historic customer feature database and external big data information
It notifies breath, draws customer portrait；
Client risk evaluation engine is used for according to strategy and rule base, in conjunction with historic customer body in fund side assets feature library
Performance data are tested, calculate customer risk score using scorecard and decision engine.
Asset history customer experience scoring unit is used for according to strategy and rule base, in conjunction in fund side assets feature library
Each assets customer experience related data of historic asset performance statistics calculates customer experience score using scorecard and decision engine.
The execution module of brining together includes matching degree computing unit, fund application scheduling unit, data filing unit；
Matching degree computing unit is calculated for being matched with several fund sides respectively by the corresponding characteristic of client
Matching degree score between the client and several fund sides out；
Fund application scheduling unit, for matching degree score to sort from high to low, and according to sequence, successively to fund side
Credit requirement application is carried out, until success；
Data filing unit is made for retaining the data of clients fund demand application generation, and to data sorting and file
For the data basis of subsequent clients application.
The above description is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all at this
Under the inventive concept of invention, using equivalent structure transformation made by description of the invention and accompanying drawing content, or directly/use indirectly
It is included in other related technical areas in scope of patent protection of the invention.
Claims (7)
1. a kind of investment assetses dynamic match method, which comprises the following steps:
The data of historic customer feature database, fund side assets feature library and strategy and rule base are preloaded into inside by step 1
Basic data bus；
Step 2, client's proposition credit requirement application to real-time input interface bus, meanwhile, the real-time input interface bus is logical
Cross the external big data that external big data interface obtains corresponding client；
Step 3: according to the limitation index in fund side assets feature library, filtering out several funds for meeting clients fund demand application
Side, meanwhile, the external big data obtained to step 2 is processed, and obtains the corresponding characteristic of the client；
Step 4: according to the corresponding characteristic of the client in step 3, being matched respectively with several fund sides, calculate the visitor
Matching degree score between family and several fund sides；
Step 5: matching degree score obtained in step 4 being sorted from high to low, and according to sequence, is successively provided to fund side
Golden demand application, until success；
Step 6: retaining the data of clients fund demand application generation, and to data sorting and file.
2. investment assetses dynamic match method as described in claim 1, which is characterized in that fund side's assets feature library
Limitation index includes amount limitation index and flow restriction index, if the credit requirement application of client meets the volume of fund side simultaneously
Degree limitation index and flow restriction index, then the fund side meets the credit requirement application of client.
3. investment assetses dynamic match method as described in claim 1, which is characterized in that in the step 3, to acquired visitor
The external big data processing at family includes going through to data progress cost accounting, customer portrait generation, client risk evaluation, assets
The scoring of history customer experience.
4. investment assetses dynamic match method as described in claim 1, which is characterized in that strategy and rule base include access plan
Summary and scoring tactics；
Access strategy can be formulated as follows: basic admittable regulation s1, s2 ... sn first be arranged, if any fund side creates
Corresponding admittable regulation collection S1, S2 ... Sn is done, wherein each rule set may include more than one of basic admittable regulation, every
A corresponding flow nodes are inserted into before the one admittable regulation collection, the flow nodes include F1, F2 ... Fn；Scoring tactics are logical
Building grading system in advance is crossed, is scored using data information of the grading system to acquisition.
5. a kind of investment assetses dynamic brings device together characterized by comprising
Inner base data/address bus is preloaded, by historic customer feature database, fund side assets feature library and strategy and rule base
Three large database concepts composition, and be loaded onto for fund side's assets feature library, historic customer feature database and strategy with rule base data
The update of internal data buffer and internal data buffer；
Real-time input interface bus, for obtaining clients fund demand application information；
External big data input bus, for obtaining external big data information；
Real-time output interface bus, exports for clients fund demand application result；
Execution module, including prejudge module, data mart modeling module, bring execution module together；The anticipation module is used for according to assets
Limitation index in feature database carries out anticipation processing, filters out several fund sides for meeting clients fund demand application；The number
According to processing module for executing the processing of judging quota data mart modeling, the corresponding characteristic of the client is obtained；It is described to bring execution together
Module is used to execute the matching of clients fund demand Yu each fund side's assets.
6. investment assetses dynamic as claimed in claim 5 brings device together, which is characterized in that
The data mart modeling module includes that cost accounting unit, customer portrait generation unit, client risk evaluation engine, assets are gone through
History customer experience scoring unit；
Cost accounting unit is used to, in conjunction with client's application situation, be verified according to cost absorbing and benefit item in fund side assets feature library
The cost and situation of Profit of each assets；
Customer portrait generation unit is used to declare letter in conjunction with client according to historic customer feature database and external big data information
Breath draws customer portrait；
Client risk evaluation engine is used to combine historic customer experience performance data in assets feature library, carries out the judgement of risk access
A point judge is obtained with letter；
Asset history customer experience scoring unit obtains visitor for combining strategy to show data with rule base and historic customer experience
Experience score in family.
7. investment assetses dynamic as claimed in claim 5 brings device together, which is characterized in that the execution module of brining together includes
With degree computing unit, fund application scheduling unit, data filing unit；
Matching degree computing unit, for being matched the corresponding characteristic of client respectively with several fund sides, calculating this
Matching degree score between client and several fund sides；
Fund application scheduling unit is successively carried out to fund side for matching degree score to sort from high to low, and according to sequence
Credit requirement application, until success；
Data filing unit, for retaining the data of clients fund demand application generation, and to data sorting and file.
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| CN111507821B (en) * | 2020-04-09 | 2022-08-02 | 苏宁消费金融有限公司 | Asset pool wind control method and system based on rating and memorability strategies |
| CN112085337A (en) * | 2020-08-12 | 2020-12-15 | 上海维信荟智金融科技有限公司 | Capital party configuration method and system |
| CN113807956A (en) * | 2021-11-19 | 2021-12-17 | 北京宇信科技集团股份有限公司 | Data processing method, medium, equipment and system for joint loan |
| CN113807956B (en) * | 2021-11-19 | 2022-02-25 | 北京宇信科技集团股份有限公司 | Data processing method, medium, equipment and system for joint loan |
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