CN112784219A - User risk prediction method and device based on APP index and electronic equipment - Google Patents
User risk prediction method and device based on APP index and electronic equipment Download PDFInfo
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- G06F17/10—Complex mathematical operations
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Abstract
The invention discloses a user risk prediction method, a user risk prediction device and electronic equipment based on an APP index, wherein the method comprises the following steps: determining a historical user risk label according to the behavior information of the historical user; acquiring a first history user for installing a specified APP in a preset time period; determining a risk index of the designated APP according to the risk label of the first historical user; and determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user. According to the method, a large amount of time and energy are not needed to be invested to mine and maintain APP data characteristics, only historical user risk labels of the APP are needed to be collected to determine the APP risk index, and the user risk can be predicted according to the APP risk index installed by a user. The wind control cost can be effectively saved.
Description
Technical Field
The invention relates to the technical field of computer information processing, in particular to a user risk prediction method and device based on an APP index, electronic equipment and a computer readable medium.
Background
With the rapid popularization and rapid development of intelligent mobile terminals, various application programs (APP) with various functions are produced. It is a common practice in the financial field to obtain information related to the client terminal APP for risk control when the client is sufficiently authorized.
The existing APP risk model generally uses logistic regression or some integrated algorithms, such as GBDT, xgboost and the like. Such algorithms generally require a certain mining of features in advance to construct discriminative variables, which are then fed into a model for training. And a large amount of feature mining work is needed for building the model, for example, for mining of client APP information, the APP needs to be classified first, and then client portrait is performed by counting the installation and use preference of a client to a certain type of APP. In practice, the classification of APPs is very difficult to maintain because many APPs have multiple functions, and it is difficult to classify the APPs into a certain class. For example, an APP has not only social attributes but also functions of financing, payment and the like. This results in a large investment in digging features and troublesome feature maintenance.
Disclosure of Invention
The invention aims to solve the technical problem of high input cost caused by the fact that a large amount of time and energy are needed to be invested in the conventional APP wind control method to mine and maintain APP data characteristics.
In order to solve the above technical problem, a first aspect of the present invention provides a user risk prediction method based on APP index, where the method includes:
determining a historical user risk label according to the behavior information of the historical user;
acquiring a first history user for installing a specified APP in a preset time period;
determining a risk index of the designated APP according to the risk label of the first historical user;
and determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
According to a preferred embodiment of the present invention, the behavior information is overdue behavior, if the historical user has overdue behavior, the corresponding risk label is risky, and if the historical user has no overdue behavior, the corresponding risk label is risk-free.
According to a preferred embodiment of the invention, the risk index W for the ith given APPoeiObtained by the following formula:
wherein i represents the ith designated APP, BadiThe number of historical users with risk labels of the ith appointed APP in the historical users, GoodiThe number of historical users with risk labels of the ith appointed APP installed is no risk, BadTThe number of users with risk labels of all users who install the ith appointed APP, GoodTThe number of users with risk labels of risk-free in all users for installing the ith appointed APP is pointed.
According to a preferred embodiment of the invention, the risk of the current user is determined according to the risk indexes of all the appointed APPs installed by the current user and the weight of each appointed APP of the current user;
or inputting the risk indexes of all appointed APPs installed by the current user into a preset model, and determining the current user risk according to the output result of the preset model.
According to a preferred embodiment of the present invention, the weight of each designated APP of the current user is determined according to the time and/or frequency of using the designated APP by the current user.
According to a preferred embodiment of the present invention, the given APP is based on the installation rate of the APP, the type of APP and the risk index W of the APPoeiIs determined.
In order to solve the above technical problem, a second aspect of the present invention provides an apparatus for predicting user risk based on APP index, the apparatus comprising:
the first determining module is used for determining the risk label of the historical user according to the behavior information of the historical user;
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a first historical user for installing a specified APP in a preset time period;
a second determining module, configured to determine a risk index of the specified APP according to the risk label of the first historical user;
and the third determining module is used for determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
According to a preferred embodiment of the present invention, the behavior information is overdue behavior, if the historical user has overdue behavior, the corresponding risk label is risky, and if the historical user has no overdue behavior, the corresponding risk label is risk-free.
According to a preferred embodiment of the present invention, the obtaining module obtains the risk index W of the ith specified APP by the following formulaoei：
Wherein i represents the ith designated APP, BadiThe number of historical users with risk labels of the ith appointed APP in the historical users, GoodiThe number of historical users with risk labels of the ith appointed APP installed is no risk, BadTThe number of users with risk labels of all users who install the ith appointed APP, GoodTThe number of users with risk labels of risk-free in all users for installing the ith appointed APP is pointed.
According to a preferred embodiment of the present invention, the third determining module determines the risk of the current user according to the risk indexes of all the designated APPs installed by the current user and the weight of each designated APP of the current user;
or the third determining module inputs risk indexes of all appointed APPs installed by the current user into a preset model, and determines the current user risk according to the output result of the preset model.
According to a preferred embodiment of the present invention, the weight of each designated APP of the current user is determined according to the time and/or frequency of using the designated APP by the current user.
According to a preferred embodiment of the present invention, the given APP is based on the installation rate of the APP, the APP type and the risk of the APPIndex WoeiIs determined.
To solve the above technical problem, a third aspect of the present invention provides an electronic device, comprising:
a processor; and
a memory storing computer executable instructions that, when executed, cause the processor to perform the method described above.
To solve the above technical problems, a fourth aspect of the present 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 the above method.
The present invention takes into account that risky users (e.g., overdue users, fraudulent users, etc.) may prefer to install certain APPs for some purpose. For example, a fraudulent user may install some chat, social, credit-type APPs for the purpose of fraud on money. Therefore, the risk index of each APP is determined according to the historical risk label of each APP installation user, and then the risk of the current user is determined according to the risk indexes of all the appointed APPs installed by the current user. According to the method, a large amount of time and energy are not needed to be invested to mine and maintain APP data characteristics, only historical user risk labels of the APP are needed to be collected to determine the APP risk index, and the user risk can be predicted according to the APP risk index installed by a user. The wind control cost can be effectively saved.
Drawings
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 illustrations 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 user risk prediction method based on APP indexes;
FIG. 2 is a schematic diagram of the present invention for obtaining a first historical user installing a specific APP within a preset time period;
FIG. 3 is a schematic structural framework diagram of a user risk prediction device based on APP indexes;
FIG. 4 is a block diagram of an exemplary embodiment of an electronic device in accordance with the present invention;
FIG. 5 is a schematic diagram of one embodiment of a computer-readable medium of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention may be embodied in many specific 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 structures, properties, effects or other characteristics described in a certain embodiment may be combined in any suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features.
The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities 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.
The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only 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. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.
The method achieves a certain purpose based on the fact that risk users (such as overdue users, cheat users and the like) tend to install certain APPs. For example, a fraudulent user may install some chatting, social, credit APPs to achieve the purpose of fraud of money and money; overdue users may install some credit-type APPs to increase the line of borrowing. Thus, each APP has a different risk index for different risks (e.g., risk of fraud, risk of overdue). Based on the method, the risk index of the APP is determined according to the historical risk label of each APP installation user, and then the risk of the current user is determined according to the risk indexes of all the appointed APPs installed by the current user. According to the method, a large amount of time and energy are not needed to be invested to mine and maintain APP data characteristics, only historical user risk labels of the APP are needed to be collected to determine the APP risk index, and the user risk can be predicted according to the APP risk index installed by a user. The wind control cost can be effectively saved.
Referring to fig. 1, fig. 1 is a flowchart of a user risk prediction method based on APP index according to the present invention. As shown in fig. 1, the method includes:
s1, determining a historical user risk label according to the behavior information of the historical user;
the behavior information is used for marking a user risk label. Thus, the behavioral information may vary from risk type to risk type. For example, the behavior information for calibrating the fraud risk may be: whether there is fraud. The method and the system are mainly used for marking overdue risks of the user, so overdue behaviors are adopted as behavior information of the user. If the historical user has overdue behavior, the corresponding risk label is at risk and can be marked by 1; if the historical user has no overdue behavior, the corresponding risk label is no risk, and can be marked by adopting '0'.
S2, acquiring a first historical user for installing a specified APP in a preset time period;
among them, the designated APPs are some APPs capable of stably expressing the risk of the user installing the APPs. Can be determined according to the installation rate of APP, the type of APP and the risk index W of the APPoeiIs determined. For example, in this step, the installation rate of APP may be selected to be greater than 75%, and meanwhile, the APP types are: one of loan, health, chat is designated APP. The risk index W for each given APP is then obtained via step S3oeiRisk index W of APPoeiAnd taking the APP within the preset normal range as the final designated APP. The first historical user refers to the historical user who has installed the designated APP within the preset time period and has determined the risk label through step S1. The preset time period may be set according to the sample amount, for example, the preset time period may be set to 6 months.
In practice, the APP installed in the terminal can be captured through the enterprise's own APP in the terminal, so that the user equipment for installing each APP is obtained, and then the user for installing the specified APP is obtained. The enterprise self APP is the APP developed by the enterprise self, the enterprise self APP can acquire the relevant data of the user through user authorization, and the enterprise self APP can also capture the APP installed in the terminal through the user authorization.
Illustratively, the designated APP2 and the designated APP3 … are selected as designated APPs whose risk indexes are stable. Taking the appointed APP1 as an example, all APPs installed in each terminal within 6 months can be captured through installed enterprise-owned APPs in the terminal, the terminal for installing the appointed APP1 is extracted, whether the terminal for installing the appointed APP1 is the terminal of the historical user with the risk tag determined or not is judged, and if yes, the user corresponding to the terminal is taken as the first historical user with the appointed APP 1. As shown in fig. 2, in the terminal 10 and the terminal 11, all APPs installed in each terminal within 6 months are captured by enterprise-owned APPs and stored in the database 20, the terminal where the designated APP1 is installed includes the terminal 10 and the terminal 11, and only the terminal 11 is a terminal of a history user for which a risk tag is determined, the user corresponding to the terminal 11 is taken as a first history user where the designated APP1 is installed; similarly, a first historical user may be obtained with the designated APP2 installed, the designated APP3 … designated APPN.
S3, determining the risk index of the appointed APP according to the risk label of the first historical user;
in the invention, the APP risk index is used for reflecting the probability of overdue risk of the user installing the APP. Wherein the risk index W of the ith designated APPoeiObtained by the following formula:
wherein i represents the ith designated APP, BadiThe number of historical users with risk labels of the ith appointed APP in the historical users, GoodiThe number of historical users with risk labels of the ith appointed APP installed is no risk, BadTThe number of users with risk labels of all users who install the ith appointed APP, GoodTThe number of users with risk labels of risk-free in all users for installing the ith appointed APP is pointed. Wherein, BadT/GoodTIs a prior probability obtained from past experience and analysis, such as a prior probability determined by a probability model according to the user information of the installation ith specified APP.
And S4, determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
In one embodiment, this step may be performedCapturing all appointed APPs installed by a current user through the own APPs of the enterprise, and determining the current user risk according to the risk indexes of all the appointed APPs installed by the current user and the weight of each appointed APP of the current user; namely:
；
n is the total number of the appointed APP installed by the current user; riRefers to the weight of the ith designated APP currently installed by the user.
Specifically, the weight of each designated APP of the current user may be determined according to the time and/or frequency of using the designated APP by the current user. In one example, the time that the user uses the designated APP every day in one month may be counted to obtain the average time that the user uses the designated APP every day in one month, and the weight of the designated APP is set according to the average time that the user uses the designated APP every day in one month, for example, the longer the average time that the user uses the designated APP every day is, the greater the weight of the designated APP is set.
In another example, weights of the usage time and the usage frequency may be set, and then the time and the frequency of the user using the specified APP every day in one month are counted to obtain an average time and an average frequency of the user using the specified APP every day in one month, and then a score value R of the user using the specified APP every day is obtained by means of weighted summation, that is:
wherein t is the average time of using the appointed APP by the user every day within one month; f is the average frequency of daily usage of the given APP by the user for a month, r1 is the weight of the time of usage, and r2 is the weight of the frequency of usage.
And finally, setting the weight of the user-specified APP according to the value R of the score of the specified APP.
In another specific embodiment, the risk indexes of all designated APPs installed by the historical user and whether the historical user has the risk label of overdue behavior or not can be collected, and the preset model is trained according to the risk indexes of all designated APPs installed by the historical user and the risk label of the historical user in a supervised learning mode. Therefore, through training of a large number of samples, when risk indexes of all appointed APPs installed by the current user are input into the preset model, the preset model can accurately estimate the risk generated by the user.
The preset model can adopt classification models such as XGBoost, GBDT and decision tree.
Fig. 3 is a schematic diagram of an architecture of a user risk prediction apparatus based on APP indexes, as shown in fig. 3, the apparatus includes:
the first determining module 31 is configured to determine a historical user risk tag according to behavior information of a historical user;
the obtaining module 32 is configured to obtain a first historical user who installs a specified APP within a preset time period;
a second determining module 33, configured to determine a risk index of the specified APP according to the risk label of the first historical user;
and a third determining module 34, configured to determine the risk of the current user according to the risk indexes of all the specified APPs installed by the current user.
The behavior information is overdue behavior, if the historical user has overdue behavior, the corresponding risk label is risky, and if the historical user has no overdue behavior, the corresponding risk label is risk-free.
The obtaining module 32 obtains the risk index W of the ith specified APP by the following formulaoei：
Wherein i represents the ith designated APP, BadiThe number of historical users with risk labels of the ith appointed APP in the historical users, GoodiThe number of historical users with risk labels of the ith appointed APP installed is no risk, BadTThe number of users with risk labels of all users who install the ith appointed APP, GoodTThe number of users with risk labels of risk-free in all users for installing the ith appointed APP is pointed.
The third determining module 34 determines the risk of the current user according to the risk indexes of all the designated APPs installed by the current user and the weight of each designated APP of the current user; or, the third determining module 34 inputs risk indexes of all designated APPs installed by the current user into a preset model, and determines the current user risk according to an output result of the preset model.
In the invention, the weight of each appointed APP of the current user is determined according to the time and/or frequency of the appointed APP used by the current user. The designated APP is based on the installation rate of the APP, the APP type and the risk index W of the APPoeiIs determined.
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.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as an implementation in physical form 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. 4 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. The electronic device shown in fig. 4 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. 4, the electronic device 400 of the exemplary embodiment is represented in the form of a general-purpose data processing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 connecting different electronic device components (including the memory unit 420 and the processing unit 410), a display unit 440, and the like.
The storage unit 420 stores a computer-readable program, which may be a code of a source program or a read-only program. The program may be executed by the processing unit 410 such that the processing unit 410 performs the steps of various embodiments of the present invention. For example, the processing unit 410 may perform the steps as shown in fig. 1.
The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM) 4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203. The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: operating the electronic device, 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 400 may also communicate with one or more external devices 300 (e.g., keyboard, display, network device, bluetooth device, etc.), enable a user to interact with the electronic device 400 via the external devices 300, and/or enable the electronic device 400 to communicate with one or more other data processing devices (e.g., router, modem, etc.). Such communication may occur via input/output (I/O) interfaces 450, and may also occur via a network adapter 460 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). The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID electronics, tape drives, and data backup storage electronics, among others.
FIG. 5 is a schematic diagram of one computer-readable medium embodiment of the present invention. As shown in fig. 5, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic device, apparatus, or device that is electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any 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 program, when executed by one or more data processing devices, enables the computer-readable medium to implement the above-described method of the invention, namely: determining a historical user risk label according to the behavior information of the historical user; acquiring a first history user for installing a specified APP in a preset time period; determining a risk index of the designated APP according to the risk label of the first historical user; and determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
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 data processing 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 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 electronic device, 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 of the present invention may be written in any combination of one or more programming languages, including object oriented programming languages such as Java, C + + or the like and conventional procedural programming languages, such as "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 present invention can be implemented as a method, an apparatus, an electronic device, or a computer-readable medium executing a computer program. Some or all of the functions of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP).
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 (9)
1. A user risk prediction method based on an APP index is characterized by comprising the following steps:
determining a historical user risk label according to the behavior information of the historical user;
acquiring a first history user for installing a specified APP in a preset time period;
determining a risk index of the designated APP according to the risk label of the first historical user; the first historical user refers to a user who installs a designated APP within a preset time period and determines a risk label;
and determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
2. The method of claim 1, wherein the behavior information is overdue behavior, and wherein the risk label is risky if the historical user has overdue behavior, and wherein the risk label is no risk if the historical user has no overdue behavior.
3. The method according to claim 2, characterized in that the risk index W of the ith given APPoeiObtained by the following formula:
wherein i represents the ith designated APP, BadiThe number of historical users with risk labels of the ith appointed APP in the historical users, GoodiRefers to a calendar with risk label as no risk in the historical users who install the ith appointed APPNumber of history users, BadTThe number of users with risk labels of all users who install the ith appointed APP, GoodTThe number of users with risk labels of risk-free in all users for installing the ith appointed APP is pointed.
4. The method according to claim 3, characterized in that the risk of the current user is determined according to the risk indexes of all the designated APPs installed by the current user and the weight of each designated APP of the current user;
or inputting the risk indexes of all appointed APPs installed by the current user into a preset model, and determining the current user risk according to the output result of the preset model.
5. Method according to claim 4, wherein the weight of each specific APP for the current user is determined according to the time and/or frequency of the specific APP used by the current user.
6. Method according to claim 1, characterized in that said given APP is determined according to the installation rate of the APP, the APP type and the risk index W of the APPoeiIs determined.
7. An apparatus for predicting risk of a user based on an APP index, the apparatus comprising:
the first determining module is used for determining the risk label of the historical user according to the behavior information of the historical user;
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a first historical user for installing a specified APP in a preset time period;
the first historical user refers to a user who installs a designated APP within a preset time period and determines a risk label;
a second determining module, configured to determine a risk index of the specified APP according to the risk label of the first historical user;
and the third determining module is used for determining the risk of the current user according to the risk indexes of all the appointed APPs installed by the current user.
8. An electronic device, comprising:
a processor; and
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-6.
9. 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 1-6.
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