CN110689425A - Method and device for pricing quota based on income and electronic equipment - Google Patents
Method and device for pricing quota based on income and electronic equipment Download PDFInfo
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- 238000012549 training Methods 0.000 claims description 6
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The application provides a method for pricing quota based on income, which obtains information data of a plurality of users and comprises the following steps: the portrait data and the three-party social data respectively grant different credit worthiness for a plurality of users, calculate income data based on credit worthiness expression under different credit worthiness, construct a credit model based on information data of the users and income data under different credit worthiness, grant the credit for the users based on the information data of the users by using the credit model, can predict under which credit the information data of the users can generate better income data, is not limited by fixed credit existing in credit classification, considers the influence of the income data on credit worthiness pricing, and improves income.
Description
Technical Field
The application relates to the field, in particular to a method and a device for pricing quota based on income and electronic equipment.
Background
The limit pricing is that the user applying for loan determines the loan limit, and the pricing for the loan user is always the leading subject of the financial research.
Pricing is carried out on the user, whether the qualification and the credit of the user are reliable or not is mostly evaluated, loan is issued or loan is refused to be issued for the user according to the evaluation result, and when the loan is issued for the user, the user is allowed to move the fund of the credit line in a mode of granting the credit line to the user, so that loan is realized.
Therefore, the industry gradually appears to divide the quota into a plurality of hierarchies according to the risk hierarchy, grant a high-hierarchy quota for a user with a high qualification level, and still grant a lower quota for a user with a lower qualification level instead of directly refusing to loan, so as to gradually match the loan requirements of users with different qualification levels. In the prior art, the user is graded by using the A card score, although the grading credit granting requirement can be met, the A card score only depends on the user attribute to predict the user risk and income, so that the actual expression value and the predicted expression value have certain deviation.
Disclosure of Invention
The embodiment of the specification provides a method and a device for pricing quota based on income and electronic equipment. The method is used for solving the problem that the conventional quota pricing is fixedly limited by a quota.
The application provides a method for pricing quota based on income, which comprises the following steps:
acquiring information data of a plurality of users, wherein the information data of the users comprises: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
and constructing a quota model based on the information data of the user and the income data under different credit worthiness, so that the quota model grants quota for the user based on the information data of the user.
Optionally, the method further comprises:
clustering the users based on the information data of the users to obtain a plurality of types of user passenger groups;
the step of respectively granting different credit worthiness to the user comprises the following steps:
and granting different credit worthiness limits to the users in different user guest groups according to the types of the user guest groups.
Optionally, the representation data includes self attribute data of the user, and the three-party social data includes social behavior data of the user.
Optionally, the constructing a credit model based on the information data of the user and income data under different credit worthiness includes:
setting labels according to income data of different users under different credit line conditions, and training by taking the information data of the users as samples to obtain the line model.
Optionally, the calculating the profit data based on credit worthiness of the plurality of users under different credit worthiness comprises:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
calculating revenue data based on the credit worthiness performance data;
the establishment of the credit line model based on the information data of the user and the income data under different credit line conditions comprises the following steps:
and correcting the quota model based on the information data of the user and the income data under different credit worthiness to obtain an iterative quota model, so that the quota is granted to the user based on the information data of the user according to the iterative quota model.
Optionally, the credit worthiness performance data includes time for generating the credit worthiness behavior and a corresponding amount of money of the credit worthiness behavior.
Optionally, the credit worthiness behavior comprises: at least one of move payment and repayment.
Optionally, the revenue data includes profit margin.
The application also provides a device that quota pricing was carried out based on income, includes:
the profit data module acquires information data of a plurality of users, wherein the information data of the users comprise: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
and the quota module is used for constructing a quota model based on the information data of the user and income data under different credit quota, so that the quota model grants quota for the user based on the information data of the user.
Optionally, the revenue data module is further configured to:
clustering the users based on the information data of the users to obtain a plurality of types of user passenger groups;
the step of respectively granting different credit worthiness to the user comprises the following steps:
and granting different credit worthiness limits to the users in different user guest groups according to the types of the user guest groups.
Optionally, the representation data includes self attribute data of the user, and the three-party social data includes social behavior data of the user.
Optionally, the constructing a credit model based on the information data of the user and income data under different credit worthiness includes:
setting labels according to income data of different users under different credit line conditions, and training by taking the information data of the users as samples to obtain the line model.
Optionally, the calculating the profit data based on credit worthiness of the plurality of users under different credit worthiness comprises:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
calculating revenue data based on the credit worthiness performance data;
the establishment of the credit line model based on the information data of the user and the income data under different credit line conditions comprises the following steps:
and correcting the quota model based on the information data of the user and the income data under different credit worthiness to obtain an iterative quota model, so that the quota is granted to the user based on the information data of the user according to the iterative quota model.
Optionally, the credit worthiness performance data includes time for generating the credit worthiness behavior and a corresponding amount of money of the credit worthiness behavior.
Optionally, the credit worthiness behavior comprises: at least one of move payment and repayment.
The present application further provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above.
Various embodiments described in this specification can generate income data under different credit worthiness limits by granting different users with different credit worthiness limits, and different users show credit worthiness performance under different credit worthiness limits, so that a limit model is constructed based on the information data of the users and the income data under different credit worthiness limits, and the limit model can be used to predict under which limit the information data of the users can generate better income data based on the information data of the users, so that the limit model is not limited by limit fixation existing in the limit classification, the influence of the income data on credit worthiness pricing is considered, and income is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram illustrating a method for revenue based line pricing according to an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of an apparatus for pricing quota based on income provided by an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
For the user, the pricing method by using the quota grading can basically provide a more appropriate quota, but the pricing method is essentially to select a quota which the user can pay for according to the preset fixed quota for the user.
The applicant has found that there is room for improvement in existing methods of pricing credit of this kind, because for the way credit is rated in credit hierarchy, the credits of adjacent levels essentially form a finite number of discrete values, the selectable credits of which are fixed. If the maximum amount that a certain user can pay back is between two fixed amounts, the optimal amount cannot be found in the mode, the amount finally granted is often lower than the maximum repayment capacity, and the difference is actually space for promoting income.
Therefore, the application provides a method for pricing quota based on income, which comprises the following steps:
acquiring information data of a plurality of users, wherein the information data of the users comprises: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
and constructing a quota model based on the information data of the user and the income data under different credit worthiness, so that the quota model grants quota for the user based on the information data of the user.
The method includes the steps that different users are granted with different credit worthiness limits, and the different users show credit worthiness performance under the different credit worthiness limits, so that income data under the different credit worthiness limits are generated, limit models are built based on the information data of the users and the income data under the different credit worthiness limits, the information data of the users are processed by the models, the information data of the users can be predicted under which limit the information data of the users can generate better income data, limit of limit fixation existing in limit classification is avoided, influence of the income data on credit worthiness limits is considered, income is improved, influence of the income data on credit worthiness pricing limits is considered, and income is improved.
In addition, because the portrait data and the three-party social data are modeled, the accuracy of evaluating the credit worthiness of the user can be improved, and accurate portrait is realized, so that the credit granted to the user can be predicted more accurately to generate better income data.
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
FIG. 1 is a schematic diagram of a method for revenue-based quota pricing according to an embodiment of the present disclosure, where the method may include:
s101: acquiring information data of a plurality of users, wherein the information data of the users comprises: portrait data and three-party social data.
In one embodiment, the portrait data may include user's own attribute data, and the three-party social data may include user's social behavior data, such as user applying loan, drawing funds, repayment, etc. in a third-party credit platform, and such as user's payroll transfer, deposit, etc., where portrait data and three-party social data are not specifically described.
The self attribute information and the three-party social data can reflect the repayment capacity and the repayment willingness of the user from the side, and the repayment capacity and the repayment willingness are used as the information data of the user, so that the accuracy of evaluating the credit worthiness of the user can be improved, accurate portrayal is realized, and the amount granted to the user can be more accurately predicted to generate better income data.
S102: and respectively granting different credit worthiness to a plurality of users.
By granting different credit worthiness to different users, different users can present credit worthiness expression under respective credit worthiness, and then the income data is calculated according to the credit worthiness expressions under different credits.
Optionally, before granting different credit worthiness to a plurality of the users respectively, the method may further include:
clustering the users based on the information data of the users to obtain a plurality of types of user passenger groups;
thus, the granting different credit worthiness limits to the user may include:
and granting different credit worthiness limits to the users in different user guest groups according to the types of the user guest groups.
A plurality of user guest groups are obtained in a clustering mode, the guest groups of the users are used as units to grant the quota, the number of samples under the same credit worthiness is increased, the collection of credit worthiness performance is more accurate, and the influence of unstable factors is small.
S103: and calculating income data based on credit worthiness of a plurality of users under different credit worthiness.
In this embodiment of the present specification, the credit worthiness expression may be generated by credit worthiness of the user after the user is granted the credit worthiness limit, such as move payment, repayment principal, repayment interest, or may be data counted by the room lender at a preset time node, for example, when the repayment time arrives, it is determined whether the user repays to obtain the credit worthiness expression, and it is visible that the acquisition of the credit worthiness expression may be caused by the user's behavior or may be initiated by the lender itself.
Therefore, calculating the revenue data based on credit worthiness of a plurality of said users under different credit worthiness may comprise:
acquiring credit worthiness expressions of a plurality of users under different credit worthiness;
and calculating income data based on credit standing performances under the different credit standing lines.
The credit worthiness performance data comprises time for generating credit worthiness behavior and amount corresponding to the credit worthiness behavior.
The credit worthiness behavior may include: at least one of move payment and repayment.
In the embodiment of the present specification, the operating platform holds funds to pay opportunity cost, so that the more profit is brought by the same amount of principal, the better profit is represented, and therefore, the profit data may include profit margin, i.e. the ratio of loan income to held principal, where the loan income refers not to income in a business of a certain user alone but to income after removing overdue loss and default loss.
Conceivably, in a practical scenario, the credit worthiness of the user is increased within the bearable range of the user, so that more interest can be obtained, but the overdue risk is not increased, in this case, the income can be increased, and the optimal credit worthiness is the credit estimated by using a credit model next class.
In this embodiment of the present specification, in order to obtain revenue data in time, credit worthiness of a user may be monitored, and therefore, optionally, the calculating revenue data based on credit worthiness performances of a plurality of users under different credit worthiness may include:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
and calculating income data based on the credit worthiness performance data.
S104: and constructing a quota model based on the information data of the user and the income data under different credit worthiness, so that the quota model grants quota for the user based on the information data of the user.
The method includes the steps that different users are granted with different credit worthiness limits, and the different users show credit worthiness performance under the different credit worthiness limits, so that income data under the different credit worthiness limits are generated, a limit model is built based on the information data of the users and the income data under the different credit worthiness limits, the information data of the users are processed by the model, the information data of the users can be predicted under which limit can generate better income data, the limit is not limited by limit fixation existing in limit classification, the influence of the income data on credit worthiness pricing is considered, and income is improved.
In addition, because the portrait data and the three-party social data are modeled, the accuracy of evaluating the credit worthiness of the user can be improved, and accurate portrait is realized, so that the credit granted to the user can be more accurately mined to generate better income data.
In the embodiment of the present specification, in order to make the credit amount estimated by the model not limited by the fixed amount in the amount classification, the amount model may be obtained by training in a supervised learning manner.
Therefore, the constructing of the credit line model based on the information data of the user and the income data under different credit line can include:
setting labels according to income data of different users under different credit line conditions, and training by taking the information data of the users as samples to obtain the line model.
In the embodiment of the specification, the credit model can be continuously corrected, and dynamic iteration is realized. Therefore, the calculating the income data based on the credit worthiness of the plurality of users under different credit worthiness may include:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
calculating revenue data based on the credit worthiness performance data;
the establishing of the credit line model based on the information data of the user and the income data under different credit line conditions may include:
and correcting the quota model based on the information data of the user and the income data under different credit worthiness to obtain an iterative quota model, so that the quota is granted to the user based on the information data of the user according to the iterative quota model.
The model can consider the influence of income on quota pricing, and therefore profit maximization can be achieved.
Based on the same concept, the embodiment of the specification further provides a device for pricing quota based on income.
Fig. 2 is a schematic structural diagram of an apparatus for pricing quota based on income provided by an embodiment of the present specification, where the apparatus may include:
the profit data module 201 obtains information data of a plurality of users, where the information data of the users includes: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
the quota module 202 is used for constructing a quota model based on the information data of the user and income data under different credit quota, so that the quota model grants quota for the user based on the information data of the user.
When the limit pricing is carried out again, the device grants the blind credit worthiness limit for different users, and different users show credit worthiness performance under different credit worthiness limits, so that income data under different credit worthiness limits are generated.
In addition, because the portrait data and the three-party social data are modeled, the accuracy of evaluating the credit worthiness of the user can be improved, and accurate portrait is realized, so that the credit granted to the user can be more accurately mined to generate better income data.
Optionally, the revenue data module 201 is further configured to:
clustering the users based on the information data of the users to obtain a plurality of types of user passenger groups;
the step of respectively granting different credit worthiness to the user comprises the following steps:
and granting different credit worthiness limits to the users in different user guest groups according to the types of the user guest groups.
Optionally, the representation data includes self attribute data of the user, and the three-party social data includes social behavior data of the user.
Optionally, the constructing a credit model based on the information data of the user and income data under different credit worthiness includes:
setting labels according to income data of different users under different credit line conditions, and training by taking the information data of the users as samples to obtain the line model.
Optionally, the calculating the profit data based on credit worthiness of the plurality of users under different credit worthiness comprises:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
calculating revenue data based on the credit worthiness performance data;
the establishment of the credit line model based on the information data of the user and the income data under different credit line conditions comprises the following steps:
and correcting the quota model based on the information data of the user and the income data under different credit worthiness to obtain an iterative quota model, so that the quota is granted to the user based on the information data of the user according to the iterative quota model.
Optionally, the credit worthiness performance data includes time for generating the credit worthiness behavior and a corresponding amount of money of the credit worthiness behavior.
Optionally, the credit worthiness behavior comprises: at least one of move payment and repayment.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 330 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for pricing quota based on income, which is characterized by comprising the following steps:
acquiring information data of a plurality of users, wherein the information data of the users comprises: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
and constructing a quota model based on the information data of the user and the income data under different credit worthiness, so that the quota model grants quota for the user based on the information data of the user.
2. The method of claim 1, further comprising:
clustering the users based on the information data of the users to obtain a plurality of types of user passenger groups;
the step of respectively granting different credit worthiness to the user comprises the following steps:
and granting different credit worthiness limits to the users in different user guest groups according to the types of the user guest groups.
3. The method of any of claims 1-2, wherein the representation data includes self attribute data of the user, and the three-party social data includes social behavior data of the user.
4. The method according to any one of claims 1-3, wherein the building of the credit model based on the information data of the user and income data under different credit worthiness comprises:
setting labels according to income data of different users under different credit line conditions, and training by taking the information data of the users as samples to obtain the line model.
5. The method according to any one of claims 1-4, wherein said calculating revenue data based on credit worthiness of a plurality of said users under different credit worthiness comprises:
after granting credit worthiness to the user, monitoring credit worthiness expression data generated by credit worthiness behavior of the user;
calculating revenue data based on the credit worthiness performance data;
the establishment of the credit line model based on the information data of the user and the income data under different credit line conditions comprises the following steps:
and correcting the quota model based on the information data of the user and the income data under different credit worthiness to obtain an iterative quota model, so that the quota is granted to the user based on the information data of the user according to the iterative quota model.
6. The method according to any one of claims 1-5, wherein the credit worthiness data comprises a time at which credit worthiness is generated and an amount of money corresponding to the credit worthiness.
7. The method according to any of claims 1-6, wherein the credit worthiness act comprises: at least one of move payment and repayment.
8. An apparatus for pricing quota based on revenue, comprising:
the profit data module acquires information data of a plurality of users, wherein the information data of the users comprise: portrait data and three-party social data;
respectively granting different credit worthiness to a plurality of users;
calculating income data based on credit standing performances of a plurality of users under different credit standing lines;
and the quota module is used for constructing a quota model based on the information data of the user and income data under different credit quota, so that the quota model grants quota for the user based on the information data of the user.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN112598244A (en) * | 2020-12-15 | 2021-04-02 | 深圳前海微众银行股份有限公司 | Risk revenue management method, device and system and computer readable storage medium |
| CN116777609A (en) * | 2023-08-25 | 2023-09-19 | 中信消费金融有限公司 | Method and device for processing quota, server and readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106971338A (en) * | 2017-04-26 | 2017-07-21 | 北京趣拿软件科技有限公司 | The method and apparatus of data assessment |
| CN109685649A (en) * | 2018-12-28 | 2019-04-26 | 上海点融信息科技有限责任公司 | The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence |
| CN110060144A (en) * | 2019-03-18 | 2019-07-26 | 平安科技（深圳）有限公司 | Amount model training method, amount appraisal procedure, device, equipment and medium |
| CN110189218A (en) * | 2019-04-25 | 2019-08-30 | 北京互金新融科技有限公司 | The management method and device of loan limit |
-
2019
- 2019-09-30 CN CN201910942737.8A patent/CN110689425A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106971338A (en) * | 2017-04-26 | 2017-07-21 | 北京趣拿软件科技有限公司 | The method and apparatus of data assessment |
| CN109685649A (en) * | 2018-12-28 | 2019-04-26 | 上海点融信息科技有限责任公司 | The method, apparatus and storage medium of the accrediting amount are determined based on artificial intelligence |
| CN110060144A (en) * | 2019-03-18 | 2019-07-26 | 平安科技（深圳）有限公司 | Amount model training method, amount appraisal procedure, device, equipment and medium |
| CN110189218A (en) * | 2019-04-25 | 2019-08-30 | 北京互金新融科技有限公司 | The management method and device of loan limit |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112598244A (en) * | 2020-12-15 | 2021-04-02 | 深圳前海微众银行股份有限公司 | Risk revenue management method, device and system and computer readable storage medium |
| CN112598244B (en) * | 2020-12-15 | 2024-04-16 | 深圳前海微众银行股份有限公司 | Risk profit management method, apparatus, system and computer readable 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 |
| CN116777609A (en) * | 2023-08-25 | 2023-09-19 | 中信消费金融有限公司 | Method and device for processing quota, server and readable storage medium |
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