CN110807653A - Method and device for screening users and electronic equipment - Google Patents
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Abstract
The invention discloses a method, a device, electronic equipment and a computer readable medium for screening users, which comprises the following steps: acquiring user data and creating a user risk portrait; dividing users into a plurality of guest groups according to the user risk portrayal; constructing a risk rule, and filtering users in the multiple guest groups according to the risk rule; segmenting the users in the filtered passenger groups to form passenger group blocks; and screening the guest group blocks according to the income and risk data of the guest group blocks to complete user screening. According to the method and the system, the user risk figures can be used for dividing the guest groups, and the risk rules and the user behavior scores are combined to screen the users in different guest groups, so that accurate risk control is achieved.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a method and a device for screening users, electronic equipment and a computer readable medium.
Background
In the prior art, when the quota is adjusted by screening target customers, the financial platform often focuses on the risk level of the target customers and uses a uniform policy threshold for verification. The head client risk quality selected by the prior art is better, but the coverage rate compared with all clients is lower, the action of promoting the action payment amount is limited, and the long-term optimization of the overall risk level and the business income after the action payment are not facilitated.
Disclosure of Invention
The invention aims to solve the technical problem of how to improve the screening passing rate of users and optimize the overall risk and income.
One aspect of the present invention provides a method for screening users, including: acquiring user data and creating a user risk portrait; dividing users into a plurality of guest groups according to the user risk portrayal; constructing a risk rule, and filtering users in the multiple guest groups according to the risk rule; segmenting the users in the filtered passenger groups to form passenger group blocks; and screening the guest group blocks according to the income and risk data of the guest group blocks to complete user screening.
In a preferred embodiment of the present invention, the acquiring user data and creating a user risk representation further comprises: acquiring user data; dividing the user data into data of a plurality of dimensions; establishing different user tags aiming at data with different dimensions; based on the different user tags, a user risk representation is created.
According to a preferred embodiment of the present invention, the plurality of dimensions further comprises: at least one of an attribute dimension, a behavior dimension, a risk dimension, and a model scoring dimension.
According to a preferred embodiment of the present invention, the dividing the users into a plurality of guest groups according to the user risk profiles further comprises: selecting a user label; establishing a grouping rule based on the selected user label; and analyzing the user risk portrait according to the clustering rule, and dividing the user into a plurality of guest groups.
According to a preferred embodiment of the present invention, the constructing the risk rule further includes: and constructing strong risk rules, wherein the strong risk rules are used for eliminating users in the guest group.
According to a preferred embodiment of the present invention, further comprising: and constructing a weak risk rule, wherein the weak risk rule is used for carrying out differentiation processing on users in the guest group.
According to a preferred embodiment of the present invention, the segmenting the filtered guest group, wherein the user forms a guest group segment, further includes: and acquiring a user behavior score, and segmenting the users in the filtered guest group according to the user behavior score to form guest group blocks.
According to a preferred embodiment of the present invention, the segmenting the filtered user group according to the user behavior score to form a user group partition further includes: sorting the users in the filtered guest groups according to the user behavior scores; and segmenting the users in the filtered guest groups according to the sequencing result to form guest group blocks.
According to a preferred embodiment of the present invention, further comprising: acquiring income and risk data of the guest group blocks; and enabling the income and risk data of the customer group block to accord with monotonicity by adjusting the user behavior score.
A second aspect of the present invention provides an apparatus for screening users, including: the user data acquisition module is used for acquiring user data and creating a user risk portrait; the guest group dividing module is used for dividing the user into a plurality of guest groups according to the user risk portrait; the risk rule constructing and using module is used for constructing risk rules and filtering the users in the multiple guest groups according to the risk rules; the guest group block generation module is used for segmenting the filtered customers in the guest group to form guest group blocks; and the passenger group blocking screening module is used for screening the passenger group blocks according to the income and risk data of the passenger group blocks so as to complete user screening.
According to a preferred embodiment of the present invention, the user data obtaining module further includes: a user data acquisition unit for acquiring user data; a user data dividing unit for dividing the user data into data of a plurality of dimensions; the user label establishing unit is used for establishing different user labels aiming at data with different dimensions; and the user risk portrait creating unit is used for creating the user risk portrait based on the different user tags.
According to a preferred embodiment of the present invention, the plurality of dimensions further comprises: at least one of an attribute dimension, a behavior dimension, a risk dimension, and a model scoring dimension.
According to a preferred embodiment of the present invention, the guest group dividing module further includes: the user label selecting unit is used for selecting a user label; a clustering rule establishing unit, configured to establish a clustering rule based on the selected user tag; and the clustering rule using unit is used for analyzing the user risk portrait according to the clustering rule and dividing the user into a plurality of guest groups.
According to a preferred embodiment of the present invention, the risk rule constructing and using module further includes: and the strong risk rule building unit is used for building a strong risk rule, and the strong risk rule is used for eliminating the users in the guest group.
According to a preferred embodiment of the present invention, further comprising: and the weak risk rule building unit is used for building a weak risk rule, and the weak risk rule is used for carrying out differentiation processing on users in the guest group.
According to a preferred embodiment of the present invention, the guest group block generation module further includes: and the guest group block generating unit is used for acquiring the user behavior scores and segmenting the filtered users in the guest groups according to the user behavior scores to form guest group blocks.
According to a preferred embodiment of the present invention, the guest group block generation unit further includes: the user sorting subunit is used for sorting the users in the filtered guest group according to the user behavior scores; and the user segmentation unit is used for segmenting the users in the filtered passenger groups according to the sequencing result to form the passenger group blocks.
According to a preferred embodiment of the present invention, further comprising: a profit and risk data acquisition unit for acquiring profit and risk data of the guest group block; and the behavior score adjusting unit is used for enabling the income and risk data of the guest group blocks to accord with monotonicity by adjusting the user behavior scores.
A third aspect of the present invention provides an electronic apparatus, wherein the electronic apparatus 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 any of the methods.
A fourth aspect of the invention 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 methods.
The technical scheme of the invention has the following beneficial effects:
according to the invention, the users are grouped by creating the user risk images, so that a differentiated screening strategy is configured according to the characteristics of each passenger group, and the purpose of fine management is achieved.
According to the invention, a differential screening strategy aiming at the characteristics of each passenger group is realized by constructing a strong risk rule and a weak risk rule.
According to the method and the system, the user behavior scores are used for partitioning the guest groups, so that the blocking risk and the blocking income are monotonous, and the effect of optimizing the overall risk and the blocking income is achieved.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only drawings of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive step.
FIG. 1 is a schematic flow chart of a method for screening users according to the present invention;
FIG. 2 is a schematic flow chart of a method of screening users to create a user risk representation according to the present invention;
FIG. 3 is a schematic representation of a user risk profile of a method of screening users of the present invention;
FIG. 4 is a block diagram of an apparatus for screening users according to the present invention;
FIG. 5 is a schematic diagram of a user data acquisition module of an apparatus for screening users according to the present invention;
FIG. 6 is a schematic diagram of an electronic device architecture for screening users in accordance with the present invention;
FIG. 7 is a schematic diagram of a computer readable storage medium of the present invention.
Detailed Description
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 any and all combinations of one or more of the associated listed items.
In the prior art, only single-dimension variables are usually emphasized when target customers are screened according to business characteristics. For example, when the financial platform adjusts the amount by screening the target customers, the financial platform often only focuses on the risk level of the target customers, and omits variables of other dimensions, so that a large amount of data is wasted. In the prior art, a single variable is used, the risk quality of a selected head client is better, but the coverage rate of all clients is lower, the action of the head client on the action of the action payment is limited, and the head client is not beneficial to optimizing the whole risk level for a long time and the business income after the action payment is actuated.
The prior art has the problem of coarse screening granularity when screening target users.
According to the invention, the user data is divided into the data with multiple dimensions by acquiring the user data, and the data with single dimension is avoided.
In addition, the method also comprises the steps of constructing the user risk portrait, dividing the user risk portrait into the guest groups, screening the users in different guest groups by combining the risk rules and the user behavior scores, and the like, so that the target users can be accurately screened, and the risk and the income can be controlled.
FIG. 1 is a schematic flow chart of a method for screening users according to the present invention; as shown in fig. 1, the method of the present invention includes at least steps S101 to S105.
S101: and acquiring user data and creating a user risk representation.
FIG. 2 is a schematic flow chart of creating a user risk representation according to a method of screening users of the present invention, as shown in FIG. 2. Wherein, the acquiring user data and creating a user risk representation further comprises: acquiring user data; dividing the user data into data of a plurality of dimensions; establishing different user tags aiming at data with different dimensions; based on the different user tags, a user risk representation is created.
Wherein the plurality of dimensions further comprises: at least one of an attribute dimension, a behavior dimension, a risk dimension, and a model scoring dimension.
As an example, user data is obtained from various data sources. The data of the user such as age, sex, academic calendar, area, occupation and income are acquired through the job hunting platform; the online shopping platform comprises data such as online shopping software login time, online shopping software login frequency, online shopping commodity types, online shopping commodity prices, monthly online shopping consumption amount and the like; and acquiring data such as overdue times, overdue days, selected installments, platform loan frequency, AI model scores and the like through the financial platform.
And dividing the acquired data into dimensions such as attribute data, behavior data, risk data, model scoring data and the like.
Wherein the model scoring data comprises at least: and (4) scoring the AI model.
And establishing different machine learning models aiming at the attribute data, the behavior data and the risk data, outputting scores aiming at different dimensional data, and obtaining the label of the user according to the scores.
As an example, the attribute class scoring model may be implemented by regression analysis, decision trees, artificial neural networks, support vector machines, K-Means, association rules, and/or time-series pattern algorithms, so that when user attribute data is input, the probability that an attribute is nand can be obtained as an attribute class score, and the attribute and the user score are 1, so as to assign a corresponding user tag; and if the user score of the attribute is not 0, the corresponding user label is not given.
The construction process of the behavior scoring model and the risk scoring model is similar to that of the attribute scoring model, and the description of the construction process is omitted here.
By way of example, FIG. 3 is a schematic representation of a user risk profile of a method of screening users of the present invention; as shown in fig. 3, when the obtained tags of the user include attributes, behaviors, risks, AI model scores, and the like, a user risk representation is created according to the obtained tags.
S102: and dividing the users into a plurality of guest groups according to the user risk representation.
Wherein, according to the user risk portrayal, dividing users into a plurality of guest groups, further comprising: selecting a user label; establishing a grouping rule based on the selected user label; and analyzing the user risk portrait according to the clustering rule, and dividing the user into a plurality of guest groups.
As an example, four labels of attribute AND, attribute NOT, high risk and low risk are selected; and establishing a grouping rule based on four labels of good attribute, bad attribute, high risk and low risk. The grouping rule may be: the users with the attributes and the risks are classified into a guest group A, the users with the attributes and the risks are classified into a guest group B, the users with the attributes and the risks are classified into a guest group C, and the users with the attributes and the risks are classified into a guest group D. According to the established grouping rule, all users can be divided into a guest group A, a guest group B, a guest group C and a guest group D.
S103: and constructing a risk rule, and filtering the users in the multiple guest groups according to the risk rule.
Wherein, constructing the risk rule further comprises: and constructing strong risk rules, wherein the strong risk rules are used for eliminating users in the guest group.
And constructing a weak risk rule, wherein the weak risk rule is used for carrying out differentiation processing on users in the guest group.
By way of example, users meeting strong risk rules are uniformly culled from various guest groups. A strong risk rule, i.e. a rule that does not pass at all, i.e. a hard condition for the user, indicates that the risk for the user is extremely high. For example, a user in the guest group a is overdue for 100 times and the risk of the user is extremely high, and the user in the guest group a is rejected by using the strong risk rule.
After the strong risk rules are adopted for each passenger group, the weak risk rules are respectively analyzed for each passenger group, and because the attributes of each passenger group are different, the weak risk rules do not present effective discrimination for each class of passenger groups, and only the weak risk rules are used for individual passenger groups, so that the differentiation strategies of customer grouping are shown.
The weak risk rules do not eliminate customers, and only do the customer group differentiated amplitude modulation strategy. For example: if the academic record rule exists or not, the risk corresponding value is 0.3% and 0.5%, the amplitude of the academic record client is high, and the amplitude of the academic record client is low.
S104: and segmenting the users in the filtered passenger groups to form the passenger group segments.
Wherein, the segmentation filters the users in the guest group to form guest group blocks, further comprising:
and acquiring a user behavior score, and segmenting the users in the filtered guest group according to the user behavior score to form guest group blocks.
Further, the segmenting, according to the user behavior score, the users in the filtered guest groups into guest group segments further includes: sorting the users in the filtered guest groups according to the user behavior scores; and segmenting the users in the filtered guest groups according to the sequencing result to form guest group blocks.
As an example, Behavior scores of users are obtained by using a Behavior score, the users in the filtered guest group are ranked from high to low according to the user Behavior scores, and the users in the filtered guest group are segmented according to ranking results to form guest group blocks.
The granularity of other strategies such as strong wind and weak risk rules of user behavior scoring is the most exquisite, so that the user behavior scoring is used as the final bottom-pocketing rule to be beneficial to dividing the guest group into small squares.
The method of the invention also comprises the following steps: acquiring income and risk data of the guest group blocks; and making the income and risk data of the customer group block accord with monotonicity by adjusting the user behavior score
S105: and screening the guest group blocks according to the income and risk data of the guest group blocks to complete user screening.
Evaluating the actual risk performance and the actual cash flow performance corresponding to each customer group block, based on the stability after the customer groups and the superiority of a behavior scoring model, according to the behavior scoring, each customer group block divided from high to low should present the profit and the risk of a monotonous trend, eliminating small blocks of which the profit does not reach or is higher than the whole risk, or adjusting the strategy to present monotonicity of the profit and the risk, and obtaining the customer group screening result based on profit-loss balance and optimized risk.
Finally, according to the screening results obtained in steps S10S-105, the policy maker can balance the scale, risk and profit according to his own business objectives (such as KPI objectives), thereby making multiple sets of wind-controlled screening policies based on different business objectives.
According to the invention, the users are grouped by creating the user risk images, so that a differentiated screening strategy is configured according to the characteristics of each passenger group, and the purpose of fine management is achieved.
According to the invention, a differential screening strategy aiming at the characteristics of each passenger group is realized by constructing a strong risk rule and a weak risk rule.
According to the method and the system, the user behavior scores are used for partitioning the guest groups, so that the blocking risk and the blocking income are monotonous, and the effect of optimizing the overall risk and the blocking income is achieved.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
FIG. 4 is a block diagram of an apparatus for screening users according to the present invention; as shown in fig. 4, the apparatus 400 of the present invention includes a user data obtaining module 401, a guest group partitioning module 402, a risk rule constructing and using module 403, a guest group block generating module 404, and a guest group block screening module 405.
And a user data acquisition module 401, configured to acquire user data and create a user risk representation.
And a guest group dividing module 402, configured to divide the user into a plurality of guest groups according to the user risk profile.
A risk rule constructing and using module 403, configured to construct a risk rule, and filter users in the multiple guest groups according to the risk rule.
And a guest group block generating module 404, configured to segment the filtered guest groups into guest group blocks.
And a customer group blocking screening module 405, configured to screen the customer group blocking according to the profit and risk data of the customer group blocking to complete user screening.
FIG. 5 is a schematic diagram of a user data acquisition module of an apparatus for screening users according to the present invention; as shown in fig. 5, the user data obtaining module 401 further includes: the system comprises a user data acquisition unit 501, a user data dividing unit 502, a user label establishing unit 503 and a user risk sketch creating unit 504.
A user data obtaining unit 501, configured to obtain user data.
A user data dividing unit 502, configured to divide the user data into data of multiple dimensions.
A user tag establishing unit 503, configured to establish different user tags for data of different dimensions.
A user risk representation creation unit 504 for creating a user risk representation based on the different user tags.
Wherein the plurality of dimensions further comprises: at least one of an attribute dimension, a behavior dimension, a risk dimension, and a model scoring dimension.
Wherein the guest group dividing module further comprises: the user label selecting unit is used for selecting a user label; a clustering rule establishing unit, configured to establish a clustering rule based on the selected user tag; and the clustering rule using unit is used for analyzing the user risk portrait according to the clustering rule and dividing the user into a plurality of guest groups.
Wherein the risk rule construction and use module further comprises: and the strong risk rule building unit is used for building a strong risk rule, and the strong risk rule is used for eliminating the users in the guest group.
Wherein, further include: and the weak risk rule building unit is used for building a weak risk rule, and the weak risk rule is used for carrying out differentiation processing on users in the guest group.
Wherein the guest group block generation module further comprises: and the guest group block generating unit is used for acquiring the user behavior scores and segmenting the filtered users in the guest groups according to the user behavior scores to form guest group blocks.
Wherein the guest group block generation unit further includes: the user sorting subunit is used for sorting the users in the filtered guest group according to the user behavior scores; and the user segmentation unit is used for segmenting the users in the filtered passenger groups according to the sequencing result to form the passenger group blocks.
The device of the invention also comprises: a profit and risk data acquisition unit for acquiring profit and risk data of the guest group block; and the behavior score adjusting unit is used for enabling the income and risk data of the guest group blocks to accord with monotonicity by adjusting the user behavior scores.
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. 6 is a schematic structural framework diagram of the electronic device for screening users according to the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 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. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 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.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 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 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, 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: acquiring user data and creating a user risk portrait; dividing users into a plurality of guest groups according to the user risk portrayal; constructing a risk rule, and filtering users in the multiple guest groups according to the risk rule; segmenting the users in the filtered passenger groups to form passenger group blocks; and screening the guest group blocks according to the income and risk data of the guest group blocks to complete user screening.
The computer program may be stored on one or more computer readable media, as shown in FIG. 7. 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.
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.
Claims (10)
1. A method of screening users, comprising:
acquiring user data and creating a user risk portrait;
dividing users into a plurality of guest groups according to the user risk portrayal;
constructing a risk rule, and filtering users in the multiple guest groups according to the risk rule;
segmenting the users in the filtered passenger groups to form passenger group blocks;
and screening the guest group blocks according to the income and risk data of the guest group blocks to complete user screening.
2. The method of claim 1, wherein said obtaining user data, creating a user risk representation, further comprises:
acquiring user data;
dividing the user data into data of a plurality of dimensions;
establishing different user tags aiming at data with different dimensions;
based on the different user tags, a user risk representation is created.
3. The method of any of claims 1-2, wherein the plurality of dimensions, further comprises:
at least one of an attribute dimension, a behavior dimension, a risk dimension, and a model scoring dimension.
4. The method of any of claims 1-3, wherein the dividing users into a plurality of guest groups according to the user risk profile further comprises:
selecting a user label;
establishing a grouping rule based on the selected user label;
and analyzing the user risk portrait according to the clustering rule, and dividing the user into a plurality of guest groups.
5. The method of any one of claims 1-4, wherein the constructing a risk rule further comprises:
and constructing strong risk rules, wherein the strong risk rules are used for eliminating users in the guest group.
6. The method of any one of claims 1-5, further comprising:
and constructing a weak risk rule, wherein the weak risk rule is used for carrying out differentiation processing on users in the guest group.
7. The method of any of claims 1-6, wherein segmenting the filtered guest groups into guest group segments, further comprises:
and acquiring a user behavior score, and segmenting the users in the filtered guest group according to the user behavior score to form guest group blocks.
8. An apparatus for screening users, comprising:
the user data acquisition module is used for acquiring user data and creating a user risk portrait;
the guest group dividing module is used for dividing the user into a plurality of guest groups according to the user risk portrait;
the risk rule constructing and using module is used for constructing risk rules and filtering the users in the multiple guest groups according to the risk rules;
the guest group block generation module is used for segmenting the filtered customers in the guest group to form guest group blocks;
and the passenger group blocking screening module is used for screening the passenger group blocks according to the income and risk data of the passenger group blocks so as to complete user screening.
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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