CN110363652A - A kind of financial product pricing method, device and electronic equipment based on Price Sensitive model - Google Patents
A kind of financial product pricing method, device and electronic equipment based on Price Sensitive model Download PDFInfo
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Abstract
The invention discloses a kind of financial product pricing method, device, electronic equipment and computer-readable mediums based on Price Sensitive model characterized by comprising obtains storage user characteristic data, wherein the storage user is the user for not paying off loaning bill；The storage user characteristic data and price adjustment volume are inputted into Price Sensitive model, prediction user moves branch probability, wherein the Price Sensitive model, the dynamic branch probability for predicting to carry out storage user after price adjustment according to price adjustment volume；Target user is filtered out based on the dynamic branch probability；According to the price adjustment volume, the financial product price of the target user is determined.The present invention can adjust the dynamic branch probability of storage user after financial product price by Price Sensitive model prediction, retain not influencing remaining sum, user perceives in weaker situation price, and the price for changing some users carrys out additional income.
Description
Technical field
The present invention relates to computer information processing fields, in particular to a kind of finance based on Price Sensitive model
Price fixing method, apparatus, electronic equipment and computer-readable medium.
Background technique
Internet financial platform can adjust determining for financial product according to the loaning bill behavior of user after user borrows money
Valence, to optimize whole remaining sum retention and income.If perceiving the price that stronger user adjusts financial product, bring to price
Income is very undesirable, and under serious situation, any price for adjusting financial product will lead to the loss of client.
For the storage user of surplus, part of user is weaker to price perception, and internet financial platform is intended to be directed to
The certain customers adjust financial product price, and then bring income.
But the prior art lacks specifically executable measure, while adjusting financial product price, promotes user's
Dynamic branch rate, and bring ideal income.
Summary of the invention
The technical problem to be solved by the present invention is to promote the dynamic branch of user how while adjusting financial product price
Rate, and bring ideal income.
An aspect of of the present present invention provides a kind of financial product pricing method based on Price Sensitive model, which is characterized in that
It include: to obtain storage user characteristic data, wherein the storage user is the user for not paying off loaning bill；By the storage user
Characteristic and price adjustment volume input Price Sensitive model, and prediction user moves branch probability, wherein the Price Sensitive model,
Dynamic branch probability for predicting to carry out storage user after price adjustment according to price adjustment volume；It is filtered out based on the dynamic branch probability
Target user；According to the price adjustment volume, the financial product price of the target user is determined.
A preferred embodiment of the invention, further includes: according to the storage user characteristic data, set price
Adjustment volume.
A preferred embodiment of the invention, described according to the storage user characteristic data, tune of setting price
Whole volume further comprises: according to the storage user characteristic data, promoting price, adjustment volume of setting price according to fixed value；
And/or according to the storage user characteristic data, price, adjustment volume of setting price are promoted according to fixed proportion.
A preferred embodiment of the invention, the acquisition storage user characteristic data further comprise: obtaining
Storage user attribute data, the attribute data include: at least one of age, gender, educational background, income, place；Acquisition is deposited
Measure user's lend-borrow action data, the lend-borrow action data include: total borrowing balance, always loaning bill stroke count, loaning bill stroke count in half a year,
At least one of login times in half a year；Storage user APP operation data is obtained, the APP operation data includes: that APP is stepped on
Record at least one of time, APP login times；According to the attribute data, lend-borrow action data, and/or APP operation data
Generate the characteristic.
A preferred embodiment of the invention, the step that target user is filtered out based on the dynamic branch probability
Suddenly, further comprise: determining dynamic branch probability critical value；Determine target user's screening rule, wherein the target user screens rule
Then it is greater than dynamic branch probability critical value for dynamic branch probability；Dynamic branch probability, dynamic branch probability critical value are substituted into target user's screening rule,
Filter out target user.
A preferred embodiment of the invention, further includes: price is constructed based on history storage user characteristic data
Sensitive model.
A preferred embodiment of the invention, it is described that Price Sensitive is constructed based on history storage user characteristic data
The step of model, further comprises: obtaining history storage user data；History is filtered out from the history storage user data
Storage user characteristic data；Price Sensitive model is constructed based on the history storage user characteristic data；To the Price Sensitive
The characteristic parameter of model is adjusted, and obtains the Price Sensitive model of optimization.
A preferred embodiment of the invention, it is described to filter out history from the history storage user data and deposit
The step of measuring user characteristic data further comprises: carrying out data cleansing to the history storage user data, obtains criterion numeral
According to；By Feature Engineering, from the normal data, history storage user characteristic data is screened；To the history storage user
Characteristic is tested, and the history storage user characteristic data of optimization is obtained.
A preferred embodiment of the invention, it is described that the storage user characteristic data and price adjustment volume is defeated
Enter Price Sensitive model, predicts the step of user moves branch probability, further comprise: choosing xgboost from Ensemble Learning Algorithms
Algorithm of the binary classification algorithm as Price Sensitive model；The storage user characteristic data and price adjustment volume are inputted into price
Sensitive model predicts that user moves branch probability by xgboost binary classification algorithm.
A preferred embodiment of the invention, further includes: by data output interface, export the target user
To storage system.
The second aspect of the present invention provides a kind of financial product pricing device based on Price Sensitive model, and feature exists
Module is obtained in, comprising: storage user characteristic data, for obtaining storage user characteristic data, wherein the storage user is
The user of loaning bill is not paid off；Dynamic branch probabilistic forecasting module, for inputting the storage user characteristic data and price adjustment volume
Price Sensitive model, prediction user move branch probability, wherein the Price Sensitive model, for predict according to price adjustment volume into
The dynamic branch probability of storage user after row price adjustment；Target user's screening module, for filtering out mesh based on the dynamic branch probability
Mark user；Financial product price determining module, for determining the financial product of the target user according to the price adjustment volume
Price.
A preferred embodiment of the invention, further includes: price adjustment volume determining module, for being deposited according to
Measure user characteristic data, adjustment volume of setting price.
A preferred embodiment of the invention, the price adjustment volume determining module further comprise: fixed value
Volume unit is mentioned, for promoting price, adjustment volume of setting price according to fixed value according to the storage user characteristic data；And/or
Fixed proportion mentions volume unit, for promoting price, tune of setting price according to fixed proportion according to the storage user characteristic data
Whole volume.
A preferred embodiment of the invention, the storage user characteristic data obtain module, further comprise:
Storage user attribute data acquiring unit, for obtaining storage user attribute data, the attribute data include: the age, gender,
At least one of educational background, income, place；Storage user's lend-borrow action data capture unit, for obtaining storage user debt-credit
Behavioral data, the lend-borrow action data include: total borrowing balance, always loaning bill stroke count, loaning bill stroke count in half a year, log in half a year
At least one of number；Storage user's APP operation data acquiring unit, it is described for obtaining storage user's APP operation data
APP operation data includes: at least one of APP login time, APP login times；Characteristic generation unit is used for root
The characteristic is generated according to the attribute data, lend-borrow action data, and/or APP operation data.
A preferred embodiment of the invention, target user's screening module further comprise: dynamic branch probability
Critical value determination unit, for determining dynamic branch probability critical value；Screening rule determination unit, for determining that target user screens rule
Then, wherein target user's screening rule is that dynamic branch probability is greater than dynamic branch probability critical value；Target user's screening unit is used
In dynamic branch probability, dynamic branch probability critical value are substituted into target user's screening rule, target user is filtered out.
A preferred embodiment of the invention, further includes: Price Sensitive model construction module, for being based on history
Storage user characteristic data constructs Price Sensitive model.
A preferred embodiment of the invention, the Price Sensitive model construction module further comprise: history
Storage user data acquiring unit, for obtaining history storage user data；Characteristic screening unit is used for from the history
History storage user characteristic data is filtered out in storage user data；Price Sensitive model construction unit, for being gone through based on described
History storage user characteristic data constructs Price Sensitive model；Price Sensitive model optimization unit, for the Price Sensitive mould
The characteristic parameter of type is adjusted, and obtains the Price Sensitive model of optimization.
A preferred embodiment of the invention, the characteristic screening unit further comprise: data cleansing
Subelement obtains normal data for carrying out data cleansing to the history storage user data；Feature Engineering subelement is used
In by Feature Engineering, from the normal data, history storage user characteristic data is screened；Characteristic optimizes subelement,
For testing to the history storage user characteristic data, the history storage user characteristic data of optimization is obtained.
A preferred embodiment of the invention, the dynamic branch probabilistic forecasting module further comprise: algorithm picks
Unit, for choosing algorithm of the xgboost binary classification algorithm as Price Sensitive model from Ensemble Learning Algorithms；Dynamic branch is general
Rate predicting unit passes through xgboost for the storage user characteristic data and price adjustment volume to be inputted Price Sensitive model
Binary classification algorithm, prediction user move branch probability.
A preferred embodiment of the invention, further includes: target user's memory module, for being exported by data
Interface exports the target user to storage system.
The third aspect of the present invention provides a kind of electronic equipment, wherein the electronic equipment includes: processor；And
The memory of computer executable instructions is stored, the executable instruction when executed executes the processor
Described in any item methods.
The fourth aspect of the present invention provides a kind of computer readable storage medium, wherein the computer-readable storage medium
Matter stores one or more programs, and one or more of programs when being executed by a processor, realize described in any item methods.
Technical solution of the present invention has the following beneficial effects:
The present invention can adjust the dynamic branch probability of storage user after financial product price by Price Sensitive model prediction,
Remaining sum retention is not influenced, and user perceives in weaker situation price, and the price for changing some users carrys out additional income.
Detailed description of the invention
In order to keep technical problem solved by the invention, the technological means of use and the technical effect of acquirement clearer,
Detailed description of the present invention specific embodiment below with reference to accompanying drawings.But it need to state, drawings discussed below is only this
The attached drawing of invention exemplary embodiment of the present, to those skilled in the art, before not making the creative labor
It puts, the attached drawing of other embodiments can be obtained according to these attached drawings.
Fig. 1 is the financial product pricing method flow diagram of the invention based on Price Sensitive model；
Fig. 2 is that the financial product pricing method of the invention based on Price Sensitive model based on dynamic branch probability filters out mesh
Mark the step schematic diagram of user；
Fig. 3 is the financial product pricing method process signal based on Price Sensitive model of a specific embodiment of the invention
Figure；
Fig. 4 is the financial product pricing device configuration diagram of the invention based on Price Sensitive model；
Fig. 5 is the electronic devices structure block schematic illustration of the financial product price of the invention based on Price Sensitive model；
Fig. 6 is computer readable storage medium schematic diagram of the invention.
Specific embodiment
Exemplary embodiment of the present invention is described more fully with reference to the drawings.However, exemplary embodiment can
Implement in a variety of forms, and is understood not to that present invention is limited only to embodiments set forth herein.On the contrary, it is exemplary to provide these
Embodiment enables to the present invention more full and complete, easily facilitates the technology that inventive concept is comprehensively communicated to this field
Personnel.Identical appended drawing reference indicates same or similar element, component or part in figure, thus will omit weight to them
Multiple description.
Under the premise of meeting technical concept of the invention, the feature described in some specific embodiment, structure, spy
Property or other details be not excluded for can be combined in any suitable manner in one or more other embodiments.
In the description for specific embodiment, feature, structure, characteristic or the other details that the present invention describes are to make
Those skilled in the art fully understands embodiment.But, it is not excluded that those skilled in the art can practice this hair
Bright technical solution is one or more without special characteristic, structure, characteristic or other details.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all content and operation/step,
It is not required to execute by described sequence.For example, some operation/steps can also decompose, and some operation/steps can close
And or part merge, therefore the sequence actually executed is possible to change according to the actual situation.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or realized in one or more hardware modules or integrated circuit
These functional entitys, or these functional entitys are realized in heterogeneous networks and/or processor device and/or microcontroller device.
Although it should be understood that may indicate the attribute of number using first, second, third, etc. to describe various devices herein
Part, element, component or part, but this should not be limited by these attributes.These attributes are to distinguish one and another one.Example
Such as, the first device is also referred to as the second device without departing from the technical solution of essence of the invention.
Term "and/or" or " and/or " include associated listing all of any of project and one or more
Combination.
Fig. 1 is the financial product pricing method flow diagram of the invention based on Price Sensitive model；As shown in Figure 1,
The method of the present invention includes following steps:
S101: storage user characteristic data is obtained, wherein the storage user is the user for not paying off loaning bill.
S102: the storage user characteristic data and price adjustment volume are inputted into Price Sensitive model, the dynamic branch of prediction user
Probability, wherein the Price Sensitive model, the dynamic branch for predicting to carry out storage user after price adjustment according to price adjustment volume
Probability.
S103: target user is filtered out based on the dynamic branch probability.
S104: according to the price adjustment volume, the financial product price of the target user is determined.
It wherein, further include step S100 before step S101: quick based on history storage user characteristic data building price
Feel model.
Further, the step S100 further comprises:
Obtain history storage user data；
History storage user characteristic data is filtered out from the history storage user data；
Price Sensitive model is constructed based on the history storage user characteristic data；
The characteristic parameter of the Price Sensitive model is adjusted, the Price Sensitive model of optimization is obtained.
Further, the step filters out history storage user characteristics number from the history storage user data
According to further comprising: carrying out data cleansing to the history storage user data, obtain normal data；By Feature Engineering, from
In the normal data, history storage user characteristic data is screened；It tests, obtains to the history storage user characteristic data
Take the history storage user characteristic data of optimization.
Step S101 obtains storage user characteristic data, further comprises:
Obtain storage user attribute data, the attribute data include: the age, gender, educational background, income, in place extremely
It is one few；
Obtain storage user lend-borrow action data, the lend-borrow action data include: total borrowing balance, always loaning bill stroke count,
Loaning bill stroke count, at least one of login times in half a year in half a year；
Storage user APP operation data is obtained, the APP operation data includes: APP login time, in APP login times
At least one；
The characteristic is generated according to the attribute data, lend-borrow action data, and/or APP operation data.
After step S101 obtains storage user characteristic data, further includes: according to the storage user characteristic data, really
Determine price adjustment volume.
Wherein, according to the storage user characteristic data described in step, adjustment volume of setting price further comprises:
According to the storage user characteristic data, price, adjustment volume of setting price are promoted according to fixed value；And/or
According to the storage user characteristic data, price, adjustment volume of setting price are promoted according to fixed proportion.
Wherein, the storage user characteristic data and price adjustment volume are inputted Price Sensitive model, prediction by step S102
User moves the step of branch probability, further comprises:
Algorithm of the xgboost binary classification algorithm as Price Sensitive model is chosen from Ensemble Learning Algorithms；
The storage user characteristic data and price adjustment volume are inputted into Price Sensitive model, pass through xgboost binary point
Class algorithm, prediction user move branch probability.
Wherein, the step of filtering out target user based on the dynamic branch probability described in step S103 further comprises:
S201: dynamic branch probability critical value is determined.
S202: target user's screening rule is determined, wherein target user's screening rule is that dynamic branch probability is greater than dynamic branch
Probability critical value.
S203: dynamic branch probability, dynamic branch probability critical value are substituted into target user's screening rule, filter out target user.
It should be noted that the financial product pricing method of the invention based on Price Sensitive model, further includes: pass through number
According to output interface, the target user is exported to storage system.
Fig. 3 is the financial product pricing method process signal based on Price Sensitive model of a specific embodiment of the invention
Figure.
It should be noted that the financial product pricing method based on Price Sensitive model of specific embodiments of the present invention,
Price Sensitive model based on step method as described above building optimization.
As shown in figure 3, financial product pricing method is explained in detail in the Price Sensitive model based on building.
User A, B, C are storage user, obtain user A, the characteristic of B, C.
Wherein, the attribute data of user A includes age 36, the data such as gender male；The lend-borrow action data of user A include total
Borrowing balance 80000, total loaning bill stroke count 18, the data such as nearly half a year loaning bill stroke count 8, the APP operation data of user A include nearly half
The data such as year login times 3.
Wherein, the attribute data of user B includes age 28, the data such as gender male；The lend-borrow action data of user B include total
Borrowing balance 6000, total loaning bill stroke count 1, the data such as nearly half a year loaning bill stroke count 0, the APP operation data of user B includes nearly half a year
The data such as login times 0.
Wherein, the attribute data of user C includes age 31, the data such as gender female；The lend-borrow action data of user C include total
Borrowing balance 45000, total loaning bill stroke count 8, the data such as nearly half a year loaning bill stroke count 2, the APP operation data of user C includes nearly half a year
The data such as login times 2.
Respectively by user A, the characteristic of B, C input Price Sensitive model, pass through the xgboost in Ensemble Learning Algorithms
Binary classification algorithm, the dynamic branch probability that user A is calculated is 85%；The dynamic branch probability of user B is 32%；The dynamic branch of user C
Probability is 46%.
In specific embodiments of the present invention, determine that dynamic branch probability critical value is 60%；Determine that target user's screening rule is
Dynamic branch probability is greater than dynamic branch probability critical value.
It is legal in user A, B, C based on dynamic branch probability critical value target user screening rule sieve series target user
Target user is user A.
By data output interface, target user A is exported to storage system.
As an example, retaining not influencing remaining sum, user is perceived in weaker situation, changes the target user filtered out
Financial product price come additional income, for example, user A to price perception it is weaker in the case where, by the borrowing rate of user A
18% is increased to from 9%.
It should be noted that showing that part loaning bill is more frequent with test by concrete analysis, APP operation is more active
User, it is weaker to the perception of price.
Due to user B and C and target user's screening rule is not met, then does not adjust the financial product price of user B and C.
The present invention can adjust the dynamic branch probability of storage user after financial product price by Price Sensitive model prediction,
Remaining sum retention is not influenced, user perceives in weaker situation price, and the price for changing some users carrys out additional income, according to
Estimation internet financial platform income is able to ascend 15%.
It will be understood by those skilled in the art that realizing that all or part of the steps of above-described embodiment is implemented as by computer
The program (computer program) that data processing equipment executes.It is performed in the computer program, offer of the present invention is provided
The above method.Moreover, the computer program can store in computer readable storage medium, which can be with
It is the readable storage medium storing program for executing such as disk, CD, ROM, RAM, is also possible to the storage array of multiple storage medium compositions, such as magnetic
Disk or tape storage array.The storage medium is not limited to centralised storage, is also possible to distributed storage, such as based on
The cloud storage of cloud computing.
The device of the invention embodiment is described below, which can be used for executing embodiment of the method for the invention.For
Details described in apparatus of the present invention embodiment should be regarded as the supplement for above method embodiment；For in apparatus of the present invention
Undisclosed details in embodiment is referred to above method embodiment to realize.
It will be understood by those skilled in the art that each module in above-mentioned apparatus embodiment can be distributed in device according to description
In, corresponding change can also be carried out, is distributed in one or more devices different from above-described embodiment.The mould of above-described embodiment
Block can be merged into a module, can also be further split into multiple submodule.
Fig. 4 is the financial product pricing device configuration diagram of the invention based on Price Sensitive model；As shown in figure 4,
The device of the invention 400 includes: that storage user characteristic data obtains module 401, moves branch probabilistic forecasting module 402, target user
Screening module 403, financial product price determining module 404.
Storage user characteristic data obtains module, for obtaining storage user characteristic data, wherein the storage user is
The user of loaning bill is not paid off.
Dynamic branch probabilistic forecasting module, for the storage user characteristic data and price adjustment volume to be inputted Price Sensitive mould
Type, prediction user move branch probability, wherein the Price Sensitive model carries out price adjustment according to price adjustment volume for predicting
The dynamic branch probability of storage user afterwards.
Target user's screening module, for filtering out target user based on the dynamic branch probability.
Financial product price determining module, for determining that the finance of the target user is produced according to the price adjustment volume
Product price.
Financial product pricing device based on Price Sensitive model of the invention, further includes: price adjustment volume determining module,
For according to the storage user characteristic data, adjustment volume of setting price.
Wherein, the price adjustment volume determining module further comprises: fixed value mentions volume unit, and fixed proportion mentions volume list
Member.
Fixed value mentions volume unit, for promoting price according to fixed value, determining valence according to the storage user characteristic data
The whole volume of style；And/or
Fixed proportion mentions volume unit, for promoting price according to fixed proportion, really according to the storage user characteristic data
Determine price adjustment volume.
Wherein, the user characteristic data obtains module, further comprises: attribute data acquiring unit, lend-borrow action number
According to acquiring unit, characteristic generation unit.
Attribute data acquiring unit, for obtaining user attribute data, the attribute data includes: age, gender,
It goes through, take in, at least one of place.
Lend-borrow action data capture unit, for obtaining user's lend-borrow action data, the lend-borrow action data include: total
Borrowing balance, always loaning bill stroke count, loaning bill stroke count, at least one of login times in half a year in half a year.
Characteristic generation unit, for generating the characteristic according to the attribute data and lend-borrow action data.
Wherein, the storage user characteristic data obtains module, further comprises: storage user attribute data obtains single
Member, storage user's lend-borrow action data capture unit, storage user's APP operation data acquiring unit, characteristic generation unit.
Storage user attribute data acquiring unit, for obtaining storage user attribute data, the attribute data includes: year
At least one of age, gender, educational background, income, place.
Storage user's lend-borrow action data capture unit, for obtaining storage user's lend-borrow action data, the debt-credit row
It include: total borrowing balance, always loaning bill stroke count, loaning bill stroke count, at least one of login times in half a year in half a year for data.
Storage user's APP operation data acquiring unit, for obtaining storage user's APP operation data, the APP operand
According to including: at least one of APP login time, APP login times.
Characteristic generation unit, for according to the attribute data, lend-borrow action data, and/or APP operation data
Generate the characteristic.
Wherein, target user's screening module further comprises: dynamic branch probability critical value determination unit, screening rule
Determination unit, target user's screening unit.
Dynamic branch probability critical value determination unit, for determining dynamic branch probability critical value；
Screening rule determination unit, for determining target user's screening rule, wherein target user's screening rule is
Dynamic branch probability is greater than dynamic branch probability critical value；
Target user's screening unit, for dynamic branch probability, dynamic branch probability critical value to be substituted into target user's screening rule, sieve
Select target user.
Wherein, the financial product pricing device of the invention based on Price Sensitive model, further includes: Price Sensitive model structure
Block is modeled, for constructing Price Sensitive model based on history storage user characteristic data.
Wherein, the Price Sensitive model construction module further comprises: history storage user data acquiring unit, special
Levy data screening unit, Price Sensitive model construction unit, Price Sensitive model optimization unit.
History storage user data acquiring unit, for obtaining history storage user data；
Characteristic screening unit, for filtering out history storage user characteristics number from the history storage user data
According to；
Price Sensitive model construction unit, for constructing Price Sensitive mould based on the history storage user characteristic data
Type；
Price Sensitive model optimization unit is adjusted for the characteristic parameter to the Price Sensitive model, is obtained excellent
The Price Sensitive model of change.
Wherein, the characteristic screening unit further comprises: data cleansing subelement, Feature Engineering subelement, special
Levy data-optimized subelement.
Data cleansing subelement obtains normal data for carrying out data cleansing to the history storage user data.
Feature Engineering subelement, for from the normal data, screening history storage user characteristics by Feature Engineering
Data.
Characteristic optimizes subelement and obtains optimization for testing to the history storage user characteristic data
History storage user characteristic data.
Wherein, the dynamic branch probabilistic forecasting module further comprises: algorithm picks unit, moves branch probability prediction unit.
Algorithm picks unit, for choosing xgboost binary classification algorithm from Ensemble Learning Algorithms as Price Sensitive
The algorithm of model.
Dynamic branch probability prediction unit, for the storage user characteristic data and price adjustment volume to be inputted Price Sensitive mould
Type predicts that user moves branch probability by xgboost binary classification algorithm.
Wherein, the financial product pricing device of the invention based on Price Sensitive model, further includes: target user stores mould
Block, for exporting the target user to storage system by data output interface.
Electronic equipment embodiment of the invention is described below, which can be considered as the method for aforementioned present invention
With the specific entity embodiment of Installation practice.For details described in electronic equipment embodiment of the present invention, should be regarded as pair
In the above method or the supplement of Installation practice；For undisclosed details, Ke Yican in electronic equipment embodiment of the present invention
It is realized according to the above method or Installation practice.
Fig. 5 is the structural block diagram of the exemplary embodiment of a kind of electronic equipment according to the present invention.It is retouched referring to Fig. 5
State the electronic equipment 500 of the embodiment according to the present invention.The electronic equipment 500 that Fig. 5 is shown is only an example, should not be to this
The function and use scope of inventive embodiments bring any restrictions.
As shown in figure 5, electronic equipment 500 is showed in the form of universal computing device.The component of electronic equipment 500 can be with
Including but not limited to: at least one processing unit 510, at least one storage unit 520, the different system components of connection (including are deposited
Storage unit 520 and processing unit 510) bus 530, display unit 540 etc..
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 510
Row, so that the processing unit 510 executes described in this specification above-mentioned electronic prescription circulation processing method part according to this
The step of inventing various illustrative embodiments.For example, the processing unit 510 can execute step as shown in Figure 1.
The storage unit 520 may include the readable medium of volatile memory cell form, such as random access memory
Unit (RAM) 5201 and/or cache memory unit 5202 can further include read-only memory unit (ROM) 5203.
The storage unit 520 can also include program/practical work with one group of (at least one) program module 5205
Tool 5204, such program module 5205 includes but is not limited to: operating system, one or more application program, other programs
It may include the realization of network environment in module and program data, each of these examples or certain combination.
Bus 530 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 500 can also be with one or more external equipments 600 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 500 communicate, and/or with make
The electronic equipment 500 any equipment (such as the router, modulatedemodulate that can be communicated with one or more of the other calculating equipment
Adjust device etc.) communication.This communication can be carried out by input/output (I/O) interface 550.Also, electronic equipment 500 may be used also
To pass through network adapter 560 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network
Network, such as internet) communication.Network adapter 560 can be communicated by bus 530 with other modules of electronic equipment 500.
It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 500, including but not
Be limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art it can be readily appreciated that the present invention describe it is exemplary
Embodiment can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to this hair
The technical solution of bright embodiment can be embodied in the form of software products, which can store calculates at one
In the readable storage medium of machine (can be CD-ROM, USB flash disk, mobile hard disk etc.) or on network, including some instructions are so that one
Platform calculates equipment (can be personal computer, server or network equipment etc.) and executes according to the above method of the present invention.When
When the computer program is executed by a data processing equipment so that the computer-readable medium can be realized it is of the invention upper
State method, it may be assumed that obtain storage user characteristic data, wherein the storage user is the user for not paying off loaning bill；By the storage
User characteristic data and price adjustment volume input Price Sensitive model, and prediction user moves branch probability, wherein the Price Sensitive mould
Type, the dynamic branch probability for predicting to carry out storage user after price adjustment according to price adjustment volume；Based on the dynamic branch probability screen
Select target user；According to the price adjustment volume, the financial product price of the target user is determined.
The computer program can store on one or more computer-readable mediums, as shown in Figure 6.Computer can
Reading medium can be readable signal medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic,
Optical, electromagnetic, the system of infrared ray or semiconductor, device or device, or any above combination.Readable storage medium storing program for executing is more
Specific example (non exhaustive list) includes: the electrical connection with one or more conducting wires, portable disc, hard disk, deposits at random
It is access to memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable
Formula compact disk read-only memory (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
The computer readable storage medium may include in a base band or the data as the propagation of carrier wave a part are believed
Number, wherein carrying readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetism
Signal, optical signal or above-mentioned any appropriate combination.Readable storage medium storing program for executing can also be any other than readable storage medium storing program for executing
Readable medium, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or
Person's program in connection.The program code for including on readable storage medium storing program for executing can transmit with any suitable medium, packet
Include but be not limited to wireless, wired, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In conclusion the present invention can be implemented in hardware, or the software to run on one or more processors
Module is realized, or is implemented in a combination thereof.It will be understood by those of skill in the art that micro process can be used in practice
The communications data processing units such as device or digital signal processor (DSP) come realize according to embodiments of the present invention in it is some or
The some or all functions of whole components.The present invention is also implemented as a part for executing method as described herein
Or whole device or device program (for example, computer program and computer program product).Such realization present invention
Program can store on a computer-readable medium, or may be in the form of one or more signals.Such letter
It number can be downloaded from an internet website to obtain, be perhaps provided on the carrier signal or be provided in any other form.
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical scheme and beneficial effects
It describes in detail bright, it should be understood that the present invention is not inherently related to any certain computer, virtual bench or electronic equipment, various
The present invention also may be implemented in fexible unit.The above is only a specific embodiment of the present invention, is not limited to this hair
Bright, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the present invention
Protection scope within.
Claims (10)
1. a kind of financial product pricing method based on Price Sensitive model characterized by comprising
Obtain storage user characteristic data, wherein the storage user is the user for not paying off loaning bill；
The storage user characteristic data and price adjustment volume are inputted into Price Sensitive model, prediction user moves branch probability, wherein
The Price Sensitive model, the dynamic branch probability for predicting to carry out storage user after price adjustment according to price adjustment volume；
Target user is filtered out based on the dynamic branch probability；
According to the price adjustment volume, the financial product price of the target user is determined.
2. the method as described in claim 1, which is characterized in that further include:
According to the storage user characteristic data, adjustment volume of setting price.
3. such as method of any of claims 1-2, which is characterized in that described according to the storage user characteristics number
According to adjustment volume of setting price further comprises:
According to the storage user characteristic data, price, adjustment volume of setting price are promoted according to fixed value；And/or
According to the storage user characteristic data, price, adjustment volume of setting price are promoted according to fixed proportion.
4. method as claimed in any one of claims 1-3, which is characterized in that the acquisition storage user characteristic data, into
One step includes:
Storage user attribute data is obtained, the attribute data includes: age, gender, educational background, income, at least one in place
It is a；
Storage user lend-borrow action data are obtained, the lend-borrow action data include: total borrowing balance, always loaning bill stroke count, half a year
Interior loaning bill stroke count, at least one of login times in half a year；
Obtain storage user APP operation data, the APP operation data include: APP login time, in APP login times extremely
It is one few；
The characteristic is generated according to the attribute data, lend-borrow action data, and/or APP operation data.
5. such as method of any of claims 1-4, which is characterized in that described to filter out mesh based on the dynamic branch probability
The step of marking user further comprises:
Determine dynamic branch probability critical value；
Determine target user's screening rule, wherein target user's screening rule is that dynamic branch probability is critical greater than dynamic branch probability
Value；
Dynamic branch probability, dynamic branch probability critical value are substituted into target user's screening rule, filter out target user.
6. method according to any one of claims 1 to 5, which is characterized in that further include:
Price Sensitive model is constructed based on history storage user characteristic data.
7. such as method of any of claims 1-6, which is characterized in that described to be based on history storage user characteristic data
The step of constructing Price Sensitive model further comprises:
Obtain history storage user data；
History storage user characteristic data is filtered out from the history storage user data；
Price Sensitive model is constructed based on the history storage user characteristic data；
The characteristic parameter of the Price Sensitive model is adjusted, the Price Sensitive model of optimization is obtained.
8. a kind of financial product pricing device based on Price Sensitive model characterized by comprising
Storage user characteristic data obtains module, for obtaining storage user characteristic data, wherein the storage user is not go back
The user to borrow money clearly；
Dynamic branch probabilistic forecasting module, for the storage user characteristic data and price adjustment volume to be inputted Price Sensitive model,
Predict that user moves branch probability, wherein the Price Sensitive model is deposited after carrying out price adjustment according to price adjustment volume for predicting
Measure the dynamic branch probability of user；
Target user's screening module, for filtering out target user based on the dynamic branch probability；
Financial product price determining module, for determining that the financial product of the target user is fixed according to the price adjustment volume
Valence.
9. a kind of electronic equipment, wherein the electronic equipment includes:
Processor；And
The memory of computer executable instructions is stored, the executable instruction makes the processor execute basis when executed
Method of any of claims 1-7.
10. a kind of computer readable storage medium, wherein the computer-readable recording medium storage one or more program,
One or more of programs when being executed by a processor, realize method of any of claims 1-7.
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| CN201910571102.1A CN110363652A (en) | 2019-06-27 | 2019-06-27 | A kind of financial product pricing method, device and electronic equipment based on Price Sensitive model |
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| CN201910571102.1A CN110363652A (en) | 2019-06-27 | 2019-06-27 | A kind of financial product pricing method, device and electronic equipment based on Price Sensitive model |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111833141A (en) * | 2020-05-29 | 2020-10-27 | 摩拜(北京)信息技术有限公司 | Information push processing method, device, equipment and storage medium |
| CN111833142A (en) * | 2020-05-29 | 2020-10-27 | 摩拜(北京)信息技术有限公司 | Information push processing method, device, equipment and storage medium |
| CN112508689A (en) * | 2021-02-01 | 2021-03-16 | 四川新网银行股份有限公司 | Method for realizing decision evaluation based on multiple dimensions |
| CN113313562A (en) * | 2021-07-29 | 2021-08-27 | 太平金融科技服务(上海)有限公司深圳分公司 | Product data processing method and device, computer equipment and storage medium |
| CN115439208A (en) * | 2022-08-01 | 2022-12-06 | 睿智合创(北京)科技有限公司 | Client dynamic pricing method based on client credit |
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2019
- 2019-06-27 CN CN201910571102.1A patent/CN110363652A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111833141A (en) * | 2020-05-29 | 2020-10-27 | 摩拜(北京)信息技术有限公司 | Information push processing method, device, equipment and storage medium |
| CN111833142A (en) * | 2020-05-29 | 2020-10-27 | 摩拜(北京)信息技术有限公司 | Information push processing method, device, equipment and storage medium |
| CN112508689A (en) * | 2021-02-01 | 2021-03-16 | 四川新网银行股份有限公司 | Method for realizing decision evaluation based on multiple dimensions |
| CN113313562A (en) * | 2021-07-29 | 2021-08-27 | 太平金融科技服务(上海)有限公司深圳分公司 | Product data processing method and device, computer equipment and storage medium |
| CN115439208A (en) * | 2022-08-01 | 2022-12-06 | 睿智合创(北京)科技有限公司 | Client dynamic pricing method based on client credit |
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