CN111582645B - APP risk assessment method and device based on factoring machine and electronic equipment - Google Patents

APP risk assessment method and device based on factoring machine and electronic equipment Download PDF

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Publication number
CN111582645B
CN111582645B CN202010274615.9A CN202010274615A CN111582645B CN 111582645 B CN111582645 B CN 111582645B CN 202010274615 A CN202010274615 A CN 202010274615A CN 111582645 B CN111582645 B CN 111582645B
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app
user
features
feature
installation list
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CN111582645A (en
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聂婷婷
张蓉
姚王照
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Shanghai Qiyu Information Technology Co ltd
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Shanghai Qiyu Information Technology Co ltd
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    • 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

Abstract

The invention discloses an APP risk assessment method and device based on a factoring machine and electronic equipment, wherein the method comprises the following steps: acquiring APP installation list information of a mobile terminal associated with a user; extracting APP features and APP combination features according to the APP installation list information; performing single-heat coding on the APP features and the APP combination features to obtain single-heat coding features associated with users; and inputting the single-heat coding features into a trained machine learning model, and calculating the risk score of the user. According to the invention, the unique heat coding features converted from the user mobile terminal APP installation list information are substituted into the preset machine learning model for training, so that the trust risk of the user is obtained, the operation is simple, the implementation is easy, the accuracy of the evaluated trust risk result is high, meanwhile, the contribution degree to the trust risk when a plurality of feature combinations exist can be evaluated under the condition of a sparse data set, the stability is good, the investment labor is less in the aspects of modeling and feature maintenance, the resource is saved, and the effect is obvious.

Description

APP risk assessment method and device based on factoring machine and electronic equipment
Technical Field
The invention relates to the field of computer information processing, in particular to an APP risk assessment method and device based on a factorization machine, electronic equipment and a computer readable medium.
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. Because APP data is important, consumer finance companies in the industry will build APP risk models before or during the loan. 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 model training. The models can learn the potential rules of the features and are used for risk control, but the models answer the problems that when APP a and b exist simultaneously or the APP a and the b exist simultaneously, the contribution of combined features < a, b > to the risk Y is large, and the like, and meanwhile, a large amount of feature mining work is needed for constructing the models, so that the investment of feature mining is large and the maintenance of the features is troublesome.
Disclosure of Invention
In order to solve the technical problem that financial risks cannot be accurately predicted when data are fewer in the prior art, the invention provides an APP risk assessment method, an APP risk assessment device, electronic equipment and a computer readable medium based on a factoring machine.
An aspect of the present invention provides an APP risk assessment method based on a factoring machine, for assessing financial risk of a user, including:
acquiring APP installation list information of a mobile terminal associated with a user;
extracting APP features and APP combination features according to the APP installation list information;
performing single-heat coding on the APP characteristics and the APP combined characteristics to obtain single-heat coding characteristics associated with a user;
and inputting the single-heat coding features into a trained machine learning model, and calculating the risk score of the user.
According to a preferred embodiment of the invention, the machine learning model is a factorization machine model.
According to a preferred embodiment of the present invention, the extracting APP feature and APP combination feature according to the APP installation list information further includes:
setting corresponding features for each APP in the APP installation list information;
and combining the characteristics corresponding to any two APP in the APP installation list information to obtain the combined characteristics.
According to a preferred embodiment of the present invention, the setting of the corresponding feature for each APP in the APP installation list information further includes:
classifying each APP in the APP installation list information;
and setting the same type of APP in the APP installation list information as the same feature.
According to a preferred embodiment of the invention, said type comprises 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 present invention, the extracting APP feature and APP combination feature according to the APP installation list information further includes:
setting corresponding characteristics for the user according to the APP installation list information of the user;
and combining any two characteristics of the user to obtain the combined characteristic.
According to a preferred embodiment of the present invention, the performing the one-time encoding on the APP feature and the APP combined feature to obtain a one-time encoded feature 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.
A second aspect of the present invention provides an APP risk assessment device based on a factoring machine, comprising:
the information acquisition module is used for acquiring APP installation list information of the mobile terminal associated with the user;
the feature extraction module is used for extracting APP features and APP combination features according to the APP installation list information;
the information coding module is used for performing single-heat coding on the APP characteristics and the APP combination characteristics to obtain single-heat coding characteristics associated with a user;
and the feature training module is used for inputting the single-heat coding features into a trained machine learning model and calculating the risk score of the user.
According to a preferred embodiment of the invention, the machine learning model is a factorization machine model.
According to a preferred embodiment of the present invention, the feature extraction module further includes:
a first feature setting unit, configured to set a corresponding feature for each APP in the APP installation list information;
the first feature combination unit is used for combining features corresponding to any two APP in the APP installation list information to obtain the combined features.
According to a preferred embodiment of the present invention, the first feature setting unit further includes:
an application classification unit, configured to classify each APP in the APP installation list information;
and the feature allocation unit is used for setting the same type of APP in the APP installation list information as the same feature.
According to a preferred embodiment of the invention, said type comprises 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 present invention, the feature extraction module further includes:
the second feature setting unit is used for setting corresponding features for the user according to the APP installation list information of the user;
and the second feature combination unit is used for combining any two features of the user to obtain the combined feature.
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.
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, APP characteristics and APP combination characteristics are extracted according to the mobile terminal APP installation list information, and the characteristics are subjected to independent heat coding to obtain independent heat coding characteristics, a machine learning model is established, the independent heat coding characteristics and the credit risk results of the sample user are utilized to train the machine learning model, finally the independent heat coding characteristics converted from the mobile terminal APP installation list information of the user to be trusted are substituted into the trained machine learning model to train, so that the credit 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 view of an application scenario of an APP risk assessment method based on a factorization machine;
FIG. 2 is a schematic flow chart of an APP risk assessment method based on a factoring machine;
FIG. 3 is a schematic diagram of an APP risk assessment device module architecture based on a factoring machine according to the present invention;
FIG. 4 is a schematic diagram of an electronic device architecture for factoring machine-based APP risk assessment 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 an application scenario schematic diagram of an APP risk assessment method based on a factoring machine. In the embodiment of the invention, the financial institution server extracts APP features and APP combined features from the mobile terminal APP installation list information by collecting the mobile terminal APP installation list information of a sample user, performs independent heat encoding on the APP features and the APP combined features, converts the APP features and the APP combined features into independent heat encoding features, establishes a machine learning model, trains the machine learning model by utilizing the independent heat encoding features and the credit risk results of the sample user, and finally substitutes the independent heat encoding features converted from the mobile terminal APP installation list information of the user to be trusted into the trained machine learning model to train the credit risk of the user to be trusted.
Fig. 2 is a schematic flow chart of an APP risk assessment method based on a factoring machine. As shown in fig. 2, the method includes:
s101, acquiring APP installation list information of a mobile terminal associated with a 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, extracting APP features and APP combination features according to the APP installation list information.
Specifically, the features of each APP in the APP installation list are extracted first, all the existing APPs are classified, classification rules can refer to the classification published by the well-known website, classification can also be performed according to the purpose of each APP, such as finance class, loan class, financial class, social class, game class, work class and the like, the same features are set for APPs of the same class after classification is completed, such as game APP settable features are game or entertainment and the like, and then APPs in the user APP installation list are found out from the existing APPs, so that corresponding features can be set for each APP in the user APP installation list.
Sometimes, because the number of APPs installed on the user terminal is smaller, errors possibly existing in subsequent calculation risk are larger, set combination features are added, a large number of new combination features can be constructed on a sparse data set, when the combination features are set, firstly, combination features corresponding to combinations of any two APP of different types in all APPs are set, then, combination features corresponding to any two APP combinations in an APP installation list are found out from the combination APPs, for example, the feature corresponding to APP1 is a, the feature corresponding to APP2 is b, the feature corresponding to APP3 is c, then, three types of combination features < a, b >, < a, c >, < b, c > exist, and only APP1 and APP3 are installed in the APP installation list, then, the combination features of the user are < a, c >.
Preferably, in setting the features, besides setting the features according to the APP type disclosed above, the features may be set for the user according to the APP type used by the user, for example, a client installs APP with many female preferences and APP with many office and game types, so that it may be judged that the user may be a female white collar, and the features of the user may be set as "female", "office" and "game", and when the combination features are set for the user, any two features of the user are combined to obtain the combination features.
And S103, performing one-time thermal coding on the APP characteristics and the APP combination characteristics to obtain one-time thermal coding characteristics associated with a user.
Specifically, the APP features of a single APP are subjected to one-time thermal encoding, the financial institution server firstly obtains the coverage of the current multiple APPs through multiple ways, the APP coverage is the number of people with the APP users installed in a specific area, the number of people is larger (wider), then all APPs are ordered and sequenced according to the obtained APP coverage from the user coverage to the smaller one, the higher the APP ranking is, the more the APP ranking can be divided into various forms, such as comprehensive ranking and ranking of each category, the existing APP has multiple categories, such as financial category, loan category, financial category, social category, game category, work category and the like, the comprehensive coverage ranking of all APPs before classification can be obtained first, and then the coverage ranking of APPs under each category is obtained respectively.
After the coverage rank of the APP is obtained, the financial institution server establishes an APP set, sets a threshold range for the set, and can only divide the APP set if the requirement of the APP is greater than the threshold range, where the APP can be set to have a coverage greater than the set threshold, or can be set to have a ranking greater than the set threshold.
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, such as '1'; when a certain APP in the APP set fails to match with an APP in the APP list, it is indicated that the user does not install an APP in the APP set, and at this time, the APP is converted into a second unique thermal encoding feature, for example, "0".
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 encoding feature 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 encoding feature 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.
After the feature corresponding to the single APP is subjected to the single-hot encoding, the combined feature in the user APP installation list in all the combined features is set to be single-hot encoding "1", the combined feature not is set to be single-hot encoding "0", for example, APP1 to APP5 are respectively existing different types of APP, and the corresponding feature is a, b, c, d, e, and then the two-dimensional combined features may include: < a, b >, < a, c >, < a, d >, < a, e >, < b, c >, < b, d >, < b, e >, < c, d >, < c, e >, < d, e >. If the user installs APP1, APP3 and APP5, then it contains the combined features: < a, c >, < a, e > and < c, e >, his single heat codes are as follows:
<a,b> <a,c> <a,d> <a,e> <b,c> <b,d> <b,e> <c,d> <c,e> <d,e>
0 1 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0
the single thermal encoding feature of < a, c > is: 0100000000, < a, e > is characterized by: 0001000000, < c, e > is characterized by: 0000000010. thus, the unique heat coding features corresponding to the combined features of any two APP combinations in the user APP installation list are obtained, and the unique heat coding features corresponding to the single APP in the APP installation list obtained in the embodiment can be spliced into a series of complete unique heat coding features.
S104, inputting the single-heat coding features into a trained machine learning model, and calculating the risk scores of the users.
Specifically, according to the APP installation list information of the historical user acquired in step S101, the relevant features of the APP are extracted according to the method in step S102, the unique heat coding features of the historical user are acquired according to the method in step S103, a machine learning model is built, and the unique heat coding features of the historical user and the financial performance data thereof are input into the machine learning model as training samples, 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.
After the training of the machine learning model is completed, firstly acquiring APP installation list information of a new user, acquiring unique heat coding features according to the methods in the steps S102 and S103, and inputting the coding features into the trained credit giving risk model to obtain a risk scoring result of the user to be credit given. 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, the accuracy of the estimated trust risk result is high, the contribution degree to the trust risk when a plurality of feature combinations exist can be estimated under the condition of a sparse data set, the stability is good, the investment in modeling and feature maintenance is small, the resources are saved, and the effect is obvious.
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 an APP risk assessment device module architecture based on a factoring machine according to the present invention. As shown in fig. 3, the apparatus 200 includes:
an information acquisition module 201, configured to acquire APP installation list information of a mobile terminal associated with a user;
the feature extraction module 202 is configured to extract APP features and APP combination features according to the APP installation list information;
the information encoding module 203 is configured to perform one-time thermal encoding on the APP feature and the APP combined feature to obtain a one-time thermal encoding feature associated with a user;
and the feature training module 204 is used for inputting the single-heat coding features into a trained machine learning model and calculating the risk score of the user.
Specifically, a user accesses a financial institution server through a mobile terminal provided by a financial institution, a credit request is sent to the financial institution, after the financial institution server receives a lending request, the credit giving risk of the user to be trusted is evaluated through analysis of user data. The machine learning model used in the invention is a factorization machine model.
The feature extraction module 202 further includes:
a first feature setting unit, configured to set a corresponding feature for each APP in the APP installation list information;
the first feature combination unit is used for combining features corresponding to any two APP in the APP installation list information to obtain the combined features.
The feature setting unit further includes:
an application classification unit, configured to classify each APP in the APP installation list information;
and the feature allocation unit is used for setting the same type of APP in the APP installation list information as the same feature.
According to a preferred embodiment of the invention, said type comprises any one of the following: finance class, loan class, financing class, social class, gaming class, and work class.
The feature extraction module 202 further includes:
the second feature setting unit is used for setting corresponding features for the user according to the APP installation list information of the user;
and the second feature combination unit is used for combining any two features of the user to obtain the combined feature.
The information encoding module 203 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.
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 of APP risk assessment based on a factoring machine 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; extracting APP features and APP combination features according to the APP installation list information; performing single-heat coding on the APP features and the APP combination features to obtain single-heat coding features associated with users; and inputting the single-heat coding features into a trained machine learning model, and calculating the risk score of the 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. An APP risk assessment method based on a factoring machine, for assessing financial risk of a user, comprising:
acquiring APP installation list information of a mobile terminal associated with a user;
extracting APP features and APP combination features according to the APP installation list information;
acquiring user coverage of a plurality of APP; establishing an APP set such that the user of each APP in the APP set overrides The coverage is larger than a preset value; comparing the APP installation list of the user with the APP set respectively, if the user installs the APP set If a certain APP is 1, otherwise, 0, obtaining the first unique thermal coding feature associated with the user; according to APP installation list letter Obtaining the number of the APP installation, and performing single-heat coding according to whether the number of the APP installation exceeds a preset value to obtain the relationship with the user A second single thermal encoding feature of the link; and splicing the first single thermal coding feature and the second single thermal coding feature to form a new single thermal coding feature A thermal encoding feature; the APP coverage is the number of people who install the APP users in a specific area range；
Inputting the single-heat coding features into a trained machine learning model, and calculating a risk score of the user;
wherein: the extracting APP features and APP combination features according to the APP installation list information comprises:
setting corresponding features for each APP in the APP installation list information;
when the combination characteristics are set, firstly, the combination characteristics corresponding to the combination of any two APP of different types in all APP are set, and then the combination characteristics corresponding to any two APP combinations in the APP installation list are searched out from the combination APP.
2. The method of claim 1, wherein the machine learning model is a factorization machine model.
3. The method of claim 1, wherein the setting of the corresponding feature for each APP in the APP installation list information further comprises:
classifying each APP in the APP installation list information;
and setting the same type of APP in the APP installation list information as the same feature.
4. A method according to claim 3, wherein the type comprises any one of the following: finance class, loan class, financing class, social class, gaming class, and work class.
5. The method of claim 1, wherein extracting APP features and APP combination features from the APP installation list information further comprises:
setting corresponding characteristics for a user according to APP installation list information of the user;
and combining any two characteristics of the user to obtain the combined characteristic.
6. The method of claim 1, wherein the establishing the set of APPs such that the user coverage of each APP in the set of APPs 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.
7. An APP risk assessment device based on a factoring machine, comprising:
the information acquisition module is used for acquiring APP installation list information of the mobile terminal associated with the user;
the feature extraction module is used for extracting APP features and APP combination features according to the APP installation list information;
information coding module forAcquiring user coverage of a plurality of APP; establishing an APP set such that in the APP set The user coverage of each APP is greater than a predetermined value; comparing the user's APP installation list with the APP set, respectively, if using If a certain APP in the APP set is installed by a user, the APP is 1, otherwise, the APP is 0, and the first unique heat coding feature associated with the user is obtained; obtaining the number of the APP installation according to the APP installation list information, performing the independent heat coding according to whether the number of the APP installation exceeds a preset value, obtaining the said sumA second one-time-heat-coded feature associated with the user; and advancing the first and second unique thermal coding features Splicing the rows to form a new single-heat coding characteristic; the APP coverage is the person who installs the APP user in the specific area Number of digits；
The feature training module is used for inputting the single-heat coding features into a trained machine learning model and calculating risk scores of the users;
wherein: the feature extraction module includes:
a first feature setting unit, configured to set a corresponding feature for each APP in the APP installation list information;
the first feature combination unit is used for firstly setting the combination features corresponding to the combination of any two APP of different types in all APP when setting the combination features, and then searching the combination features corresponding to any two APP combinations in the APP installation list from the combination APP.
8. The apparatus of claim 7, wherein the machine learning model is a factorization machine model.
9. The apparatus according to claim 7, wherein the first feature setting unit further comprises:
an application classification unit, configured to classify each APP in the APP installation list information;
and the feature allocation unit is used for setting the same type of APP in the APP installation list information as the same feature.
10. The apparatus of claim 9, wherein the type comprises any one of: finance class, loan class, financing class, social class, gaming class, and work class.
11. The apparatus of claim 7, wherein the feature extraction module further comprises:
the second feature setting unit is used for setting corresponding features for the user according to the APP installation list information of the user;
and the second feature combination unit is used for combining any two features of the user to obtain the combined feature.
12. The apparatus of claim 7, wherein 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.
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.
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