CN112784219B - APP index-based user risk prediction method and apparatus, and electronic device - Google Patents
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Abstract
The invention discloses a user risk prediction method and device based on an APP index and electronic equipment, wherein the method comprises the following steps: determining a historical user risk tag according to the behavior information of the historical user; acquiring a first historical user for installing a designated 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 APP installed by the current user. According to the method, a great amount of time and effort are not required to be invested for mining and maintaining APP data characteristics, the APP risk index is determined by only collecting the historical user risk labels of the APP, and the user risk can be predicted according to the APP risk index installed by the 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 and functional application programs (APP) have been developed. Under the condition that a client is fully authorized, acquiring information related to the client terminal APP for risk control is a common practice in the financial field.
Existing APP risk models typically use logistic regression or some integrated algorithm, such as GBDT, xgboost, etc. Such algorithms typically require a certain mining of features in advance to construct variables with a certain degree of discrimination, which are then fed into the model for training. The construction of such models requires a great deal of feature mining work, for example, for mining of client APP information, classification of the APP is required first, and then client portrayal is performed by counting the installation and use preference of clients for a certain class of APP. In practice, APP classification is very difficult to maintain, because many APPs have multiple functions, and it is difficult to classify them into a certain class. For example, an APP has social properties and functions of financial accounting, payment and the like. This results in a large investment in digging features and a cumbersome feature maintenance.
Disclosure of Invention
The invention aims to solve the technical problem that the conventional APP wind control method requires a great deal of time and effort to excavate and maintain APP data characteristics, so that the investment cost is high.
In order to solve the above technical problems, a first aspect of the present invention provides a user risk prediction method based on an APP index, the method comprising:
determining a historical user risk tag according to the behavior information of the historical user;
acquiring a first historical user for installing a designated 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 APP 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 tag is risky, and if the historical user has no overdue behavior, the corresponding risk tag is risky.
According to a preferred embodiment of the invention, the risk index W of the ith designated APP oei Obtained by the following formula:
；
wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Calendar indicating risk-free labels in history user for installing ith appointed APPNumber of users, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated.
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 risk indexes of all the appointed APP installed by the current user into a preset model, and determining the risk of the current user according to the output result of the preset model.
According to a preferred embodiment of the present invention, the weight of each of the designated APP's of the current user is determined according to the time and/or frequency of use of the designated APP by the current user.
According to a preferred embodiment of the present invention, the specified APP is based on the installation rate of APP, APP type and risk index W of APP oei Is determined by the range of (a).
To solve the above technical problem, a second aspect of the present invention provides a user risk prediction apparatus based on an APP index, the apparatus including:
the first determining module is used for determining a historical user risk tag according to the behavior information of the historical user;
the acquisition module is used for acquiring a first historical user of the installation appointed APP in a preset time period;
the second determining module is used for determining the risk index of the appointed 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 APP 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 tag is risky, and if the historical user has no overdue behavior, the corresponding risk tag is risky.
According to a preferred embodiment of the invention, the acquisition module is obtained by the following formulaObtaining the risk index W of the ith appointed APP oei ：
；
Wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Meaning the number of history users with risk labels as risk-free in the history users with the ith appointed APP installed, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated.
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 specified APPs installed by the current user and the weight of each specified APP of the current user;
or the third determining module inputs risk indexes of all the appointed APP installed by the current user into a preset model, and determines the risk of the current user according to the output result of the preset model.
According to a preferred embodiment of the present invention, the weight of each of the designated APP's of the current user is determined according to the time and/or frequency of use of the designated APP by the current user.
According to a preferred embodiment of the present invention, the specified APP is based on the installation rate of APP, APP type and risk index W of APP oei Is determined by the range of (a).
To solve the above technical problem, a third aspect of the present invention provides an electronic device, including:
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 problem, a fourth aspect of the present invention provides a computer-readable storage medium storing one or more programs, which when executed by a processor, implement the above method.
The present invention contemplates that risk users (e.g., overdue users, rogue users, etc.) may prefer to install certain APP's for some purpose. For example, fraudulent users may install some chat, social, credit-like APPs to achieve the goal of fraud money. Therefore, 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 great amount of time and effort are not required to be invested for mining and maintaining APP data characteristics, the APP risk index is determined by only collecting the historical user risk labels of the APP, and the user risk can be predicted according to the APP risk index installed by the 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 achieved more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted, however, that the drawings described below are merely illustrative of exemplary embodiments of the present invention and that other embodiments of the drawings may be derived from these drawings by those skilled in the art without undue effort.
FIG. 1 is a flow chart of a user risk prediction method based on APP index according to the present invention;
FIG. 2 is a schematic diagram of the present invention acquiring a first historical user installing a designated APP for a preset period of time;
FIG. 3 is a schematic diagram of a structural framework of an APP index-based user risk prediction apparatus according to the present invention;
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 are shown, although the exemplary embodiments may be practiced in various specific ways. 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, capabilities, effects, or other features described in a particular embodiment may be incorporated in one or more other embodiments in any suitable manner without departing from the spirit of the present invention.
In describing particular embodiments, specific details of construction, performance, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by those skilled in the art. It is not excluded, however, that one skilled in the art may implement the present invention in a particular situation in a solution that does not include the structures, properties, effects, or other characteristics described above.
The flow diagrams in the figures are merely exemplary flow illustrations and do not represent that all of the elements, operations, and steps in the flow diagrams must be included in the aspects of the present invention, nor that the steps must be performed in the order shown in the figures. For example, some operations/steps in the flowcharts may be decomposed, some operations/steps may be combined or partially combined, etc., and the order of execution 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. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus repeated descriptions of the same or similar elements, components or portions may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, these devices, elements, components or portions should not be limited by these terms. That is, these phrases are merely intended to distinguish one from the other. For example, a first device may also be referred to as a second device without departing from the spirit of the invention. Furthermore, the term "and/or," "and/or" is meant to include all combinations of any one or more of the items listed.
The present invention is based on the fact that risk users (e.g., overdue users, rogue users, etc.) may prefer to install certain APP's for some purpose. For example, fraudulent users can install some chat, social and credit-like APP to achieve the purpose of fraud money; the overdue user will install some credit-class APP to add to the borrowing amount. Thus, each APP will have a different risk index for different risks (e.g. fraud risk, overdue risk). 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 APP installed by the current user. According to the method, a great amount of time and effort are not required to be invested for mining and maintaining APP data characteristics, the APP risk index is determined by only collecting the historical user risk labels of the APP, and the user risk can be predicted according to the APP risk index installed by the 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 an APP index according to the present invention. As shown in fig. 1, the method includes:
s1, determining a historical user risk tag according to behavior information of a historical user;
the behavior information is used for labeling user risk labels. Thus, the behavior information may be different according to the risk type. For example, the behavioral information for calibrating fraud risk may be: whether there is fraud. The method and the device are mainly used for marking the overdue risk of the user, so that overdue behaviors are used as behavior information of the user. If the historical user has overdue behaviors, the corresponding risk label is risk, and can be marked by 1; if the historical user does not have overdue behavior, the corresponding risk tag is risk-free, and can be marked by 0.
S2, acquiring a first historical user of the installation designated APP in a preset time period;
wherein, the designated APP is some APPs capable of stably representing the risk of the user installing the APP. Can be based on the installation rate of APP, APP type and risk index W of APP oei Is determined by the range of (a). For example, in this step, the installation rate of APP may be selected to be greater than 75%, and at the same time, the APP is of the type: one of loan, health, chat as a designated APP. Then the risk index W of each appointed APP is obtained through the step S3 oei Risk index of APP W oei APP within a preset normal range is taken as the final designated APP. The first historical user refers to a historical user who installs a designated APP in a preset time period and determines a risk tag through step S1. The preset time period may be set according to the sample size, for example, the preset time period may be set to 6 months.
In practice, the APP installed in the terminal can be grasped by the enterprise in the terminal from the APP, so that user equipment for installing each APP is obtained, and further, a user for installing the designated APP is obtained. The enterprise own APP can acquire relevant data of a user through user authorization, and the enterprise own APP can also acquire the APP installed in the terminal through user authorization.
Illustratively, the designated APP2 and the designated APP3 … designate APPNs as the designated APPs with stable risk index performance. Taking the designated APP1 as an example, all APPs installed in each terminal can be captured by the enterprise own APPs installed in the terminal, the terminal for installing the designated APP1 is extracted, whether the terminal for installing the designated APP1 is the terminal of the history user for determining the risk tag is judged, if yes, the user corresponding to the terminal is taken as the first history user for installing the designated APP 1. As shown in fig. 2, in the terminals 10 and 11, capturing all APPs installed by each terminal in 6 months by the enterprise own APPs and storing the APPs in the database 20, wherein the terminal for installing the designated APP1 includes the terminal 10 and the terminal 11, and only the terminal 11 is the terminal of the history user for determining the risk tag, and then the user corresponding to the terminal 11 is taken as the first history user for installing the designated APP 1; similarly, a first history user with a designated APP2, designated APP3 …, and designated APPN installed may be obtained.
S3, determining a 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 a user who installs the APP. Wherein the i-th designated APP risk index W oei Obtained by the following formula:
；
wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Meaning the number of history users with risk labels as risk-free in the history users with the ith appointed APP installed, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated. Wherein Bad is T /Good T The prior probability is obtained according to past experience and analysis, such as the prior probability determined by a probability model according to the user information of the i-th designated APP.
S4, determining the risk of the current user according to risk indexes of all the appointed APP installed by the current user.
In a specific embodiment, the step can grasp all appointed APP installed by the current user through the enterprise own APP, and then determine the risk of the current user according to the risk index of all appointed APP installed by the current user and the weight of each appointed APP of the current user; namely:；
n is the total number of the APP specified by the current user installation; r is R i Refers to the weight of the i-th designated APP installed by the current user.
Specifically, the weight of each designated APP of the current user may be determined according to the time and/or frequency at which the designated APP is used by the current user. In one example, the time of using the designated APP per day by the user in one month may be counted, to obtain the average time of using the designated APP per day by the user in one month, and then the weight of the designated APP is set according to the average time of using the designated APP per day by the user in one month, for example, the longer the average time of using the designated APP per day, the greater the weight of the designated APP is set.
In another example, the weights of the usage time and the usage frequency can be set, then the time and the frequency of the usage of the designated APP by the user every day in one month are counted to obtain the average time and the average frequency of the usage of the designated APP by the user every day in one month, and then the scoring value R of the usage of the designated APP by the user every day is obtained by a weighted summation method, namely:
；
wherein t is the average time of the user using the designated APP every day in one month; f is the average frequency of the application specified by the user every day in one month, r1 is the weight of the use time, and r2 is the weight of the use frequency.
Finally, the weight of the user-specified APP is set according to the magnitude of the scoring value R of the specified APP.
In another embodiment, the risk indexes of all the specified APPs installed by the historical user and the risk labels of whether the historical user has overdue behaviors can be collected, and the preset model is trained according to the risk indexes of all the specified APPs installed by the historical user and the risk labels of the historical user in a supervised learning mode. Therefore, through training of a large number of samples, when risk indexes of all the appointed APP installed by the current user are input into the preset model, the preset model can accurately estimate the risk which will be generated by the user.
The preset model can adopt classification models such as XGboost, GBDT, decision tree and the like.
Fig. 3 is a schematic architecture diagram of an APP index-based user risk prediction device according to the present invention, as shown in fig. 3, the device includes:
a first determining module 31, configured to determine a historical user risk tag according to behavior information of a historical user;
an obtaining module 32, configured to obtain a first historical user who installs a specified APP in a preset period of time;
a second determining module 33, configured to determine a risk index of the designated APP according to risk tags of the first historical user;
a third determining module 34 is configured to determine the risk of the current user according to risk indexes of all designated 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 acquisition module 32 obtains the risk index W of the ith designated APP by the following formula oei ：
；
Wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Meaning the number of history users with risk labels as risk-free in the history users with the ith appointed APP installed, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated.
The third determining module 34 determines the risk of the current user according to the risk indexes of all the specified APPs installed by the current user and the weight of each specified APP of the current user; alternatively, the third determining module 34 inputs risk indexes of all the specified APPs installed by the current user into a preset model, and determines the risk of the current user according to the 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 current user using the appointed APP. The appointed APP is according to the installation rate of the APP, the type of the APP and the risk index W of the APP oei Is determined by the range of (a).
It will be appreciated by those skilled in the art that the modules in the embodiments of the apparatus described above may be distributed in an apparatus as described, or may be distributed in one or more apparatuses different from the embodiments described above with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
The following describes an embodiment of an electronic device of the present invention, which may be regarded as a physical form of implementation for the above-described embodiment of the method and apparatus of the present invention. Details described in relation to the embodiments of the electronic device of the present invention should be considered as additions to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described method or apparatus embodiments.
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 be construed as limiting the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 4, the electronic device 400 of the exemplary embodiment is 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 the different electronic device components (including memory unit 420 and processing unit 410), a display unit 440, and the like.
The storage unit 420 stores a computer readable program, which may be a source program or code of 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 shown in fig. 1.
The memory unit 420 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (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: an operating electronic device, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 430 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 400 may also communicate with one or more external devices 300 (e.g., keyboard, display, network device, bluetooth device, etc.), such that a user can interact with the electronic device 400 via the external devices 300, and/or such that the electronic device 400 can communicate with one or more other data processing devices (e.g., routers, modems, etc.). Such communication may occur through an input/output (I/O) interface 450, and may also occur through a network adapter 460 to one or more networks, such as 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 electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID electronics, tape drives, data backup storage electronics, and the like.
FIG. 5 is a schematic diagram of one embodiment of a computer readable medium 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 can be, for example, but not limited to, an electronic device, apparatus, or means of 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 would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk 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 carry out the above-described method of the present invention, namely: determining a historical user risk tag according to the behavior information of the historical user; acquiring a first historical user for installing a designated 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 APP installed by the current user.
From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, comprising several instructions to cause a data processing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the present invention.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 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, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, 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., connected via the Internet using an Internet service provider).
In summary, the present invention may be implemented in a method, apparatus, electronic device, or computer readable medium that executes 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 Digital Signal Processor (DSP).
The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. A user risk prediction method based on an APP index, the method comprising:
determining a historical user risk tag according to the behavior information of the historical user;
acquiring a first historical user for installing a designated 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 in a preset time period and determines a risk tag;
determining the risk of the current user according to risk indexes of all appointed APP installed by the current user;
the behavior information is overdue behavior, if the historical user has overdue behavior, the corresponding risk label is risk, and if the historical user does not have overdue behavior, the corresponding risk label is risk-free;
risk index W of ith designated APP oei Obtained by the following formula:
；
wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Meaning the number of history users with risk labels as risk-free in the history users with the ith appointed APP installed, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated.
2. The method of claim 1, wherein the current user risk is determined based on risk indices of all specified APPs installed by the current user and weights of each specified APP of the current user;
or inputting risk indexes of all the appointed APP installed by the current user into a preset model, and determining the risk of the current user according to the output result of the preset model.
3. The method of claim 2, wherein the weight of each of the designated APPs for the current user is determined based on the time and/or frequency at which the designated APPs are used by the current user.
4. The method of claim 1, wherein the specifying the APP is based on an APP installation rate, APP type, and APP risk index W oei Is determined by the range of (a).
5. An APP index-based user risk prediction apparatus, the apparatus comprising:
the first determining module is used for determining a historical user risk tag according to the behavior information of the historical user; the behavior information is overdue behavior;
the acquisition module is used for acquiring a first historical user of the installation appointed APP in a preset time period; the first historical user refers to a user who installs a designated APP in a preset time period and determines a risk tag;
the second determining module is used for determining the risk index of the appointed APP according to the risk label of the first historical user;
the third determining module is used for determining the risk of the current user according to the risk indexes of all the appointed APP installed by the current user;
the first determining module is specifically configured to determine that the corresponding risk tag is risk if the historical user has overdue behavior, and determine that the corresponding risk tag is risk-free if the historical user does not have overdue behavior;
the second determining module is specifically configured to obtain the risk index W of the ith designated APP by the following formula oei ：
；
Wherein i represents the i-th designated APP, bad i Refers to the number of history users with risk labels being at risk in the history users provided with the ith appointed APP, good i Finger mounting ith designationRisk label is the number of history users without risk in APP history users, bad T Refers to the number of users with risk labels being at risk in all users provided with ith appointed APP, good T And the number of users with risk labels being risk-free in all users provided with the ith appointed APP is indicated.
6. 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 1-4.
7. A computer readable storage medium storing one or more programs, which when executed by a processor, implement the method of any of claims 1-4.
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