CN111210109A - Method and device for predicting user risk based on associated user and electronic equipment - Google Patents
Method and device for predicting user risk based on associated user and electronic equipment Download PDFInfo
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Abstract
The application provides a method for predicting user risks based on associated users, which comprises the steps of determining associated users associated with users to be predicted by second attribute characteristics when processing business data of the users with missing first attribute characteristics, obtaining first attribute characteristics of the associated users, predicting the first attribute characteristics of the users to be predicted by using the first attribute characteristics of the associated users, predicting risks of the users to be predicted based on the first attribute characteristics and the second attribute characteristics of the users to be predicted, further processing the business data of the users to be predicted, wherein the associated users are associated with the users to be predicted by the second attribute characteristics, so that certain association exists between the associated users and the users to be predicted on the first attribute characteristics, the first attribute characteristics of the users to be predicted can be predicted by using the first attribute characteristics of the associated users, and then the predicted missing characteristics are combined to predict risks and process the business data, the problem of incomplete characteristics of predicting the user risk is solved.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method and an apparatus for predicting a user risk based on an associated user, and an electronic device.
Background
In the process of business data processing (such as credit line granting, qualification review, etc.) for incoming (submitted data) users, risk assessment is often required for the users.
In the process of risk assessment for users, data submitted by users or data of users obtained from third-party platforms are generally required to be used as user characteristics for risk assessment.
However, in real-world situations, there are often situations where features are missing, for example, the data of incoming documents is incomplete due to the channel of incoming documents, or user data cannot be obtained from a third-party platform (for example, a people bank) due to policy changes, and in this case, there is a problem that the features are incomplete when risk assessment is performed only based on the incoming document data of the user.
Disclosure of Invention
The embodiment of the specification provides a method, a device and electronic equipment for predicting user risk based on a related user, and aims to solve the problem that in the prior art, when business data is processed for a user, features for predicting user risk are incomplete.
The application provides a method for predicting user risks based on associated users, which comprises the following steps:
determining a related user of a user to be predicted, wherein the user to be predicted is a user with a missing first attribute feature, and the related user is related to the user to be predicted through a second attribute feature;
acquiring a first attribute feature of the associated user, and predicting the first attribute feature of the user to be predicted by using the first attribute feature of the associated user;
predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted;
and processing the business data of the user to be predicted based on the predicted risk.
Optionally, before the determining the associated user of the user to be predicted, the method further includes: constructing a relationship map, comprising:
acquiring characteristics of multiple attributes of multiple incoming users;
associating the incoming users based on the characteristics of the incoming users to form a relation map;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated user of the user to be predicted in the relation graph.
Optionally, the associating the incoming user based on the characteristics of the incoming user comprises:
associating a plurality of incoming users with characteristic values located at the same risk level;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated users which are positioned at the same risk level with the second attribute characteristics of the user to be evaluated.
Optionally, the predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted includes:
setting weights for the characteristics of the attributes of the user to be predicted based on the attributes of the characteristics;
and predicting the risk category of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted and the weight.
Optionally, the setting, by the feature-based attribute, a weight for a feature of each attribute of the user to be predicted includes:
and if the first attribute feature and the second attribute feature have an external risk class feature, increasing the weight of the external risk class feature.
Optionally, the constructing a relationship map further includes:
and updating the relation map when the second attribute characteristics of the user to be predicted change.
Optionally, the risk tier comprises at least one of a consumption class risk tier, an income class risk tier, and a self-attribute class risk tier.
Optionally, the determining the associated user of the user to be predicted further includes:
acquiring a related user indirectly related to the user to be predicted;
the predicting the first attribute feature of the user to be predicted by using the first attribute feature of the associated user comprises the following steps:
and predicting the first attribute characteristics of the user to be predicted by utilizing the first attribute characteristics of the indirectly associated users of the directly associated users.
An embodiment of the present specification further provides an apparatus for predicting a user risk based on a related user, where the apparatus includes:
the correlation module is used for determining a correlation user of a user to be predicted, wherein the user to be predicted is a user with the missing first attribute feature, and the correlation user is correlated with the user to be predicted through a second attribute feature;
the missing characteristic prediction module is used for acquiring the first attribute characteristic of the associated user and predicting the first attribute characteristic of the user to be predicted by using the first attribute characteristic of the associated user;
the risk prediction module predicts the risk of the user to be predicted based on the first attribute characteristic and the second attribute characteristic of the user to be predicted;
and the processing module is used for processing the business data of the user to be predicted based on the predicted risk.
Optionally, the associating module is further configured to:
constructing a relationship map, comprising:
acquiring characteristics of multiple attributes of multiple incoming users;
associating the incoming users based on the characteristics of the incoming users to form a relation map;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated user of the user to be predicted in the relation graph.
Optionally, the associating the incoming user based on the characteristics of the incoming user comprises:
associating a plurality of incoming users with characteristic values located at the same risk level;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated users which are positioned at the same risk level with the second attribute characteristics of the user to be evaluated.
Optionally, the predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted includes:
setting weights for the characteristics of the attributes of the user to be predicted based on the attributes of the characteristics;
and predicting the risk category of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted and the weight.
Optionally, the setting, by the feature-based attribute, a weight for a feature of each attribute of the user to be predicted includes:
and if the first attribute feature and the second attribute feature have an external risk class feature, increasing the weight of the external risk class feature.
Optionally, the associating module is further configured to:
and updating the relation map when the second attribute characteristics of the user to be predicted change.
Optionally, the risk tier comprises at least one of a consumption class risk tier, an income class risk tier, and a self-attribute class risk tier.
Optionally, the associating module is further configured to:
acquiring a related user indirectly related to the user to be predicted;
the missing feature prediction module is further configured to:
and predicting the first attribute characteristics of the user to be predicted by utilizing the first attribute characteristics of the indirectly associated users of the directly associated users.
The present application further provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
Embodiments in the present specification provide that when the service data processing is performed on the user with the missing first attribute feature, determining a related user related to the user to be predicted by the second attribute characteristic, acquiring a first attribute characteristic of the related user, predicting the first attribute characteristic of the user to be predicted by utilizing the first attribute characteristic of the related user, predicting the risk of the user to be predicted based on the first attribute characteristic and the second attribute characteristic of the user to be predicted, further processing service data of the user to be predicted, because the associated user is associated with the user to be predicted through the second attribute characteristic, the associated user and the user to be predicted have certain association on the first attribute characteristic, the first attribute characteristic of the user to be predicted can be predicted by utilizing the first attribute characteristic of the associated user, and then risk prediction and business data processing are carried out by combining the predicted missing characteristics, and the problem that the characteristics for predicting the user risk are incomplete is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram illustrating a method for predicting a user risk based on a correlated user according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an apparatus for predicting a user risk based on an associated user according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram illustrating a method for predicting a user risk based on a correlated user according to an embodiment of the present disclosure, where the method may include:
s101, determining a related user of a user to be predicted, wherein the user to be predicted is a user with the missing first attribute feature, and the related user is related to the user to be predicted through a second attribute feature.
In this specification embodiment, a user with a missing first attribute feature may be determined based on an association between data of an existing user and the user.
In this embodiment of the present specification, before determining the associated user of the user to be predicted, the user to be predicted with a missing feature may be determined.
In an application scenario, based on data of existing users, if characteristics of some users are incomplete, the users can be used as users to be predicted, or in a process that some users submit business applications, data submitted by the users are incomplete, and only the data submitted by the users are not enough to accurately evaluate risks, the users can also be used as the users to be predicted.
The user to be predicted can be determined simultaneously with the missing features of the user to be predicted.
In this embodiment of this specification, an existing data may be used to construct a relationship graph, and the user may be associated, so before determining the associated user of the user to be predicted, the method may further include: constructing a relationship map may include:
acquiring characteristics of multiple attributes of multiple incoming users;
associating the incoming users based on the characteristics of the incoming users to form a relation map;
thus, the determining the associated user of the user to be predicted may include:
and determining the associated user of the user to be predicted in the relation graph.
The incoming user as the user who has submitted data can construct a relationship map according to the data submitted by the user, make full use of the existing data, and can also prepare for obtaining a policy change hard data source in the future. (for example, the student calendar cannot be checked, and the credit data of the people's bank cannot be checked).
Optionally, the relationship graph can be constructed by associating the users according to the dimensions of social security accumulation number, associated client consumption index condition, associated client occupation condition and associated client app preference condition.
In this embodiment of the present specification, a relationship graph may be constructed by using a preset rule, for example, a rule for associating users is preset according to a positive association degree and a negative association degree between different features of the users, and through the rule, an association manner and an association strength between the users in the relationship graph may be determined.
In this embodiment of the present specification, when a rule of the relationship graph is constructed, the size of the association degree may be determined based on the attribute of the user feature, for example, for the user-own attribute class feature, the association strength should be increased, and for the non-user-own attribute feature, the association strength may be decreased, for example, some features that are greatly affected by the market environment.
Of course, a machine learning method may also be used to construct the relationship graph, the relationship graph is trained through the service data expression of the sample user, and the association manner (for example, the feature for associating the user) and the association degree between the users are gradually modified, so that the missing feature prediction based on the modified relationship graph is more accurate.
Wherein the associating the incoming user based on the characteristics of the incoming user may include:
associating a plurality of incoming users with characteristic values located at the same risk level;
thus, the determining the associated user of the user to be predicted may include:
and determining the associated users which are positioned at the same risk level with the second attribute characteristics of the user to be evaluated.
Wherein the risk level may include at least one of a consumption-type risk level, an income-type risk level, and a self-attribute-type risk level, such as a black loan probability, a consumption level, and an income level.
In consideration of the fact that the characteristics of the user change in practical application, the relationship map can be updated in real time according to the acquired data.
Therefore, in the embodiments of the present specification, constructing the relationship map may further include:
and updating the relation map when the second attribute characteristics of the user to be predicted change.
Therefore, the relational graph is constructed when clients start to build, the relational graph is larger and larger as the number of the clients increases and is updated, the clients with complete data exist in the relational graph in a considerable scale, and the probability that future incoming clients are related to historical clients is extremely high, so that the variable of the future incoming clients can be predicted.
In this embodiment of the present specification, determining an associated user of a user to be predicted may further include:
acquiring a related user indirectly related to the user to be predicted;
predicting the first attribute feature of the user to be predicted by using the first attribute feature of the associated user may include:
and predicting the first attribute characteristics of the user to be predicted by utilizing the first attribute characteristics of the indirectly associated users of the directly associated users.
S102, acquiring the first attribute characteristics of the associated user, and predicting the first attribute characteristics of the user to be predicted by using the first attribute characteristics of the associated user.
After determining the first attribute characteristics of the associated users, considering the clustering of the objects and the groups, the first attribute characteristics between the users should have some association, and the risk change condition of the users to be predicted is more accurate by mining the characteristics of the associated customers with the users to predict the missing first attribute characteristics of the users to be predicted.
S103, predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted.
In this embodiment of the present specification, the predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted may include:
setting weights for the characteristics of the attributes of the user to be predicted based on the attributes of the characteristics;
and predicting the risk category of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted and the weight.
In the embodiment of the present specification, considering that there are also systematic risks and non-systematic risks in credit risks, weights and thresholds may be set for the first attribute features and the second attribute features according to the dispersion of the first attribute features and the second attribute features, for features of attributes common to the guest group, such features may be more concentrated among users, for dispersing risks, a greater weight and a lower risk threshold may be set for the features, and for features of attributes not in the group, more dispersed among users, a greater weight and a higher threshold may be set relatively.
In an actual application scenario, bad accounts of a certain class of customers may sharply surge, and for thresholds of models and policies for identifying the class of group customers, weights of the thresholds are different from thresholds of models and policies for identifying individual customers in a normal market environment, so that systematic risks influenced by the market can be identified more accurately, and damage can be effectively prevented before customer default.
Therefore, the setting of the weight for the feature of each attribute of the user to be predicted based on the feature-based attribute may include:
and if the first attribute feature and the second attribute feature have an external risk class feature, increasing the weight of the external risk class feature.
And S104, performing business data processing on the user to be predicted based on the predicted risk.
When the business data processing is carried out on the user with the missing first attribute characteristic, the associated user associated with the user to be predicted by the second attribute characteristic is determined, the first attribute characteristic of the associated user is obtained, the first attribute characteristic of the user to be predicted is predicted by utilizing the first attribute characteristic of the associated user, the risk of the user to be predicted is predicted based on the first attribute characteristic and the second attribute characteristic of the user to be predicted, and then the business data processing is carried out on the user to be predicted, because the associated user is associated with the user to be predicted through the second attribute characteristic, the associated user and the user to be predicted have certain association on the first attribute characteristic, the first attribute characteristic of the user to be predicted can be predicted by utilizing the first attribute characteristic of the associated user, and then risk prediction and business data processing are carried out by combining the predicted missing characteristics, and the problem that the characteristics for predicting the user risk are incomplete is solved.
In the embodiment of the present specification, the business data processing is performed on the user to be predicted based on the predicted risk, which may be directly making a trust policy for the user, or may be used to store user data for the user, so that the data may be utilized when services such as trust and dynamic support are performed subsequently, so as to prevent the problem of future data loss, and no specific explanation or limitation is provided herein.
Fig. 2 is a schematic structural diagram of an apparatus for predicting a user risk based on an associated user according to an embodiment of the present disclosure, where the apparatus may include:
the association module 201 is configured to determine an associated user of a user to be predicted, where the user to be predicted is a user with a missing first attribute feature, and the associated user is associated with the user to be predicted through a second attribute feature;
the missing feature prediction module 202 is configured to obtain a first attribute feature of the associated user, and predict a first attribute feature of the user to be predicted by using the first attribute feature of the associated user;
the risk prediction module 203 predicts the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted;
and the processing module 204 is used for processing the service data of the user to be predicted based on the predicted risk.
Optionally, the association module 201 may further be configured to:
constructing a relationship map may include:
acquiring characteristics of multiple attributes of multiple incoming users;
associating the incoming users based on the characteristics of the incoming users to form a relation map;
the determining the associated user of the user to be predicted may include:
and determining the associated user of the user to be predicted in the relation graph.
Optionally, the associating the incoming user based on the characteristics of the incoming user may include:
associating a plurality of incoming users with characteristic values located at the same risk level;
the determining the associated user of the user to be predicted may include:
and determining the associated users which are positioned at the same risk level with the second attribute characteristics of the user to be evaluated.
Optionally, the predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted may include:
setting weights for the characteristics of the attributes of the user to be predicted based on the attributes of the characteristics;
and predicting the risk category of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted and the weight.
Optionally, the setting of the weight for the feature of each attribute of the user to be predicted based on the feature-based attribute may include:
and if the first attribute feature and the second attribute feature have an external risk class feature, increasing the weight of the external risk class feature.
Optionally, the association module 201 may further be configured to:
and updating the relation map when the second attribute characteristics of the user to be predicted change.
Optionally, the risk tier may include at least one of a consumption class risk tier, an income class risk tier, and a self-attribute class risk tier.
Optionally, the association module 201 may further be configured to:
acquiring a related user indirectly related to the user to be predicted;
the missing feature prediction module 202 may be further configured to:
and predicting the first attribute characteristics of the user to be predicted by utilizing the first attribute characteristics of the indirectly associated users of the directly associated users.
The device determines the associated user associated with the user to be predicted by the second attribute characteristic when the user with the missing first attribute characteristic is processed with the business data, acquires the first attribute characteristic of the associated user, predicts the first attribute characteristic of the user to be predicted by utilizing the first attribute characteristic of the associated user, predicts the risk of the user to be predicted based on the first attribute characteristic and the second attribute characteristic of the user to be predicted, and further processes the business data of the user to be predicted, because the associated user is associated with the user to be predicted through the second attribute characteristic, the associated user and the user to be predicted have certain association on the first attribute characteristic, the first attribute characteristic of the user to be predicted can be predicted by utilizing the first attribute characteristic of the associated user, and then risk prediction and business data processing are carried out by combining the predicted missing characteristics, and the problem that the characteristics for predicting the user risk are incomplete is solved.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for predicting user risk based on a correlated user, comprising:
determining a related user of a user to be predicted, wherein the user to be predicted is a user with a missing first attribute feature, and the related user is related to the user to be predicted through a second attribute feature;
acquiring a first attribute feature of the associated user, and predicting the first attribute feature of the user to be predicted by using the first attribute feature of the associated user;
predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted;
and processing business data of the user based on the predicted risk.
2. The method of claim 1, prior to the determining the associated user of the user to be predicted, further comprising: constructing a relationship map, comprising:
acquiring characteristics of multiple attributes of multiple incoming users;
associating the incoming users based on the characteristics of the incoming users to form a relation map;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated user of the user to be predicted in the relation graph.
3. The method according to any one of claims 1-2, wherein said associating the incoming user based on the characteristics of the incoming user comprises:
associating a plurality of incoming users with characteristic values located at the same risk level;
the determining the associated user of the user to be predicted comprises the following steps:
and determining the associated users which are positioned at the same risk level with the second attribute characteristics of the user to be evaluated.
4. The method according to any one of claims 1-3, wherein the predicting the risk of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted comprises:
setting weights for the characteristics of the attributes of the user to be predicted based on the attributes of the characteristics;
and predicting the risk category of the user to be predicted based on the first attribute feature and the second attribute feature of the user to be predicted and the weight.
5. The method according to any one of claims 1 to 4, wherein the weighting is set for the feature of each attribute of the user to be predicted based on the feature-based attribute, and comprises:
and if the first attribute feature and the second attribute feature have an external risk class feature, increasing the weight of the external risk class feature.
6. The method according to any one of claims 1-5, wherein the constructing a relationship map further comprises:
and updating the relation map when the second attribute characteristics of the user to be predicted change.
7. The method of any one of claims 1-6, wherein the risk tiers comprise at least one of a consumer risk tier, a revenue risk tier, and a self-attribute risk tier.
8. An apparatus for predicting user risk based on an associated user, comprising:
the correlation module is used for determining a correlation user of a user to be predicted, wherein the user to be predicted is a user with the missing first attribute feature, and the correlation user is correlated with the user to be predicted through a second attribute feature;
the missing characteristic prediction module is used for acquiring the first attribute characteristic of the associated user and predicting the first attribute characteristic of the user to be predicted by using the first attribute characteristic of the associated user;
the risk prediction module predicts the risk of the user to be predicted based on the first attribute characteristic and the second attribute characteristic of the user to be predicted;
and the processing module is used for processing the business data of the user to be predicted based on the predicted risk.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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