CN111582649B - Risk assessment method and device based on user APP single-heat coding and electronic equipment - Google Patents
Risk assessment method and device based on user APP single-heat coding and electronic equipment Download PDFInfo
- Publication number
- CN111582649B CN111582649B CN202010275324.1A CN202010275324A CN111582649B CN 111582649 B CN111582649 B CN 111582649B CN 202010275324 A CN202010275324 A CN 202010275324A CN 111582649 B CN111582649 B CN 111582649B
- Authority
- CN
- China
- Prior art keywords
- app
- user
- coding
- heat
- machine learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a risk assessment method, a risk assessment device, electronic equipment and a computer readable medium based on user APP single-heat coding, wherein the risk assessment method comprises the following steps: acquiring APP installation list information of a mobile terminal associated with a user; performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user; establishing a machine learning model, and training the machine learning model by using the single-heat coding characteristics and the financial performance data of the historical user; and inputting the single-heat coding characteristics of the new user into a trained machine learning model, and calculating the risk score of the new user. According to the invention, the mobile terminal APP installation list information of the user to be trusted is converted into the unique heat coding feature, and the machine learning model is utilized for training, so that the trust risk of the user to be trusted is obtained, the operation is simple, the implementation is easy, the evaluated trust risk result is high in accuracy and good in stability, and in the aspects of modeling and feature maintenance, the input labor is less, the resources are saved, and the effect is obvious.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a risk assessment method, a risk assessment device, electronic equipment and a computer readable medium based on user APP (application) single-heat coding.
Background
In the case of a sufficient authorization of a customer, it is a common practice in the consumer financial field to obtain information about the Application (APP) of the customer terminal for risk control. For mining of client APP information, it is common practice to categorize the APPs and then to conduct client portrayal by counting the client's installation and usage preferences for a certain class of APPs. The disadvantage of this approach is that the classification of APP is very difficult to maintain, since many APPs themselves 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. The method is influenced by factors such as that APP classification accuracy cannot be guaranteed, so that accurate risk images of clients are difficult to carry out on the characteristics derived by the method, and maintenance is difficult. In the prior art, APP data are mined from other dimensions, such as APP classification is performed according to the number of active users in the APP day, and characteristics such as the number of APP hit mass APP installed by clients are mined; variable mining and the like can be performed according to risk attributes of the APP on the platform. But all have the problems of large digging difficulty, more manpower input, high maintenance cost, unsatisfactory stability and the like.
Disclosure of Invention
In order to solve the technical problem of how to stably, accurately and efficiently complete the trust risk assessment of a user, the invention provides a risk assessment method, a device, electronic equipment and a computer readable medium based on user APP single-heat coding.
An aspect of the present invention provides a risk assessment method based on user APP single-heat encoding, for assessing financial risk of a user, including:
acquiring APP installation list information of a mobile terminal associated with a user, wherein the user comprises a history user and a new user;
performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user;
establishing a machine learning model, and training the machine learning model by using the single-heat coding characteristics and the financial performance data of the historical user;
and inputting the single-heat coding characteristics of the new user into the trained machine learning model, and calculating the risk score of the new user.
According to a preferred embodiment of the present invention, the performing the one-time thermal encoding according to the APP installation list information to obtain one-time thermal encoding features associated with a user further includes:
acquiring user coverage of a plurality of APP;
establishing an APP set, so that the user coverage of each APP in the APP set is greater than a preset value;
comparing the user's APP installation list to the APP set to obtain the one-hot encoding feature associated with the user.
According to a preferred embodiment of the present invention, the establishing an APP set such that the user coverage of each APP in the APP set is greater than a predetermined value further comprises:
ordering the plurality of APP to form a sequence according to the order of the user coverage from big to small;
and selecting the APP ranked in the preset quantity in the sequence as the APP in the APP set.
According to a preferred embodiment of the present invention, the performing the one-time thermal encoding according to the APP installation list information to obtain one-time thermal encoding features associated with a user further includes:
and obtaining the APP installation quantity according to the APP installation list information, and carrying out the independent heat coding according to whether the APP installation quantity exceeds a preset value to obtain the independent heat coding characteristic associated with the user.
According to a preferred embodiment of the present invention, the performing the one-time thermal encoding according to the APP installation list information to obtain one-time thermal encoding features associated with a user further includes:
and obtaining the number of the APP installations of the specific type according to the APP installation list information, and performing the single-heat coding according to whether the number of the APP installations of the specific type exceeds a preset value to obtain the single-heat coding characteristic associated with the user.
According to a preferred embodiment of the invention, the specific type is a plurality, thereby creating a plurality of single-heat encoding features.
According to a preferred embodiment of the invention, the specific types include any one of the following: finance class, loan class, financing class, social class, gaming class, and work class.
According to a preferred embodiment of the invention, the machine learning model is an extreme gradient lifting model.
A second aspect of the present invention provides a risk assessment apparatus based on user APP single-heat encoding, comprising:
the information acquisition module is used for acquiring APP installation list information of the mobile terminal associated with the user, wherein the user comprises a history user and a new user;
the information coding module is used for performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user;
the model training module is used for establishing a machine learning model and training the machine learning model by using the unique heat coding characteristics of the historical user and the financial performance data thereof;
and the feature training module inputs the single-heat coding features of the new user into the trained machine learning model and calculates the risk score of the new user.
According to a preferred embodiment of the present invention, the information encoding module further includes:
the coverage acquisition unit is used for acquiring user coverage of a plurality of APP;
a set establishing unit, configured to establish an APP set such that the user coverage of each APP in the APP set is greater than a predetermined value;
and the comparison unit is used for comparing the APP installation list of the user with the APP set to obtain the single-hot coding feature associated with the user.
According to a preferred embodiment of the present invention, the set-up unit further comprises:
the ordering unit is used for ordering the plurality of APP to form a sequence according to the order of the user coverage from big to small;
and the screening unit is used for selecting the APP ranked in the preset quantity in the sequence as the APP in the APP set.
According to a preferred embodiment of the present invention, the information encoding module further includes:
the counting unit is used for obtaining the APP installation quantity according to the APP installation list information;
and the first characteristic acquisition unit is used for carrying out one-time thermal coding according to whether the APP installation number exceeds a preset value, and acquiring the one-time thermal coding characteristic associated with the user.
According to a preferred embodiment of the present invention, the information encoding module further includes:
the classification counting unit is used for obtaining the APP installation quantity of a specific type according to the APP installation list information;
and the second characteristic acquisition unit is used for carrying out the single-heat coding according to whether the number of the specific type of APP installation exceeds a preset value, and acquiring the single-heat coding characteristic associated with the user.
According to a preferred embodiment of the invention, the specific type is a plurality, thereby creating a plurality of single-heat encoding features.
According to a preferred embodiment of the invention, the specific types include any one of the following: finance class, loan class, financing class, social class, gaming class, and work class.
According to a preferred embodiment of the invention, the machine learning model is an extreme gradient lifting model.
A third aspect of the present invention provides an electronic apparatus, wherein the electronic apparatus includes: a processor; the method comprises the steps of,
a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of the claims.
A fourth aspect of the invention provides a computer readable storage medium storing one or more programs which when executed by a processor implement the method of any one of the claims.
The technical scheme of the invention has the following beneficial effects:
according to the invention, the mobile terminal APP installation list information of a sample user is collected, the mobile terminal APP installation list information is converted into the single-heat coding feature, a machine learning model is built, the single-heat coding feature of the sample user and the trust risk result are utilized to train the machine learning model, and finally the single-heat coding feature converted by the mobile terminal APP installation list information of the user to be trusted is substituted into the trained machine learning model to train, so that the trust risk of the user to be trusted is obtained.
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 schematic diagram of an application scenario of a risk assessment method based on user APP single-heat coding;
FIG. 2 is a schematic flow chart of a risk assessment method based on user APP single-heat coding;
FIG. 3 is a schematic diagram of a risk assessment device module architecture based on user APP single-heat encoding according to the present invention;
FIG. 4 is a schematic diagram of an electronic device architecture for risk assessment based on user APP single-heat encoding in accordance with the present invention;
fig. 5 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. However, the exemplary embodiments can 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 in the drawings denote the same or similar elements, components or portions, and thus a repetitive description thereof will be omitted.
The features, structures, characteristics or other details described in a particular embodiment do not exclude that may be combined in one or more other embodiments in a suitable manner, without departing from the technical idea of the invention.
In the description of specific embodiments, features, structures, characteristics, or other details described in the present invention are provided to enable one skilled in the art to fully understand the embodiments. However, it is not excluded that one skilled in the art may practice the present invention without one or more of the specific features, structures, characteristics, or other details.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they 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 order of actual execution may be changed according to actual situations.
The block diagrams depicted in the figures are merely 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.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, this should not be limited by these terms. These words are used 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.
The term "and/or" and/or "includes all combinations of any of the associated listed items and one or more.
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Fig. 1 is a schematic diagram of an application scenario of a risk assessment method based on user APP single-heat coding. As shown in fig. 1, a user accesses a financial institution server through a mobile terminal provided by a financial institution, submits a credit request to the financial institution, and after receiving the credit request, the financial institution server evaluates the credit risk of the user to be trusted by analyzing user data.
Fig. 2 is a schematic flow chart of a risk assessment method based on user APP single-hot coding. As shown in fig. 2, the method includes:
s101, acquiring APP installation list information of a mobile terminal associated with a user, wherein the user comprises a history user and a new user.
Specifically, the financial institution server first obtains a sufficient number of historical users of known trusted financial performance data, obtains APP information installed in their mobile terminals under the authorization of the users, and sends the APP information in a list form to the financial institution server as a training sample of the trusted risk model.
When a new user starts a client of a financial institution through a mobile terminal and sends a credit request, the client of the financial institution pops up a dialog box to request the user to authorize so as to acquire APP information installed in the mobile terminal of the user, and when the user authorizes, the client also sends the APP installed by the user to a server of the financial institution in a list form, so that the server of the financial institution can process the APP in the list later.
S102, performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user.
Specifically, the financial institution server firstly obtains the coverage of the current multiple types of APP through multiple ways, the APP coverage is the number of people with the APP users installed in a specific area range, the number of people is larger (wide) to indicate the coverage of the APP users, then all APP is ordered and sequenced according to the order from large to small of the user coverage according to the obtained APP coverage, the higher APP ranks are more front, the ranks can be divided into multiple forms, such as comprehensive ranks and ranks of each type, the existing APP has multiple types, such as finance type, loan type, financial type, social type, game type, work type and the like, the comprehensive coverage ranks of all APP before classification can be obtained first, and then the coverage ranks of APP under each type are obtained respectively.
After obtaining the coverage rank of the APP, the financial institution server may establish an APP set, set a threshold range for the set, and only the APP with a preset condition greater than the threshold range may be allocated to the set, where the APP with a coverage greater than the set threshold may be allocated to the set, or may be set such that only the APP with a rank greater than the set threshold may be allocated to the set.
Preferably, when the APP set is established, various forms of APP sets can be established according to different ranking forms, for example, the APP set can be established according to comprehensive coverage ranking of APPs, and the APP set can also be established according to a specific type of APP, so that the method is suitable for different types of users.
Finally, when a new user to be trusted exists, the financial institution server acquires APP list information installed by the mobile terminal of the user under the condition of user authorization, compares each APP in the APP set with all APPs in the APP list respectively, determines whether the user installs the APP in the APP set, and when a certain APP in the APP set is successfully matched with the APP in the APP list, indicates that the user installs the APP in the APP set, and converts the APP into a first unique heat coding feature; when matching of a certain APP in the APP set and an APP in the APP list fails, the user is not provided with the APP in the APP set, and the APP is converted into a second unique thermal coding feature.
For example, ranking all the APPs according to the coverage, selecting about 1000 APPs with top ranks to be divided into APP sets, then matching 1000 APPs in the APP sets with APP installation lists of users respectively, comparing 1000 times, if a user installs one APP in the APP sets, setting 1, otherwise setting 0, and finally setting the feature dimension of the user to be a group of 1000-dimensional features composed of 0 and 1 variables. It should be noted that the dimension of the one-hot encoding feature is the same as the number of APPs in the APP set.
Preferably, after the APP in the APP set is converted into the unique thermal coding feature according to the APP list installed by the user, in order to make the unique thermal coding feature between each user more accurate, the number of APPs installed may be obtained according to the APP installation list information, and the unique thermal coding feature associated with the user may be obtained according to whether the number of APPs installed exceeds a predetermined value. For example, setting the user-installed APP number threshold to 50, the one-hot code is 10 when the user-installed APP number exceeds 50, otherwise it is 01. And splicing the single thermal code with the single thermal code to form a new single thermal code characteristic.
Preferably, the number of the specific types of the APP installations can be obtained according to the APP installation list information, and the unique thermal coding feature associated with the user can be obtained according to whether the number of the specific types of the APP installations exceeds a preset value or not. For example, setting the user-installed financial type APP number threshold to 10, the one-hot code is 10 when the user-installed financial type APP number exceeds 10, otherwise it is 01. And splicing the single thermal code with the single thermal code to form a new single thermal code characteristic. The unique heat coding features corresponding to each APP type of the user can be obtained simultaneously according to the method, and the unique heat coding features can be spliced in the unique heat coding features corresponding to the comprehensive types selectively to form new unique heat coding features.
S103, establishing a machine learning model, and training the machine learning model by using the single-heat coding characteristics and the financial performance data of the historical user.
According to the APP installation list information of the historical users acquired in the step S101, the unique heat coding characteristics of the historical users are acquired according to the method in the step S102, meanwhile, a machine learning model is built, and the unique heat coding characteristics of the historical users and the financial performance data of the unique heat coding characteristics are used as training samples to be input into the machine learning model, so that the machine model can be trained, and a trained machine training model is obtained. For example, the financial performance data of the historical user is data of "whether overdue exceeds 7 days", i.e., the performance data may be "yes" or "no", or indicated by "1" and "0". Preferably, in the embodiment of the present invention, the machine learning model may be obtained by training an extreme gradient lifting model, and the embodiment of the present invention does not limit the type of the machine learning model.
S104, inputting the single-heat coding characteristics of the new user into the trained machine learning model, and calculating the risk score of the new user.
Specifically, firstly, the acquired APP installation list information of the new user acquires the unique thermal coding feature according to the method in step S102, and inputs the unique thermal coding feature into the trust risk model trained in step 103 to obtain the risk scoring result of the user to be trusted. The user can be reasonably trusted through the result.
For example, a machine learning model trained on historical user "overdue more than 7 days" as financial performance data may accept APP installation list one-time-hot encoded information of new users, thereby predicting the probability that their financial performance data will be "overdue more than 7 days" at the future, which probability is between 0 and 1, with a closer to 1 indicating that it is more likely that "overdue more than 7 days" will occur. Thus, the new user can be subjected to a risk operation such as trust or rejection according to a predetermined policy.
The method disclosed by the invention is simple to operate and easy to implement, and the estimated trust risk result is high in accuracy and good in stability, and has the advantages of less labor investment, resource saving and remarkable effect in modeling and feature maintenance.
Those skilled in the art will appreciate that all or part of the steps implementing the above-described embodiments are implemented as a program (computer program) executed by a computer data processing apparatus. The above-described method provided by the present invention can be implemented when the computer program is executed. Moreover, 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, for example, a magnetic disk or a tape storage array. The storage medium is not limited to a centralized storage, but may be a distributed storage, such as cloud storage based on cloud computing.
The following describes apparatus embodiments of the invention that may be used to perform method embodiments of the invention. Details described in the embodiments of the device according to the invention should be regarded as additions to the embodiments of the method described above; for details not disclosed in the embodiments of the device according to the invention, reference may be made to the above-described method embodiments.
Fig. 3 is a schematic diagram of a risk assessment device module architecture based on user APP single-hot encoding according to the present invention. As shown in fig. 3, the apparatus 200 includes:
an information obtaining module 201, configured to obtain APP installation list information of a mobile terminal associated with a user, where the user includes a history user and a new user.
And the information coding module 202 is configured to perform one-time thermal coding according to the APP installation list information, and obtain one-time thermal coding features associated with a user.
The model training module 203 is configured to establish a machine learning model, and train the machine learning model by using the unique thermal coding features of the historical user and the financial performance data thereof; in the embodiment of the invention, the trust risk model can be obtained by training an extreme gradient lifting model, and the embodiment of the invention does not limit the types of the extreme gradient lifting model.
Feature training module 204 inputs the unique heat-coded features of the new user into the trained machine learning model and calculates a risk score for the new user.
Specifically, a user accesses a financial institution server through a mobile terminal provided by a financial institution to provide a credit request for the financial institution, after the financial institution server receives a lending request, the financial institution server evaluates the credit risk of the user to be trusted by analyzing user data.
The information encoding module 202 further includes:
the coverage acquisition unit is used for acquiring user coverage of a plurality of APP;
a set establishing unit, configured to establish an APP set such that the user coverage of each APP in the APP set is greater than a predetermined value;
and the comparison unit is used for comparing the APP installation list of the user with the APP set to obtain the single-hot coding feature associated with the user.
The set-up unit further includes:
the ordering unit is used for ordering the plurality of APP to form a sequence according to the order of the user coverage from big to small;
and the screening unit is used for selecting the APP ranked in the preset quantity in the sequence as the APP in the APP set.
The information encoding module 202 further includes:
the counting unit is used for obtaining the APP installation quantity according to the APP installation list information;
and the first characteristic acquisition unit is used for carrying out one-time thermal coding according to whether the APP installation number exceeds a preset value, and acquiring the one-time thermal coding characteristic associated with the user.
The information encoding module 202 further includes:
the classification counting unit is used for obtaining the APP installation quantity of a specific type according to the APP installation list information;
and the second characteristic acquisition unit is used for carrying out the single-heat coding according to whether the number of the specific type of APP installation exceeds a preset value, and acquiring the single-heat coding characteristic associated with the user. The particular type is a plurality, such as a finance class, loan class, financial class, social class, gaming class, work class, etc., thereby producing a plurality of unique thermally coded features.
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 according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to 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 schematic diagram of an electronic device structure framework based on risk assessment of user APP single-heat encoding according to the present invention. An electronic device 600 according to this embodiment of the present invention is described below with reference to fig. 4. The electronic device 600 shown in fig. 4 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, the electronic device 600 is embodied in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the electronic prescription stream processing method section above in this specification. For example, the processing unit 610 may perform the steps shown in fig. 2.
The memory unit 620 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage 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 or some combination of which may include an implementation of a network environment.
Bus 630 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 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with 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, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
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, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned method according to the present invention. The computer program, when executed by a data processing device, enables the computer readable medium to carry out the above-described method of the present invention, namely: acquiring APP installation list information of a mobile terminal associated with a user, wherein the user comprises a history user and a new user; performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user; establishing a machine learning model, and training the machine learning model by using the single-heat coding characteristics and the financial performance data of the historical user; and inputting the single-heat coding characteristics of the new user into the trained machine learning model, and calculating the risk score of the new user.
The computer program may be stored on one or more computer readable media. Fig. 5 is a schematic diagram of a computer readable storage medium of the present invention, as shown in fig. 5. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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 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 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 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 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 accordance with embodiments 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 present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
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 (14)
1. A risk assessment method based on user APP single-heat coding for assessing financial risk of a user, comprising:
acquiring APP installation list information of a mobile terminal associated with a user, wherein the user comprises a history user and a new user;
acquiring user coverage of a plurality of APP; the user coverage of the APP is the number of people with the APP users installed in a specific area;
ordering the plurality of APP to form a sequence according to the order of the user coverage of the APP from big to small;
selecting the APP ranked in the preset quantity in the sequence as the APP in the APP set;
and performing single-heat encoding according to the APP installation list information to obtain single-heat encoding characteristics associated with a user, wherein the single-heat encoding characteristics comprise: comparing the APP installation list of the user with the APP set, and converting the APP into a first unique thermal coding feature when a certain APP in the APP set is successfully matched with the APP in the APP installation list; when matching of a certain APP in the APP set and the APP in the APP installation list fails, converting the APP into a second unique thermal coding feature; the dimension of the single-heat coding feature is the same as the number of the APP in the APP set;
establishing a machine learning model, and training the machine learning model by using the single-heat coding characteristics and the financial performance data of the historical user;
and inputting the single-heat coding characteristics of the new user into a trained machine learning model, and calculating the risk score of the new user.
2. The method of claim 1, wherein the one-time encoding based on the APP install list information to obtain one-time encoded features associated with a user, further comprising:
and obtaining the APP installation quantity according to the APP installation list information, and performing independent heat coding according to whether the APP installation quantity exceeds a preset value, and splicing the independent heat coding with the independent heat coding characteristics to form new independent heat coding characteristics.
3. The method of claim 1, wherein the one-time encoding based on the APP install list information to obtain one-time encoded features associated with a user, further comprising:
and obtaining the APP installation quantity of the specific type according to the APP installation list information, performing independent heat coding according to whether the APP installation quantity of the specific type exceeds a preset value, and splicing the independent heat coding with the independent heat coding characteristic to form a new independent heat coding characteristic.
4. A method according to claim 3, wherein the specific type is a plurality, thereby producing a plurality of single thermal encoding features.
5. A method according to claim 3, wherein the specific type comprises any one of the following: finance class, loan class, financing class, social class, gaming class, and work class.
6. The method of claim 1, wherein the machine learning model is an extreme gradient lifting model.
7. A risk assessment device based on user APP single-heat coding, characterized by comprising:
the information acquisition module is used for acquiring APP installation list information of the mobile terminal associated with the user, wherein the user comprises a history user and a new user;
the information coding module is used for performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user;
the model training module is used for establishing a machine learning model and training the machine learning model by using the unique heat coding characteristics of the historical user and the financial performance data thereof;
the feature training module inputs the single-heat coding features of the new user into a trained machine learning model, and calculates a risk score of the new user;
wherein: the information encoding module further includes:
the coverage acquisition unit is used for acquiring user coverage of a plurality of APP; the user coverage of the APP is the number of people with the APP users installed in a specific area;
the ordering unit is used for ordering the plurality of the APP to form a sequence according to the order of the user coverage of the APP from big to small;
the screening unit is used for selecting the APP ranked in the preset quantity in the sequence as the APP in the APP set;
the comparison unit is used for performing single-heat coding according to the APP installation list information to obtain single-heat coding characteristics associated with a user, and comprises the following steps: comparing the APP installation list of the user with the APP set, and converting the APP into a first unique thermal coding feature when a certain APP in the APP set is successfully matched with the APP in the APP installation list; when matching of a certain APP in the APP set and the APP in the APP installation list fails, converting the APP into a second unique thermal coding feature; the dimension of the one-time-heat-encoding feature is the same as the number of APPs in the APP set.
8. The apparatus of claim 7, wherein the information encoding module further comprises:
the counting unit is used for obtaining the APP installation quantity according to the APP installation list information;
and the first characteristic acquisition unit is used for performing independent heat coding according to whether the APP installation number exceeds a preset value, and splicing the independent heat coding with the independent heat coding characteristic to form a new independent heat coding characteristic.
9. The apparatus of claim 7, wherein the information encoding module further comprises:
the classification counting unit is used for obtaining the APP installation quantity of a specific type according to the APP installation list information;
and the second characteristic acquisition unit is used for performing one-time thermal coding according to whether the installation quantity of the APP of the specific type exceeds a preset value, and splicing the one-time thermal coding with the one-time thermal coding characteristic to form a new one-time thermal coding characteristic.
10. The apparatus of claim 9, wherein the particular type is a plurality, thereby producing a plurality of single-heat encoded features.
11. The apparatus of claim 9, wherein the particular type comprises any one of: finance class, loan class, financing class, social class, gaming class, and work class.
12. The apparatus of claim 7, wherein the machine learning model is an extreme gradient lifting model.
13. An electronic device, wherein the electronic device comprises:
a processor; the method comprises the steps of,
a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1-6.
14. 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010275324.1A CN111582649B (en) | 2020-04-09 | 2020-04-09 | Risk assessment method and device based on user APP single-heat coding and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010275324.1A CN111582649B (en) | 2020-04-09 | 2020-04-09 | Risk assessment method and device based on user APP single-heat coding and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111582649A CN111582649A (en) | 2020-08-25 |
| CN111582649B true CN111582649B (en) | 2023-09-29 |
Family
ID=72117044
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010275324.1A Active CN111582649B (en) | 2020-04-09 | 2020-04-09 | Risk assessment method and device based on user APP single-heat coding and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111582649B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112465657A (en) * | 2021-02-02 | 2021-03-09 | 北京淇瑀信息科技有限公司 | Risk assessment method and device based on tree model feature derivation and electronic equipment |
| CN113591065A (en) * | 2021-07-21 | 2021-11-02 | 上海淇玥信息技术有限公司 | User equipment authentication method, device and equipment based on APP installation list grouping |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126597A (en) * | 2016-06-20 | 2016-11-16 | 乐视控股（北京）有限公司 | User property Forecasting Methodology and device |
| CN108470027A (en) * | 2018-03-30 | 2018-08-31 | 广州优视网络科技有限公司 | Pornographic application and identification method and device, computing device and storage medium |
| CN109146671A (en) * | 2018-08-28 | 2019-01-04 | 卫盈联信息技术（深圳）有限公司 | Air control method, apparatus and computer readable storage medium |
| CN109194635A (en) * | 2018-08-22 | 2019-01-11 | 杭州安恒信息技术股份有限公司 | Malice URL recognition methods and device based on natural language processing and deep learning |
| CN110349000A (en) * | 2019-06-29 | 2019-10-18 | 上海淇毓信息科技有限公司 | Method, apparatus and electronic equipment are determined based on the volume strategy that mentions of tenant group |
| CN110796542A (en) * | 2019-09-26 | 2020-02-14 | 北京淇瑀信息科技有限公司 | Financial risk control method, financial risk control device and electronic equipment |
-
2020
- 2020-04-09 CN CN202010275324.1A patent/CN111582649B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126597A (en) * | 2016-06-20 | 2016-11-16 | 乐视控股（北京）有限公司 | User property Forecasting Methodology and device |
| CN108470027A (en) * | 2018-03-30 | 2018-08-31 | 广州优视网络科技有限公司 | Pornographic application and identification method and device, computing device and storage medium |
| CN109194635A (en) * | 2018-08-22 | 2019-01-11 | 杭州安恒信息技术股份有限公司 | Malice URL recognition methods and device based on natural language processing and deep learning |
| CN109146671A (en) * | 2018-08-28 | 2019-01-04 | 卫盈联信息技术（深圳）有限公司 | Air control method, apparatus and computer readable storage medium |
| CN110349000A (en) * | 2019-06-29 | 2019-10-18 | 上海淇毓信息科技有限公司 | Method, apparatus and electronic equipment are determined based on the volume strategy that mentions of tenant group |
| CN110796542A (en) * | 2019-09-26 | 2020-02-14 | 北京淇瑀信息科技有限公司 | Financial risk control method, financial risk control device and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111582649A (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111210335B (en) | User risk identification method and device and electronic equipment | |
| CN111582645B (en) | APP risk assessment method and device based on factoring machine and electronic equipment | |
| CN112508580A (en) | Model construction method and device based on rejection inference method and electronic equipment | |
| CN111181757B (en) | Information security risk prediction method and device, computing equipment and storage medium | |
| CN116541610B (en) | Training method and device for recommendation model | |
| CN111582649B (en) | Risk assessment method and device based on user APP single-heat coding and electronic equipment | |
| CN111383100A (en) | Risk model-based full life cycle management and control method and device | |
| CN113326991A (en) | Automatic authorization method, device, computer equipment and storage medium | |
| CN112017042A (en) | Resource quota determining method and device based on tweed distribution and electronic equipment | |
| CN111178687A (en) | Financial risk classification method and device and electronic equipment | |
| CN111199469A (en) | User payment model generation method and device and electronic equipment | |
| CN111191677B (en) | User characteristic data generation method and device and electronic equipment | |
| CN111210332A (en) | Method and device for generating post-loan management strategy and electronic equipment | |
| CN113610625A (en) | Overdue risk warning method and device and electronic equipment | |
| CN110704614B (en) | Information processing method and device for predicting user group type in application | |
| CN112508690A (en) | Risk assessment method and device based on joint distribution adaptation and electronic equipment | |
| CN110363583B (en) | Method and device for creating user consumption behavior label based on position information and electronic equipment | |
| CN109951859B (en) | Wireless network connection recommendation method and device, electronic equipment and readable medium | |
| US11222143B2 (en) | Certified information verification services | |
| CN116684330A (en) | Traffic prediction method, device, equipment and storage medium based on artificial intelligence | |
| CN111210109A (en) | Method and device for predicting user risk based on associated user and electronic equipment | |
| CN114897607A (en) | Data processing method and device for product resources, electronic equipment and storage medium | |
| CN114897099A (en) | User classification method and device based on passenger group deviation smooth optimization and electronic equipment | |
| CN110363394B (en) | Wind control service method and device based on cloud platform and electronic equipment | |
| CN113516398A (en) | Risk equipment identification method and device based on hierarchical sampling and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |