CN116109385A - Financial product pushing method, device, equipment and storage medium - Google Patents
Financial product pushing method, device, equipment and storage medium Download PDFInfo
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- 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
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- 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
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Abstract
The invention discloses a financial product pushing method, a financial product pushing device, financial product pushing equipment and a financial product storing medium, and relates to the field of finance. The invention provides a financial product pushing method, which comprises the following steps: acquiring user data; obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance; acquiring a recommended product set corresponding to a target user type corresponding to the first cluster, wherein the recommended product set is a product set obtained by determining the use rate of financial products corresponding to the target user type; determining a target financial product set from the recommended product set according to the demand information of the target user; pushing the target financial product set to the terminal of the target user. The need to model each financial product to recommend the user is eliminated, reducing the workload and increasing the probability of being able to push the financial product most appropriate to the user.
Description
Technical Field
The invention relates to the field of finance, in particular to a financial product pushing method, a financial product pushing device, financial product pushing equipment and a financial product storage medium.
Background
In the related art, an intelligent matching system for financial products is provided, a logistic regression discrimination model is generated for each financial product, and logistic regression discrimination models corresponding to a plurality of financial products are imported for data of new users, so that an automobile financial product set capable of being successfully loaned is obtained.
However, there are tens of thousands of financial products that are added at intervals, each of which is modeled, consumes a lot of time and labor costs, is slow to update, and the logistic regression discriminant model of the financial product that is likely to be most suitable for the user is not selected, so that the set of selected automotive financial products that can be "loaned successfully" is not optimal.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a financial product pushing method, apparatus, device, and storage medium.
According to a first aspect of an embodiment of the present disclosure, there is provided a financial product pushing method, including:
acquiring user data, wherein the user data comprises user portrait data of a target user and user financial behavior data;
obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance, wherein the first cluster model is constructed based on successful data of the paying money;
acquiring a recommended product set corresponding to a target user type corresponding to the first cluster, wherein the recommended product set is a product set obtained by determining the use rate of financial products corresponding to the target user type;
determining a target financial product set from the recommended product set according to the demand information of the target user;
pushing the target financial product set to the terminal of the target user.
Optionally, the method further comprises:
in response to receiving first financial product data of a first financial product, determining a second cluster corresponding to the first financial product according to a second cluster model constructed in advance;
and under the condition that the first user type corresponding to the second cluster is the target user type, pushing the first financial product to the terminal of the target user.
Optionally, the method further comprises:
in response to receiving successful money release data sent by a target server, updating the first clustering model according to the successful money release data;
the money release success data comprises second financial product data and first user data corresponding to the money release success data, and the second financial product data comprises one or more of pay-per-sale information, interest rate information, credit information and deadline information.
Optionally, the method further comprises:
and preprocessing the user data before obtaining the target user type corresponding to the user data based on a first clustering model constructed in advance, so as to obtain preprocessed user data.
Optionally, the preprocessing the user data to obtain preprocessed user data includes:
detecting an abnormal index and/or a missing index of each piece of sub data in the user data;
and deleting the first sub-data to obtain the preprocessed user data under the condition that the abnormality index of any piece of the first sub-data in the sub-data exceeds a preset abnormality threshold and/or the deletion index exceeds a preset deletion threshold.
Optionally, the method further comprises:
acquiring sample user data and financial product data which corresponds to each sample user data and is successful in paying, wherein the financial product data comprises one or more of pay-per-payment information, interest rate information, credit information and deadline information;
constructing the first clustering model according to the sample user data, wherein the first clustering model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one user type;
and determining a recommended product set corresponding to the user type corresponding to each cluster according to the financial product data.
Optionally, the method further comprises:
constructing a second aggregation model according to the financial product data, wherein the second aggregation model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one financial product type;
and determining the user type corresponding to the financial product type corresponding to each cluster in the second cluster model according to the sample user data.
According to a second aspect of embodiments of the present disclosure, there is provided a financial product pushing apparatus, comprising:
the first acquisition module is used for acquiring user data, wherein the user data comprises user portrait data of a target user and user financial behavior data;
the first determining module is used for obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance;
the second acquisition module is used for acquiring a recommended product set corresponding to a target user type corresponding to the first cluster, wherein the recommended product set is a product set obtained by determining according to the use rate of the financial product corresponding to the target user type;
the second determining module is used for determining a target financial product set from the recommended product set according to the requirement information of the target user;
and the pushing module is used for pushing the target financial product set to the terminal of the target user.
According to a third aspect of embodiments of the present disclosure, there is provided a computer device comprising a processor and a non-volatile memory storing computer instructions which, when executed by the processor, perform the financial product pushing method according to any one of the first aspects of the present disclosure.
According to a fourth aspect of embodiments of the present disclosure, there is provided a readable storage medium comprising a computer program, which when executed controls a computer device on which the readable storage medium resides to perform the financial product pushing method according to any one of the first aspects of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the financial products which are more suitable for the user can be effectively recommended to the user, financial institutions do not need to model each financial product to recommend the user, the workload is reduced, and the probability of pushing the financial products which are most suitable for the user to the user is also improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described. It is appreciated that the following drawings depict only certain embodiments of the invention and are therefore not to be considered limiting of its scope. Other relevant drawings may be made by those of ordinary skill in the art without undue burden from these drawings.
Fig. 1 is a flowchart illustrating a financial product pushing method according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating a financial product pushing apparatus according to an exemplary embodiment.
Fig. 3 is a block diagram of a computer device provided in accordance with an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "inner", "outer", "left", "right", etc. are based on the directions or positional relationships shown in the drawings, or the directions or positional relationships conventionally put in place when the product of the application is used, or the directions or positional relationships conventionally understood by those skilled in the art are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, terms such as "disposed," "connected," and the like are to be construed broadly, and for example, "connected" may be either fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
As automotive finances move further into the current automotive sales market, the size of automotive credit at the consumer end has reached trillion levels of volume. With such tremendous volumes, banks, car finances, guaranty systems, financing leases, consumer finance companies, etc. have introduced financial products of tens of thousands of different credit requirements.
An intelligent matching system for automobile financial products is disclosed, which comprises generating logistic regression discrimination model for each automobile financial product, importing logistic regression discrimination model for multiple automobile financial products into data of new user to obtain an automobile financial product set capable of being successfully loaned, and selecting optimal automobile financial products in the set according to initial payment, interest rate of loan, loan term and the like.
However, there are tens of thousands of financial products that are added at intervals, each of which is modeled, consumes a lot of time and labor costs, is slow to update, and the logistic regression discriminant model of the financial product that is likely to be most suitable for the user is not selected, so that the set of selected automotive financial products that can be "loaned successfully" is not optimal.
In order to solve the problems in the related art, the present disclosure provides a financial product pushing method, apparatus, device, and storage medium.
Fig. 1 is a flowchart illustrating a financial product pushing method according to an exemplary embodiment, which may be applied to a computer device, such as a mobile phone, a personal computer, etc., and may also be applied to a server, etc., which is not limited in this disclosure. The financial product may be an automotive loan financial product, or may be another financial product, which is not specifically limited in this disclosure.
As shown in FIG. 1, the method includes steps S101-S105, which are described in detail below.
S101, acquiring user data, wherein the user data comprises user portrait data of a target user and user financial behavior data.
Specifically, the user financial behavior data may include, for example, loan data of the user, data such as success of loan, failure of loan, etc., and information such as initial payment of a financial product, interest rate of loan, credit limit, etc. of the user initiating the loan.
It should be noted that, all the actions of obtaining information or data in the present application are performed under the condition of complying with the corresponding policy of data protection regulation of the country of the location and obtaining the authorization given by the owner of the corresponding device, for example, the user portrait data and the user financial behavior data, and the financial product data in the following are performed after obtaining the related license of the user or the related financial institution.
S102, obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance.
The first clustering model is constructed based on successful data of the money release.
The first cluster model may be a K-means cluster model, each cluster in the first cluster model may correspond to a user type, and the number of clusters in the first cluster model may be calibrated according to the approximate number of user types. In addition, the data of successful money release may include financial product data of financial products of successful money release and user data of successful money release users.
S103, acquiring a recommended product set corresponding to the target user type corresponding to the first cluster.
The recommended product set is a product set obtained by determining the use rate of the financial product corresponding to the target user type.
For example, for a target user type, if the usage rate of the financial product a and the financial product B in the user corresponding to the user type is the highest according to the pre-collected financial product data representation corresponding to the user type, the financial product a and the financial product B may be added into the recommended product set corresponding to the target user type.
S104, determining a target financial product set from the recommended product set according to the requirement information of the target user.
The demand information of the target user may include, for example, loan total information, loan interest rate information, and the like, and based on the demand information, a financial product suitable for the target user may be selected from a predetermined recommended product set.
S105, pushing the target financial product set to the terminal of the target user.
Specifically, the target financial product set can be pushed to the terminal of the target user by a short message mode or an application message mode or an email mode.
In the embodiment of the disclosure, the user data of the target user is obtained, the user type corresponding to the target user is determined according to the user data by the pre-built clustering model, the financial products which are commonly selected by the user are determined according to the user type, the financial products which are more suitable for the requirements of the target user are selected from the financial products which are commonly selected by the user according to the personal requirements of the target user and pushed to the user, so that financial products which are more likely to be successfully released can be effectively recommended to the user, each financial product does not need to be modeled by a financial institution to be recommended to the user, the workload is reduced, and the probability of pushing the financial products which are most suitable for the user to the user is also improved.
In some alternative embodiments, the method further comprises:
in response to receiving first financial product data of a first financial product, determining a second cluster corresponding to the first financial product according to a second cluster model constructed in advance;
and under the condition that the first user type corresponding to the second cluster is the target user type, pushing the first financial product to the terminal of the target user.
The product data of the financial product may include one or more of pay-per-view information, interest rate information, credit information and deadline information of the financial product, and may further include other information, which is not limited in this disclosure.
It will be appreciated that the second model may determine a financial product type corresponding to the financial product based on product data of the financial product, each financial product type may correspond to a user type.
The second aggregate model may include a plurality of clusters, each cluster corresponds to a financial product type, and the second aggregate model may be constructed according to sample user data and financial product data corresponding to the sample user data, wherein the financial product data is successfully released.
By adopting the scheme, under the condition that a new financial product is pushed out, the type of the financial product corresponding to the financial product can be determined based on the product data of the financial product and the second model, the type of the user corresponding to the financial product is determined based on the type of the financial product, and the new financial product is pushed to the terminal of the corresponding user type, so that the new financial product suitable for the user type can be pushed to the users in time after being pushed out, and a logistic regression judging model corresponding to the financial product is not required to be established to judge whether the user is suitable for the financial product, the workload is effectively reduced, and the probability that the user can effectively loan is improved.
In other alternative embodiments, the method further comprises:
in response to receiving successful money release data sent by a target server, updating the first clustering model according to the successful money release data;
the successful payment data comprise second financial product data and first user data corresponding to the successful payment data, and the financial product data comprise one or more of pay-for-first information, interest rate information, credit information and deadline information.
Illustratively, updating the first cluster model according to the payoff success data includes: if the usage rate of a certain cluster in the first cluster model, i.e. a certain user type, corresponding to the second financial product is greater than a preset threshold value after the successful data of the money release is added, the second financial product can be added into a recommended product set corresponding to the user type. Or after the successful data of the money release is added, the cluster of the first cluster model is changed, namely the user data comprising a certain characteristic is transformed to the user type corresponding to another cluster.
By adopting the scheme, the first clustering model is updated by receiving the successful data of the money release, a sample which is successful in the money release can be added into the construction of the first clustering model, the first clustering model can be effectively updated in real time, the instantaneity of the first clustering model is ensured, and further, financial products which are more likely to be successfully paid by being pushed to a user can be ensured.
In some alternative embodiments, the method further comprises:
and preprocessing the user data before obtaining the target user type corresponding to the user data based on a first clustering model constructed in advance, so as to obtain preprocessed user data.
The preprocessing may include data missing value and outlier processing, among other things. For example, preprocessing the user data may be cleaning data missing or anomalous in the user data.
By adopting the scheme, the user data is preprocessed before the user type corresponding to the user data is determined, irrelevant data which possibly affects the user type determination in the user data can be cleaned, the accuracy of the user type determination is effectively ensured, and further financial products can be pushed to the user more accurately.
In some possible embodiments, the preprocessing the user sample data to obtain preprocessed user data includes:
detecting an abnormal index and/or a missing index of each piece of sub data in the user data;
and deleting the first sub-data to obtain the preprocessed user data under the condition that the abnormality index of any piece of the first sub-data in the sub-data exceeds a preset abnormality threshold and/or the deletion index exceeds a preset deletion threshold.
It will be appreciated that a larger number of sub-data may be included in the user data, and specifically, the abnormality detection model may be obtained through training in advance, or the abnormality index and/or the deletion index of each piece of sub-data may be determined by the deletion detection model.
By adopting the scheme, the abnormal index and/or the missing index of each piece of sub-data in the user data are detected, and the corresponding sub-data are deleted under the condition that the abnormal index exceeds the preset abnormal threshold value and/or the missing index exceeds the preset missing threshold value, so that irrelevant data which possibly affects the user type determination in the user data can be effectively cleaned, the accuracy of the user type determination is effectively ensured, and further, financial products can be more accurately pushed to the user.
In some alternative embodiments, the method further comprises:
acquiring sample user data and financial product data which corresponds to each sample user data and is successful in paying, wherein the financial product data comprises one or more of pay-per-payment information, interest rate information, credit information and deadline information;
constructing the first clustering model according to the sample user data, wherein the first clustering model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one user type;
and determining a recommended product set corresponding to the user type corresponding to each cluster according to the financial product data.
The sample user data can be user portrait data and user financial behavior data of a plurality of users obtained by manual collection or extraction in a back-end database.
The sample user data may also be preprocessed, for example, to detect an anomaly indicator, and/or a missing indicator, for each piece of sub-data in the sample user data prior to constructing the first cluster model; deleting the first sub-data to obtain preprocessed sample user data, and constructing the first clustering model based on the preprocessed sample user data under the condition that the abnormality index of any piece of the first sub-data exceeds a preset abnormality threshold and/or the deletion index exceeds a preset deletion threshold.
Specifically, for each user type corresponding to each cluster, according to the financial product data, that is, according to the financial product with the highest usage rate in the financial product data collected in advance, for example, if the usage rates of the financial product a and the financial product B in the users corresponding to a certain user type are the highest, the financial product a and the financial product B may be added into the recommended product set corresponding to the user type.
By adopting the scheme, the sample user data and the financial product data which correspond to the sample user data and are successfully paid out are collected, the first clustering model is constructed based on the sample user data, the recommended product set which corresponds to the user type and corresponds to each clustering cluster is determined according to the financial product data, so that the users can be effectively classified, the financial product with the highest utilization rate in the financial product which corresponds to the user type and is successfully paid out is determined based on the collected historical data, the recommended product set which corresponds to each user type is further determined, the accuracy of determining the user type is effectively ensured, and further the financial product can be pushed to the users more accurately.
In yet other alternative embodiments, the method further comprises:
constructing a second aggregation model according to the financial product data, wherein the second aggregation model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one financial product type;
and determining the user type corresponding to the financial product type corresponding to each cluster in the second cluster model according to the sample user data.
By adopting the scheme, the second aggregate model is constructed by collecting the financial product data which corresponds to the sample user data and is successful in paying, the financial products can be effectively classified, the user type corresponding to each type of financial product is determined based on the sample user data, and then when a new financial product is pushed out, the newly pushed financial product can be effectively classified, and the financial product is pushed to the corresponding user, and a logistic regression judging model corresponding to the financial product is not required to be established to judge whether the user is suitable for the financial product, so that the workload is effectively reduced, and the probability that the user can effectively loan is improved.
Based on the same inventive concept, the present disclosure also provides a schematic diagram of a financial product pushing apparatus shown in fig. 2 according to an exemplary embodiment, as shown in fig. 2, the financial product pushing apparatus 20 including:
a first acquisition module 21, configured to acquire user data, where the user data includes user portrait data of a target user and user financial behavior data;
a first determining module 22, configured to obtain a first cluster corresponding to the user data according to a first cluster model that is constructed in advance;
a second obtaining module 23, configured to obtain a recommended product set corresponding to a target user type corresponding to the first cluster, where the recommended product set is a product set obtained by determining according to a use rate of a financial product corresponding to the target user type;
a second determining module 24, configured to determine a target financial product set from the recommended product set according to the requirement information of the target user;
and the pushing module 25 is used for pushing the target financial product set to the terminal of the target user.
Optionally, the financial product pushing device 20 is configured to:
in response to receiving first financial product data of a first financial product, determining a second cluster corresponding to the first financial product according to a second cluster model constructed in advance;
and under the condition that the first user type corresponding to the second cluster is the target user type, pushing the first financial product to the terminal of the target user.
Optionally, the financial product pushing device 20 is further configured to:
in response to receiving successful money release data sent by a target server, updating the first clustering model according to the successful money release data;
the money release success data comprises second financial product data and first user data corresponding to the money release success data, and the second financial product data comprises one or more of pay-per-sale information, interest rate information, credit information and deadline information.
Optionally, the financial product pushing device 20 is further configured to:
and preprocessing the user data before obtaining the target user type corresponding to the user data based on a first clustering model constructed in advance, so as to obtain preprocessed user data.
Optionally, the financial product pushing device 20 is configured to:
the preprocessing the user data to obtain preprocessed user data comprises the following steps:
detecting an abnormal index and/or a missing index of each piece of sub data in the user data;
and deleting the first sub-data to obtain the preprocessed user data under the condition that the abnormality index of any piece of the first sub-data in the sub-data exceeds a preset abnormality threshold and/or the deletion index exceeds a preset deletion threshold.
Optionally, the financial product pushing device 20 is configured to:
acquiring sample user data and financial product data which corresponds to each sample user data and is successful in paying, wherein the financial product data comprises one or more of pay-per-payment information, interest rate information, credit information and deadline information;
constructing the first clustering model according to the sample user data, wherein the first clustering model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one user type;
and determining a recommended product set corresponding to the user type corresponding to each cluster according to the financial product data.
Optionally, the financial product pushing device 20 is configured to:
constructing a second aggregation model according to the financial product data, wherein the second aggregation model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one financial product type;
and determining the user type corresponding to the financial product type corresponding to each cluster in the second cluster model according to the sample user data.
It should be noted that, the implementation principle of the foregoing financial product pushing device 20 may refer to the implementation principle of the foregoing financial product pushing method, and will not be described herein again. It should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated when actually implemented. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the financial product pushing apparatus 20 may be a processing element which is set up separately, may be implemented in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of a program code, and the functions of the financial product pushing method may be called and executed by a processing element of the apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more specific integrated circuits (application specific integrated circuit, ASIC), or one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (centralprocessing unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
The embodiment of the invention provides a computer device 100, where the computer device 100 includes a processor and a nonvolatile memory storing computer instructions, and when the computer instructions are executed by the processor, the computer device 100 executes the foregoing financial product pushing method. As shown in fig. 3, fig. 3 is a block diagram of a computer device 100 provided in accordance with an exemplary embodiment. The computer apparatus 100 includes a financial product pushing device 20, a memory 111, a processor 112, and a communication unit 113.
For data transmission or interaction, the memory 111, the processor 112 and the communication unit 113 are electrically connected to each other directly or indirectly. For example, the elements may be electrically connected to each other via one or more communication buses or signal lines. The financial product pushing apparatus 20 includes at least one software function module which may be stored in the memory 111 in the form of software or firmware (firmware) or cured in an Operating System (OS) of the computer device 100. The processor 112 is configured to execute a financial product pushing method stored in the memory 111, such as a software function module and a computer program included in the financial product pushing apparatus 20.
The embodiment of the invention provides a readable storage medium, which comprises a computer program, wherein the computer program controls computer equipment where the readable storage medium is located to execute the financial product pushing method when running.
The foregoing description, for purpose of explanation, has been presented with reference to particular embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated. The foregoing description, for purpose of explanation, has been presented with reference to particular embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.
Claims (10)
1. A financial product pushing method, comprising:
acquiring user data, wherein the user data comprises user portrait data of a target user and user financial behavior data;
obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance, wherein the first cluster model is constructed based on successful data of the paying money;
acquiring a recommended product set corresponding to a target user type corresponding to the first cluster, wherein the recommended product set is a product set obtained by determining the use rate of financial products corresponding to the target user type;
determining a target financial product set from the recommended product set according to the demand information of the target user;
pushing the target financial product set to the terminal of the target user.
2. The method according to claim 1, wherein the method further comprises:
in response to receiving first financial product data of a first financial product, determining a second cluster corresponding to the first financial product according to a second cluster model constructed in advance;
and under the condition that the first user type corresponding to the second cluster is the target user type, pushing the first financial product to the terminal of the target user.
3. The method according to claim 1, wherein the method further comprises:
in response to receiving successful money release data sent by a target server, updating the first clustering model according to the successful money release data;
the money release success data comprises second financial product data and first user data corresponding to the money release success data, and the second financial product data comprises one or more of pay-per-sale information, interest rate information, credit information and deadline information.
4. The method according to claim 1, wherein the method further comprises:
and preprocessing the user data before obtaining the target user type corresponding to the user data based on a first clustering model constructed in advance, so as to obtain preprocessed user data.
5. The method of claim 2, wherein preprocessing the user data to obtain preprocessed user data comprises:
detecting an abnormal index and/or a missing index of each piece of sub data in the user data;
and deleting the first sub-data to obtain the preprocessed user data under the condition that the abnormality index of any piece of the first sub-data in the sub-data exceeds a preset abnormality threshold and/or the deletion index exceeds a preset deletion threshold.
6. The method according to claim 2, wherein the method further comprises:
acquiring sample user data and financial product data which corresponds to each sample user data and is successful in paying, wherein the financial product data comprises one or more of pay-per-payment information, interest rate information, credit information and deadline information;
constructing the first clustering model according to the sample user data, wherein the first clustering model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one user type;
and determining a recommended product set corresponding to the user type corresponding to each cluster according to the financial product data.
7. The method of claim 6, wherein the method further comprises:
constructing a second aggregation model according to the financial product data, wherein the second aggregation model comprises a plurality of clustering clusters, and each clustering cluster corresponds to one financial product type;
and determining the user type corresponding to the financial product type corresponding to each cluster in the second cluster model according to the sample user data.
8. A financial product pusher, comprising:
the first acquisition module is used for acquiring user data, wherein the user data comprises user portrait data of a target user and user financial behavior data;
the first determining module is used for obtaining a first cluster corresponding to the user data according to a first cluster model constructed in advance;
the second acquisition module is used for acquiring a recommended product set corresponding to a target user type corresponding to the first cluster, wherein the recommended product set is a product set obtained by determining according to the use rate of the financial product corresponding to the target user type;
the second determining module is used for determining a target financial product set from the recommended product set according to the requirement information of the target user;
and the pushing module is used for pushing the target financial product set to the terminal of the target user.
9. A computer device comprising a processor and a non-volatile memory storing computer instructions that, when executed by the processor, perform the financial product pushing method of any of claims 1-7.
10. A readable storage medium, characterized in that the readable storage medium comprises a computer program, which when run controls a computer device in which the readable storage medium is located to perform the financial product pushing method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310153418.5A CN116109385A (en) | 2023-02-22 | 2023-02-22 | Financial product pushing method, device, equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310153418.5A CN116109385A (en) | 2023-02-22 | 2023-02-22 | Financial product pushing method, device, equipment and storage medium |
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| CN116109385A true CN116109385A (en) | 2023-05-12 |
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| CN202310153418.5A Pending CN116109385A (en) | 2023-02-22 | 2023-02-22 | Financial product pushing method, device, equipment and storage medium |
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| Country | Link |
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