CN113850663A - Data processing method, system, equipment and medium for new user recommendation - Google Patents
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Abstract
The present disclosure relates to a data processing method, system, device and medium for new user recommendation, the method comprising: receiving a new user recommendation request initiated by a first user; judging whether the first user data is a system user, if so, generating and sending an exclusive recommendation link for the first user according to the new user recommendation request; acquiring second user data introduced by the dedicated recommended link, and performing compliance verification on the second user data by using a verification algorithm; binding the second user data which is checked to be in compliance with the first user data to generate binding data; inputting the binding data into a credit granting evaluation model for calculation to obtain a second user credit granting weight grade parameter; and comparing the second user credit authorization level parameter with a predefined rule table to determine the second user credit authorization parameter and the first user recommended reward authorization parameter. The method and the device can improve the bargain amount of the credit business and avoid the bad account risk of the credit business.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method, a system, equipment and a medium for new user recommendation.
Background
The existing recommendation methods related to financial products are all that systems directionally recommend related products by analyzing the data of existing users, and the business expansion of new users cannot be effectively realized; meanwhile, due to the lack of credit wind control measures for new users, the situation of bad accounts exists in many credit businesses.
Disclosure of Invention
The invention mainly aims to provide a data processing method, a system, equipment and a medium for new user recommendation, aiming at improving the marketing efficiency of credit service and realizing the rapid popularization of product service and the efficient expansion of partners by legally utilizing interpersonal resources and partner marketing reward mechanisms. Meanwhile, effective wind control of credit business is realized through a partner constraint mechanism, and the occurrence of bad account risk is reduced and prevented.
In order to achieve the above object, the present invention provides a data processing method for new user recommendation, including:
receiving a new user recommendation request initiated by a first user;
judging whether the first user data is a system user, if so, generating and sending an exclusive recommendation link for the first user according to a new user recommendation request;
acquiring second user data introduced by the exclusive recommended link, and performing compliance verification on the second user data by using a verification algorithm;
binding the second user data with the first user data to generate binding data;
inputting the binding data into a credit granting evaluation model for calculation to obtain a second user credit granting weight grade parameter;
and comparing the second user credit authorization level parameter with a predefined rule table to determine a second user credit authorization parameter and a first user recommended bonus authorization parameter.
Further, binding the second user data of the verified compliance with the first user data to generate binding data, specifically comprising:
taking the first user data as a main analysis item and taking the second user data of the verification compliance as an auxiliary analysis item;
binding the primary analysis item and the secondary analysis item in sequence to generate binding data;
wherein the first user data comprises: the first user is the server side and has the user identity information, vehicle information, historical borrowing information, repayment information, credit granting data and credit granting amount;
wherein the second user data comprises: the second user is the identity information, the vehicle information, the credit data and the credit line of the new user at the server end, and the borrowing information comprises the borrowing amount, the borrowed product deadline, the contract signing, the emergency contact information and the face image information.
Further, the binding data is input into a credit granting evaluation model for calculation to obtain a second user credit granting weight grade parameter, which specifically comprises:
a credit assessment model is constructed in advance;
inputting the main analysis item and the auxiliary analysis item into a credit assessment model for matrix calculation to obtain relative importance weights corresponding to each credit assessment index;
and carrying out mean value calculation on the relative importance weight by using a Delphi algorithm to obtain a second user credit granting weight grade parameter.
Further, after obtaining second user data introduced by the dedicated recommended link and performing compliance verification on the second user data by using a verification algorithm, the method further includes:
judging whether the first user assumes guarantee responsibility to a second user who checks the compliance;
if so, performing wind control evaluation on the second user data according to a predefined rule table to obtain a second user credit granting weight grade parameter; and if not, executing the step of binding the second user data with the first user data to generate binding data.
Further, after determining the second user credit line parameter and the first user recommended bonus line parameter, the method also comprises the following steps:
calculating the amount of the borrowed money approved by the second user according to the second user credit line parameter and the amount of the borrowed money requested by the second user;
and calculating the recommended reward amount of the first user according to the recommended reward amount parameter of the first user and the debit amount requested by the second user.
Further, after calculating the amount of the borrowed money approved by the second user according to the second user credit line parameter and the amount of the borrowed money requested by the second user, the method further comprises the following steps:
receiving repayment request data initiated by a second user, and evaluating according to a predefined rule table;
judging whether the repayment request data meets the due repayment standard or not, and if so, issuing a recommended reward amount for the first user; the issued recommended reward amount can be issued once or in batches;
and when the repayment request data does not meet the due repayment standard, continuously judging whether the first user credit granting weight grade parameter meets the credit granting descending standard, if so, performing corresponding credit granting descending on the first user credit granting weight grade parameter according to a predefined rule table, and if not, deducting corresponding amount from the recommended award amount of the first user according to the predefined rule table.
In order to achieve the above object, the present invention further provides a data processing system for new user recommendation, the system comprising:
the data extraction module is used for acquiring user data from a user side, analyzing and verifying the user data and extracting the user data which is in compliance;
the credit granting risk evaluation module is used for carrying out credit granting risk evaluation on the user data which is verified to be in compliance by the data extraction module to generate an evaluation result, judging whether an exclusive recommendation link and a recommendation result are sent to a user side corresponding to the user data or not, and initiating a debit and/or a reward fund;
the user recommendation module is used for receiving new user recommendation request data from the user side and generating an exclusive recommendation link and a recommendation result for the user side meeting the credit granting standard according to the evaluation result of the credit granting risk evaluation module;
and the fund initiating and paying module is used for initiating borrowing and/or rewarding for the user side meeting the credit granting standard according to the evaluation result of the credit granting risk evaluation module and completing payment.
The system further comprises:
the fund clearing module is used for receiving repayment request data from the user side and receiving repayment according to the borrowing condition of the fund initiating and paying module to clear;
and the risk constraint module is used for verifying the funds received and cleared by the fund clearing module and correspondingly constraining the guarantee and default responsibility of the user side according to a risk constraint mechanism.
To achieve the above object, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program, the program being executed by a processor to perform the method of the first aspect.
The beneficial effect of this disclosure does:
the method effectively enlarges the number of credit users by utilizing the active recommendation mode of the existing credit users to new users, and enhances the recommendation activity of the credit users and increases the transaction amount of credit business by a recommendation reward mechanism; meanwhile, the credit business is helped to better evaluate the credit risk of the new user by binding and comprehensively evaluating the credit data of the new user and the credit user, and the transaction amount of the credit business is increased and the bad account risk of the credit business is avoided by an incentive mechanism and a constraint mechanism of the existing credit user. In general, the new users recommended by the credit users are a group of groups with similar credit rating, so that the credit service platform has certain referential and commercial value for grasping the credit risk of the new users.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a data processing method for new user recommendation according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a data processing method for guaranteeing recommendation to a new user according to embodiment 2 of the present invention.
Fig. 3 shows a flow chart of a credit user constraint method based on credit pneumatic control according to embodiment 3 of the invention.
FIG. 4 shows a flow chart of a credit service platform-based multi-party interactive behavior method according to embodiment 4 of the invention.
Fig. 5 shows a flowchart of a partner recommending method based on a credit service platform according to embodiment 5 of the invention.
FIG. 6 is a functional block diagram of a data processing system oriented to new user recommendations according to embodiment 6 of the present invention.
Fig. 7 is a schematic structural diagram of an electronic device according to embodiment 7 of the present invention.
Fig. 8 is a schematic diagram showing a storage medium according to embodiment 8 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example 1
As shown in fig. 1, this embodiment provides a data processing method for new user recommendation, where a recommender client is equivalent to a first user, user data of the recommender client corresponds to first user data, a recommended client is equivalent to a second user, and user data of the recommended client corresponds to second user data, the method includes:
and S1, receiving new user recommendation request data initiated by the recommender client.
And S2, checking the user data of the recommender client, judging whether the user data is a system user, if so, generating and sending an exclusive recommendation link for the recommender client, and if not, automatically guiding the recommender client to complete the acquisition and analysis of the user data.
S3, registration and trust request data initiated by the client of the recommended person brought by the exclusive recommended link are received, and the client of the recommended person is automatically guided to finish the collection and analysis of user data; and carrying out compliance verification on the collected and analyzed user data of the recommended person client by using a verification algorithm.
The checking algorithm is predefined, and may be performed by querying a white list in a database, for example. For example, if the recommender belongs to a white list user, the data of the recommender is verified to be qualified, and if the recommender belongs to a black list user, the data of the recommender is verified to be unqualified, and the recommender is not allowed to recommend a new user.
And S4, binding the user data of the recommended person client terminal with the verified compliance with the user data of the recommended person client terminal to generate binding data.
Wherein S4 further includes:
s4.1, taking the user data of the recommender client with the checked compliance as a main analysis item, and taking the user data of the recommender client with the checked compliance as an auxiliary analysis item;
and S4.2, sequentially binding the primary analysis item and the secondary analysis item to generate binding data. As shown in table 1.
Table 1 binding data table of main analysis item and sub analysis item
The user data of the recommender client comprises: the user already has the identity information, vehicle information, historical borrowing information, repayment information, credit granting data, credit granting amount and the like of the user for the platform.
The user data of the recommended person client comprises the following steps: the user is the identity information, the vehicle information, the credit data and the credit amount of the new user of the platform, and the borrowing information comprises the borrowing amount, the borrowing product period, contract signing, emergency contact information, face image information and the like.
And S5, inputting the binding data into the credit granting evaluation model for calculation to obtain an evaluation result.
Wherein S5 further includes:
s5.1, constructing a credit assessment model as shown in a table 2;
TABLE 2 Credit assessment model Table
S5.2, inputting the primary analysis item and the secondary analysis item into a credit assessment model for calculation;
wherein S5.2 further comprises:
s5.2.1, calculating index values corresponding to all credit assessment indexes by using a fuzzy hierarchical analysis algorithm to obtain relative importance weights;
wherein, wiIs an evaluation index u of each creditiCorresponding relative importance weight, α is each index value uijIn betweenDifference value, i.e. according to uijCalculate wiThe formula is as follows:
s5.2.2, performing matrix calculation on the obtained relative importance weight.
Where T is the matrix order, wijIs an index value uijThe fuzzy consistency judgment matrix obtained after T times of calculation is according to uijCalculate wijAccording to wijCalculate wiThe formula is as follows:
T＝1，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
T＝2，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
......
T＝t，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
according to wi＝[wi1，wi2，......，wij]TAnd then:
w1＝[w11，w12，......，w1t]
w2＝[w21，w22，......，w2t]
......
w6＝[w61，w62，......，w6t]
and S5.3, calculating the credit weight grade by utilizing a Delphi algorithm to obtain an evaluation result.
Wherein W is the credit weight grade corresponding to the credit evaluation model, m represents the number of relative importance weights meeting the conditions, WiCredit assessment index u representing ith compliance conditioniCorresponding relative importance weights, i.e. in accordance with wiW is calculated by using the Delphi algorithm, and the formula is as follows:
e.g., α ═ 3 and T ═ 2, credit assessment indicator u2、u4And then:
then it is determined that,
then it is determined that,
w2＝[0.56,0.46]then, then
w4＝[0.17,0.13]Then, then
Then it is determined that,
if the threshold range of the third level is (0.2,0.59), the trust weight level of the recommended person corresponding to the bound data is third level.
And S6, determining the borrowable amount of the client of the recommended person according to the evaluation result and the credit awarding rule, initiating the borrowing and completing the payment.
Wherein S6 further includes:
s6.1, establishing a predefined credit granting and rewarding rule as shown in a table 3;
table 3 credit reward rule table
S6.2, determining a credit line corresponding to the client of the recommended person according to the credit weight grade;
s6.3, calculating the borrowable amount of the client of the recommended person according to the credit line;
and S6.4, initiating borrowing for the client side of the recommended person meeting the borrowing standard, and completing payment.
For example, when the credit weighting level is three levels, the corresponding credit line is 80%, the recommended reward line is 0.16%, and if the requested debit amount is 30000 yuan, the approved debit amount 30000x 80% is 24000 yuan, and the recommended reward amount 24000x 0.16% is 38.4 yuan.
The embodiment further comprises:
and S7, determining the recommended reward amount of the client of the recommender according to the evaluation result and the credit reward rule, sending a reward result and completing payment.
S7.1, determining a recommendation reward limit corresponding to the client of the recommender according to the credit authorization level;
s7.2, calculating the recommended reward amount of the client of the recommender according to the recommended reward amount;
and S7.3, sending an award result to the client of the recommender meeting the recommendation award standard, and completing payment.
The recommended reward amount can be issued once or in batches.
And the credit awarding rule is predefined, the corresponding credit awarding amount and the recommended awarding amount are determined according to the credit weighting level, and the approved debit amount and the recommended awarding amount are calculated according to the requested debit amount.
Example 2
As shown in fig. 2, this embodiment provides a data processing method for guaranteeing recommendation for a new user, and after the step of "performing compliance verification on the collected and analyzed user data of the recommended person client by using a verification algorithm" in embodiment 1 is executed, the method further includes:
s4', judging whether the recommender client end for checking the compliance undertakes the guarantee responsibility to the registration and credit request initiated by the recommender client end.
S5', when the client of the recommender undertakes the guarantee responsibility to the register and credit request initiated by the client of the recommender, the wind control evaluation is carried out to the user data of the client of the recommender according to the unilateral wind control evaluation rule to obtain the evaluation result; otherwise, the step of "binding the user data of the recommended person client checked for compliance with the user data of the recommended person client to generate binding data" in the embodiment 1 is skipped, and the subsequent method is continuously executed.
The recommended bonus can be all bonus points recommended at this time, or can be partial bonus points distributed according to the proportion, and the rest bonus points can be paid after the client of the recommended person completes the obligation of repayment.
When the recommender chooses to bear the guarantee responsibility to the recommenders, the user data of the recommender and the recommenders do not need to be bound and then brought into the wind control evaluation model for comprehensive evaluation, and the user data of the recommenders can be paid out only by simply evaluating the user data of the recommenders according to the unilateral wind control evaluation rule, so that the credit granting and borrowing verification speed, the credit granting probability and the borrowing amount of the recommenders are improved.
Example 3
As shown in fig. 3, the present embodiment provides a credit user constraint method based on credit wind control, wherein a credit user corresponds to a recommender, and the method includes:
and S8, receiving a repayment request initiated by the client of the recommended person.
And S9, evaluating the repayment data corresponding to the repayment request according to the due repayment rule.
The due repayment rule is predefined, and is set according to the due repayment standard of the borrowed money and the credit descending standard, as shown in table 4.
TABLE 4 due repayment rule Table
And S10, when the repayment data of the client of the recommended person meets the due repayment standard, issuing the remaining part of reward money, otherwise, judging whether the credit weight grade of the client of the recommended person meets the credit reduction standard.
And S11, when the credit granting weight level of the client of the recommender meets the credit granting descending weight standard, performing corresponding credit granting descending on the credit granting weight level of the client of the recommender according to the due repayment rule, otherwise, deducting the corresponding amount of money from the residual award which is not issued by the client of the recommender according to the proportion according to the due repayment rule and then issuing the balance.
The method comprises the steps of adding penalty measures of a second constraint mechanism of a recommender, for example, the recommender is not in line with an expired repayment standard (default), preferentially reducing the trust weight level of the recommender by the system, and if the trust weight level of the recommender is not enough to reach the trust reduction standard, deducting a certain default gold from the rest reward gold of the recommender as the penalty in proportion. Meanwhile, the system can improve the wind control examination standard for the following recommended persons.
For example, if the recommender assumes a guarantee liability for the recommender, when the amount of borrowed money granted by the recommender is 24000 yuan and the borrowing period is 12 months, the borrowing rate is 6.7%, and the repayment amount is 24000x (1+ 6.7%) is 25608 yuan (inclusive), if the delayed repayment period exceeds 30 days and is less than 180 days, the recommender assuming the guarantee liability should drop the right by three levels, if the trust weight level of the recommender is one level and does not meet the trust drop right requirement, the corresponding default late fund should be deducted from the remainder of the reward fund, the default late fund amount is 25%, and when the recommended reward fund 38.4 yuan has paid 50%, the remainder of the payable reward fund 38.4x 50% -38.4x 25% is 9.6 yuan.
Example 4
As shown in fig. 4, the present embodiment provides a credit service platform-based multi-party behavior interaction method, an application scenario of the present embodiment relates to interaction of three-party behaviors, a recommender client is equivalent to an existing user, a recommender client is equivalent to a new user, and a system is equivalent to a credit service platform. The method comprises the following steps:
s100, existing users initiate recommendation requests to the credit service platform.
Wherein S100 further comprises:
s101, the credit service platform receives the existing user recommendation request and inquires the financial information of the existing user;
s102, the credit service platform checks the analyzed data according to a predefined checking algorithm and judges whether the user is in compliance;
s103, if the user is not in compliance, the user fails to recommend the user;
s104, the credit service platform generates and sends a special recommendation link for the existing user, wherein the link comprises a recommended user account, a recommended service scene and the like.
The financial information of the user comprises identity information, vehicle information, historical borrowing information, repayment information, credit granting data, credit granting amount and the like of the user who is the existing user of the platform.
And S200, sharing the link to the new user by the existing user.
Wherein, S200 further comprises:
s201, a new user acquires and clicks a recommendation link, and registers user information on a credit service platform;
s202, a new user submits a credit application on a credit service platform;
s203, the credit service platform checks the analyzed data according to a predefined checking algorithm and judges whether the user is in compliance;
s204, if the new user fails to comply with the standard, feeding back the credit authorization failure of the new user;
s205, the credit service platform binds the information data of the existing user and the new user and stores the recommendation information of the existing user.
The user information comprises identity information, financial information, vehicle information, face image information and the like of a new user of the platform.
And S300, the new user submits a borrowing application.
Wherein S300 further comprises:
s301, a new user submits borrowing information on a credit service platform;
s302, the credit service platform checks the analyzed data according to a predefined checking algorithm and judges whether the user is in compliance;
s303, if the borrowing is not in compliance, the new user is fed back to be unsuccessfully borrowed;
s304, the credit service platform judges the credit wind control level and the credit line of the new user and releases the loan.
The borrowing information comprises a borrowing amount, a borrowed product period, contract signing, emergency contact information and the like.
And S400, feeding back the existing user recommendation result.
Wherein S400 further comprises:
s401, the credit service platform acquires the borrowing information and the user information of a new user;
s402, the credit service platform checks the data according to a predefined checking algorithm and judges whether the new user is in compliance;
and S403, the credit service platform feeds back the recommendation result to the existing user.
The recommendation result comprises a borrowing result of a new user, whether the borrowing period is within the effective time after the recommendation is stipulated, and the like.
S500, the existing users submit reward fund requests.
And the credit service platform issues bonus money to the existing user to complete the recommendation process.
Example 5
As shown in fig. 5, the present embodiment provides a new partner recommendation method based on a credit service platform, and an application scenario of the present embodiment relates to interaction of three-party behaviors, where a recommender client is equivalent to a credit user, a recommender client is equivalent to a new partner, and a system is equivalent to a credit service platform. The method comprises the following steps:
(1) the credit user is a user existing in the credit service platform, and submits a recommendation request of a new partner to the credit service platform;
(2) the credit service platform audits the recommendation request and the permission of the credit user, and sends an exclusive recommendation link to the credit user if the recommendation request and the permission meet the partner recommendation standard;
(3) the credit user shares own exclusive recommended link to a new partner;
(4) the new partner is linked in a specified period through the shared exclusive recommendation, and the account is registered on the credit service platform and credited successfully;
(5) the credit service platform carries out payment operation according to the borrowing request of the new partner;
(6) the credit service platform issues recommendation rewards to credit users who successfully complete new partner recommendations and obtain the recommendation rewards.
Example 6
As shown in fig. 6, the present embodiment provides a data processing system for new user recommendation, which is equivalent to the credit service platform of embodiments 4 and 5, and the system/platform includes the following modules:
the data extraction module 501: the system comprises a user terminal, a data acquisition module, a data analysis module and a data processing module, wherein the user terminal is used for acquiring user data from the user terminal, analyzing and verifying the user data and extracting the user data which is in compliance;
trust risk assessment module 502: the system is used for performing credit risk assessment on the user data which is verified to be in compliance by the data extraction module 501 to generate an assessment result, judging whether an exclusive recommendation link and a recommendation result are sent to a user side corresponding to the user data, and initiating a debit and/or a reward fund;
the user recommendation module 503: the system is used for receiving new user recommendation request data from the user side and generating an exclusive recommendation link and a recommendation result for the user side meeting the credit granting standard according to the evaluation result of the credit granting risk evaluation module 502;
funds initiation and payment module 504: the credit risk assessment module 502 is used for initiating borrowing and/or rewarding for the user terminals meeting the credit standard according to the assessment result of the credit risk assessment module, and completing payment.
The system/platform also includes the following modules:
the fund clearing module 505: for receiving the repayment request data from the user side and receiving the repayment according to the borrowing condition of the fund initiating and paying module 504 for clearing;
risk constraint module 506: the fund for checking and clearing module 505 receives and clears the fund, and carries out corresponding constraint on the guarantee and default responsibility of the user terminal according to the risk constraint mechanism.
The data processing system recommended for the new user and the data processing method recommended for the new user according to the embodiment 1 have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the application program stored in the data processing system.
The embodiment of the application also provides electronic equipment corresponding to the data processing method recommended for the new user, which is provided by the foregoing embodiment, so as to execute the data processing method recommended for the new user. The embodiments of the present application are not limited.
Example 7
As shown in fig. 7, the present embodiment provides a schematic structural diagram of an electronic device. The electronic device 2 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the data processing method for new user recommendation provided by any of the foregoing embodiments when executing the computer program.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the apparatus and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the data processing method recommended for the new user provided by the embodiment of the application have the same inventive concept and the same beneficial effects as the method adopted, operated or realized by the electronic device.
Example 8
As shown in fig. 8, the present embodiment provides a computer-readable storage medium corresponding to the data processing method for new user recommendation provided in the foregoing embodiment, and the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored, where the computer program, when executed by a processor, executes the data processing method for new user recommendation provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above embodiment of the present application and the data processing method recommended for the new user provided by the embodiment of the present application have the same inventive concept, and have the same beneficial effects as the method adopted, run, or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application 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 a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present application. The present application 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 application may be stored on a computer readable medium 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.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A data processing method for new user recommendation is characterized by comprising the following steps:
receiving a new user recommendation request initiated by a first user;
judging whether the first user data is a system user, if so, generating and sending an exclusive recommendation link for the first user according to the new user recommendation request;
acquiring second user data introduced by the dedicated recommended link, and performing compliance verification on the second user data by using a verification algorithm;
binding the second user data which is checked to be in compliance with the first user data to generate binding data;
inputting the binding data into a credit granting evaluation model for calculation to obtain a second user credit granting weight grade parameter;
and comparing the second user credit authorization level parameter with a predefined rule table to determine the second user credit authorization parameter and the first user recommended reward authorization parameter.
2. The method according to claim 1, wherein binding the second user data of the verified compliance with the first user data to generate binding data, specifically comprises:
taking the first user data as a main analysis item and the second user data of the checked compliance as an auxiliary analysis item;
binding the main analysis item and the auxiliary analysis item in sequence to generate binding data;
wherein the first user data comprises: the first user is the server side and has the user identity information, vehicle information, historical borrowing information, repayment information, credit granting data and credit granting amount;
wherein the second user data comprises: the second user is the identity information, the vehicle information, the credit data and the credit line of the new user at the server end, and the borrowing information comprises the borrowing amount, the borrowed product deadline, the contract signing, the emergency contact information and the face image information.
3. The method according to claim 1, wherein the step of inputting the binding data into a credit assessment model for calculation to obtain a second user credit weighting level parameter comprises:
a credit assessment model is constructed in advance;
inputting the main analysis item and the auxiliary analysis item into the credit assessment model for matrix calculation to obtain relative importance weight corresponding to each credit assessment index;
and carrying out mean value calculation on the relative importance weight by utilizing a Delphi algorithm to obtain a second user credit granting weight grade parameter.
4. The method according to claim 3, wherein the main analysis item and the auxiliary analysis item are input into the credit assessment model for matrix calculation to obtain relative importance weights corresponding to each credit assessment index, and specifically comprises:
calculating the index values corresponding to the credit evaluation indexes by using a fuzzy hierarchical analysis algorithm to obtain relative importance weights, wherein the calculation formula is as follows:
wherein, wiIs an evaluation index u of each creditiCorresponding relative importance weight, α is each index value uijThe difference value between, i.e. according to uijCalculate wiN is a positive integer greater than or equal to 1;
performing matrix calculation on the obtained relative importance weights to obtain the relative importance weights corresponding to the credit evaluation indexes, wherein the calculation formula is as follows:
T＝1，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
T＝2，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
……
T＝t，u1＝[u11，u12]，u2＝[u21，u22]，......，u6＝[u61，u62，u63，u64，u65]
according to wi＝[wi1，wi2，......，wij]TAnd then:
w1＝[w11，w12，......，w1t]
w2＝[w21，w22，......，w2t]
……
w6＝[w61，w62，......，w6t]
where T is the matrix order, wijIs an index value uijThe fuzzy consistency judgment matrix obtained after T times of calculation is according to uijCalculate wijAccording to wijCalculate wi。
5. The method according to claim 3, wherein the relative importance weight is averaged by using a Delphi algorithm to obtain the second user credit weight ranking parameter, and the calculation formula is as follows:
wherein W is the credit weight grade corresponding to the credit evaluation model, m represents the number of relative importance weights meeting the conditions, WiCredit assessment index u representing ith compliance conditioniCorresponding relative importance weights, i.e. in accordance with wiW is calculated using the delphire algorithm.
6. The method according to any of claims 1-5, wherein second user data introduced by the dedicated recommended link is obtained, and after performing compliance check on the second user data by using a check algorithm, the method further comprises:
determining whether the first user assumes a warranty responsibility for a second user checking compliance;
if yes, performing wind control evaluation on the second user data according to the predefined rule table to obtain a second user credit granting weight grade parameter; and if not, executing the step of binding the second user data which is checked to be in compliance with the first user data to generate binding data.
7. The method of claim 1, wherein after determining the second user credit line parameter and the first user recommended reward line parameter, the method further comprises:
calculating the amount of the borrowed money approved by the second user according to the second user credit line parameter and the amount of the borrowed money requested by the second user;
and calculating the recommended reward amount of the first user according to the recommended reward amount parameter of the first user and the debit amount requested by the second user.
8. The method as claimed in claim 7, wherein after calculating the amount of the loan authorized by the second user according to the second user credit line parameter and the amount of the loan requested by the second user, the method further comprises:
receiving repayment request data initiated by the second user, and evaluating against the predefined rule table;
judging whether the repayment request data meets an expired repayment standard or not, and if so, issuing a recommended reward amount for the first user; the issued recommended reward amount can be issued once or in batches;
and when the repayment request data does not accord with an expired repayment standard, continuously judging whether the first user credit granting weight grade parameter meets the credit granting descending standard, if so, performing corresponding credit granting descending on the first user credit granting weight grade parameter according to the predefined rule table, and if not, deducting corresponding amount from the recommended reward amount of the first user according to the predefined rule table.
9. A data processing system for new user recommendation, comprising:
the data extraction module is used for acquiring user data from a user side, analyzing and verifying the user data and extracting the user data which is in compliance;
the credit granting risk evaluation module is used for performing credit granting risk evaluation on the user data which is verified to be in compliance by the data extraction module to generate an evaluation result, judging whether an exclusive recommendation link and a recommendation result are sent to a user side corresponding to the user data, and initiating a debit and/or a reward fund;
the user recommendation module is used for receiving new user recommendation request data from the user side and generating the exclusive recommendation link and recommendation result for the user side meeting the credit granting standard according to the evaluation result of the credit granting risk evaluation module;
and the fund initiating and paying module is used for initiating the borrowing and/or the rewarding money for the user side meeting the credit granting standard according to the evaluation result of the credit granting risk evaluation module and completing payment.
10. The system of claim 9, further comprising:
the fund clearing module is used for receiving repayment request data from the user side and receiving repayment according to the borrowing condition of the fund initiating and paying module to clear;
and the risk constraint module is used for verifying the funds received and cleared by the fund clearing module and correspondingly constraining the guarantee and default responsibility of the user side according to a risk constraint mechanism.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps corresponding to the data processing method for new user recommendation as claimed in any one of claims 1 to 8 when executing the computer program.
12. A computer storage medium having computer program instructions stored thereon, wherein the program instructions, when executed by a processor, are adapted to implement the steps corresponding to the new user recommendation oriented data processing method of any of claims 1 to 8.
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