CN112669136A - Financial product recommendation method, system, equipment and storage medium based on big data - Google Patents
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Abstract
The application relates to a financial product recommendation method, a system, equipment and a storage medium based on big data, wherein the method comprises the steps of evaluating a long-term consumption level of a user according to long-term consumption data of the user by utilizing a first preset model; judging the credit condition of the user according to the credit data of the user by using a second preset model; evaluating the income level of the user according to the long-term consumption level and the credit condition of the user; evaluating a short-term consumption hierarchy of the user according to the short-term consumption data of the user; predicting the financial product demand of the user according to the income level and the short-term consumption level of the user; determining a target financial product according to the financial product requirements of the user; pushing the target financial product to the user. According to the method and the device, the financial product requirements of the user are predicted according to the long-term consumption level, the short-term consumption level and the credit condition of the user, and the target product is determined according to the financial product requirements of the user, so that accurate recommendation of financial products is achieved, and product recommendation efficiency is improved.
Description
Technical Field
The application relates to the technical field of financial big data, in particular to a financial product recommendation method, system, equipment and storage medium based on big data.
Background
With the development of socio-economic, various financial products have started to move to thousands of households, and the demands of people on the financial products are more and more diversified. Since each person has different factors such as consumption ability, income level, consumption habits, and the like, it is necessary to formulate and recommend personalized financial products for the person of the user, and thus, a large amount of data is generated. Hadoop is a distributed computing platform that can be easily constructed and used by users. The user can easily develop and run the application program for processing mass data on the Hadoop. Another Resource coordinator (YARN) is a Hadoop Resource manager, which is a universal Resource management system and can provide uniform Resource management and scheduling for upper applications. The introduction of the method brings great benefits to the cluster in the aspects of utilization rate, uniform resource management, data sharing and the like.
Disclosure of Invention
An embodiment of the application aims to provide a financial product recommendation method based on big data, which comprises the following steps: evaluating a long-term consumption level of the user according to the long-term consumption data of the user by utilizing a first preset model; judging the credit condition of the user according to the credit data of the user by using a second preset model; evaluating the income level of the user according to the long-term consumption level and the credit condition of the user; evaluating a short-term consumption hierarchy of the user according to the short-term consumption data of the user; predicting the financial product demand of the user according to the income level and the short-term consumption level of the user; determining a target financial product according to the financial product requirements of the user; pushing the target financial product to the user.
In some embodiments, the first preset model is trained from long-term consumption data and consumption level labels of known product consumers as a data set; and the consumption level label is obtained by carrying out clustering analysis on the product consumers according to the long-term consumption data.
In some embodiments, the second pre-set model is trained from credit data of known product consumers as a data set.
In some embodiments, predicting the financial product demand of the user based on the income tier and the short-term consumption tier of the user comprises screening one or more known financial products among a plurality of known financial products as the target financial product using a content correlation algorithm and/or a collaborative filtering algorithm.
In some embodiments, predicting the financial product demand of the user according to the income tier and the short-term consumption tier of the user comprises predicting one or more product attributes of the financial product demanded by the user according to the income tier and the short-term consumption tier using a fifth preset model, wherein the product attributes comprise one or more of loan interest rate, repayment deadline, earnings rate, redemption deadline and risk level; determining the target financial product based on the financial product requirements of the user includes generating the target financial product based on one or more product attributes.
The embodiment of the application aims to provide a financial product recommendation system based on big data, which comprises an application layer, a model layer, a platform layer and a data layer; wherein the content of the first and second substances,
the data layer comprises user consumption data, credit data and user information which are stored persistently;
the platform layer is used for building a distributed cluster server, providing a distributed file management system for storing user payment information, transaction information and credit product consumption information, providing a distributed resource YARN manager for flexibly scheduling calculation tasks, providing a deep learning calculation packet and building a data processing machine learning platform;
a model layer including a consumption level evaluation model for evaluating a consumption level of the user, a credit condition evaluation model for evaluating a credit condition of the user, an income level evaluation model for evaluating an income level of the user, a product demand evaluation model for evaluating a financial product demand of the user, and a product generation model for generating a target financial product according to the financial product demand of the user;
the application layer comprises a recommendation program and is used for calling a model of the model layer to realize the evaluation of the long-term consumption level of the user according to the long-term consumption data of the user; judging the credit condition of the user according to the credit data of the user; evaluating the income level of the user according to the long-term consumption level and the credit condition of the user; evaluating a short-term consumption hierarchy of the user according to the short-term consumption data of the user; predicting the financial product demand of the user according to the income level and the short-term consumption level of the user; determining a target financial product according to the financial product requirements of the user; pushing the target financial product to the user.
An object of an embodiment of the present application is to provide a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of any one of the aforementioned recommendation methods when executing the computer program.
An object of an embodiment of the present application is to provide a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program, when executed by a processor, implements the steps of any one of the aforementioned recommendation methods.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects: according to the method and the system, the financial product requirements of the user are predicted according to the long-term consumption data, the short-term consumption level and the credit condition of the user, and the target product is determined according to the financial product requirements of the user, so that the financial product is accurately recommended, and the efficiency of recommending the financial product is improved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a big data based financial product recommendation method according to the present application;
FIG. 3 is a partial flow diagram of another embodiment of a big-data based financial product recommendation method according to the present application;
FIG. 4 is an architectural diagram of one embodiment of a big-data based financial product recommendation system according to the present application;
FIG. 5 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the financial product recommendation method provided by the embodiments of the present application generally consists ofServerImplementation of, and accordingly, financial product recommendation systems are generally provided forServerIn (1).
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In some embodiments, an electronic device on which a financial product recommendation method operates (e.g., as shown in FIG. 1)Server) The terminal equipment can be connected with the terminal equipment in a wired connection mode or a wireless connection mode. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
With continued reference to FIG. 2, FIG. 2 illustrates a flow diagram of one embodiment of a big data based financial product recommendation method according to the present application. The financial product recommendation method comprises the following steps:
and step 201, evaluating the long-term consumption level of the user according to the long-term consumption data of the user by using a first preset model.
In this embodiment, the long-term consumption data may be obtained by collecting user consumption data over a longer time range. The user consumption data may include a plurality of pieces of transaction information generated during the user's purchase of various goods or services, including, for example, information on a payment account, a payment amount, a transaction time, and the like. The long-term consumption level evaluation model can be constructed according to the long-term consumption data of other known clients and serves as a first preset model, and the long-term consumption level of the user can be evaluated according to the long-term consumption data of the user by the model. For example, the long-term consumption level is divided into a plurality of levels, the longer time range is divided into a plurality of equal time intervals, long-term consumption data of a plurality of known other clients are collected, the consumption amount of the clients in each interval is calculated, the consumption amount of the clients in each interval is used as a sample feature, the long-term consumption level is marked for the clients and used as a sample label, and a data set for training the long-term consumption level evaluation model is obtained. The long-term consumption assessment model may be various supervised machine learning models, such as a support vector machine model, and the like.
And step 202, judging the credit condition of the user according to the credit data of the user by using a second preset model.
In this embodiment, a credit status evaluation model may be constructed as a second preset model from credit data of known other customers, with which the credit status of the user is evaluated from the credit data of the user. For example, different credit conditions can be represented by a plurality of credit levels, credit data of known other clients are collected, data such as credit limits, default rates, total loan amounts and loan times in the credit data are extracted as sample characteristics, the credit levels are marked for the known other clients as sample labels, and a data set for a credit condition evaluation model is obtained. The credit assessment model may be various supervised machine learning models, such as a support vector machine model.
And step 203, evaluating the income level of the user according to the long-term consumption level and the credit condition of the user.
In this embodiment, an income level assessment model may be constructed based on the long-term consumption levels and credit statuses of known other customers, and the income level of the user is assessed based on the long-term consumption levels and credit statuses of the user using the model. For example, a revenue hierarchy may be divided into a plurality of revenue grades; and (3) taking the long-term consumption level and the credit condition of the user as sample characteristics, taking the income level as a sample label to obtain a data set, and training an income level evaluation model by using the data set. The revenue hierarchy evaluation model may be various supervised machine learning models, such as a fuzzy neural network model, and the like.
And step 204, evaluating the short-term consumption hierarchy of the user according to the short-term consumption data of the user.
In the embodiment, a short-term consumption level evaluation model can be constructed according to the short-term consumption data of other known clients in a short time range, and the short-term consumption level of the user can be evaluated according to the short-term consumption data of the user by using the model. For example, the short-term consumption level is divided into a plurality of levels, the short time range is divided into a plurality of equal time intervals, short-term consumption data of a plurality of known other clients are collected, the consumption amount of the clients in each interval is calculated, the consumption amount of the clients in each interval is used as a sample feature, the consumption short-term consumption level is marked for the clients as a sample label, and a data set for training the short-term consumption level evaluation model is obtained. The short-term consumption hierarchy condition assessment model may be various supervised machine learning models, such as a support vector machine model.
In this embodiment, specifically, data of income levels, short-term consumption levels, purchased financial products and the like of a plurality of other users who have purchased financial products may be collected, and a financial product demand may be generated by using a recommendation algorithm according to the income levels and the short-term consumption levels, where the financial product demand may include information of a plurality of financial products and may also include at least one product parameter of at least one financial product. For example, a plurality of other users may be matched according to income levels and short-term consumption levels, financial products with higher purchase times or higher purchase amounts of the users are counted, the financial products with higher purchase times or higher purchase amounts are used as financial product requirements, or product attributes of the financial products with higher purchase times are used as financial product requirements, and the product attributes may include one or more of loan interest rate, repayment duration, income rate, redemption duration and risk level.
At step 206, the target financial product is determined based on the financial product requirements of the user.
In this embodiment, one or more financial products having a large purchase number or purchase amount in the financial product demand may be used as the target financial product. One or more financial products can be matched among a plurality of existing financial products as target financial products according to the purchase times or one or more financial products with larger purchase amount in the financial product demand, for example, the matching can be carried out according to the product attributes of the financial products. One or more new financial products can be generated according to the purchase times or the product attributes of one or more financial products with larger purchase amount in the financial product demand as target financial products.
In this embodiment, a message containing the target financial product, such as a short message, an application message, an advertisement, etc., may be pushed to the terminal device bound to the user account.
According to the method and the system, the financial product requirements of the user are predicted according to the long-term consumption data, the short-term consumption level and the credit condition of the user, and the target product is determined according to the financial product requirements of the user, so that accurate recommendation of the product is realized, and the product recommendation efficiency is improved.
In some embodiments, the first preset model is trained from long-term consumption data and consumption level labels of known product consumers as a data set; and the consumption level label is obtained by carrying out clustering analysis on the product consumers according to the long-term consumer data.
In some embodiments, the second pre-set model is trained from credit data of known product consumers as a data set.
In some embodiments, predicting the financial product demand of the user based on the income tier and the short-term consumption tier of the user includes screening one or more known financial products among a plurality of known financial products as the target financial product using a content correlation algorithm and/or a collaborative filtering algorithm.
FIG. 3 is a partial flow diagram of another embodiment of a big-data based financial product recommendation method according to the present application. In some embodiments, predicting the financial product demand of the user according to the income tier and the short-term consumption tier of the user comprises predicting one or more product attributes of the financial product demanded by the user according to the income tier and the short-term consumption tier using a fifth preset model, wherein the product attributes comprise one or more of loan interest rate, repayment deadline, earnings rate, redemption deadline and risk level; determining the target financial product based on the financial product requirements of the user includes generating the target financial product based on one or more product attributes.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 4, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a financial product recommendation system based on big data, where the embodiment of the system corresponds to the embodiment of the method shown in fig. 2, and the system may be applied to various electronic devices, such as a server, where the server may be a server cluster, a single server, a physical server, or a virtual server.
As shown in fig. 4, the financial product recommendation system based on big data according to this embodiment includes: a data layer, a platform layer, a model layer and an application layer, wherein
The data layer comprises user consumption data, credit data and user information which are stored persistently; the user information may include data such as a user account, user terminal information bound to the user account, and the like.
The platform layer builds a distributed cluster server, provides a distributed file management system for storing user payment information, transaction information and credit product consumption information, provides a distributed resource YARN manager for flexibly scheduling calculation tasks, provides a deep learning calculation packet and builds a data processing machine learning platform;
the model layers include a consumption level evaluation model for evaluating a consumption level of the user, a credit condition evaluation model for evaluating a credit condition of the user, an income level evaluation model for evaluating an income level of the user, a product demand evaluation model for evaluating a financial product demand of the user, and a product generation model for generating a target financial product according to the financial product demand of the user. The consumption level evaluation model may include a long-term consumption level evaluation model and a short-term consumption level evaluation model
The application layer comprises a recommendation program and is used for calling a model of the model layer to realize the evaluation of the long-term consumption level of the user according to the long-term consumption data of the user; judging the credit condition of the user according to the credit data of the user; evaluating the income level of the user according to the long-term consumption level and the credit condition of the user (income level evaluation); evaluating a short-term consumption level (consumption level evaluation) of the user according to the short-term consumption data of the user; predicting the financial product demand (product demand assessment) of the user according to the income level and the short-term consumption level of the user; determining a target financial product according to the financial product demand of the user (product generation); pushing the target financial product to the user (product push).
In some embodiments, the recommender is further adapted to filter one or more known financial products from the plurality of known financial products as the target financial product using a content correlation algorithm and/or a collaborative filtering algorithm.
In some embodiments, the recommender is further adapted to predict the financial product demand of the user based on the income tier and the short term consumption tier of the user comprises predicting one or more product attributes of the financial product demanded by the user based on the income tier and the short term consumption tier using a fifth predetermined model, wherein the product attributes comprise one or more of loan interest rate, repayment deadline, profitability, redemption deadline and risk level; determining the target financial product based on the financial product requirements of the user includes generating the target financial product based on one or more product attributes.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 5, fig. 5 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as program codes of a financial product recommendation method based on big data. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute the program code stored in the memory 61 or process data, for example, execute the program code of the big data based financial product recommendation method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The present application provides yet another embodiment of a computer-readable storage medium having stored thereon a big data-based financial product recommendation program executable by at least one processor to cause the at least one processor to perform the steps of the big data-based financial product recommendation method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A financial product recommendation method based on big data is characterized by comprising the following steps:
evaluating a long-term consumption level of the user according to the long-term consumption data of the user by utilizing a first preset model;
judging the credit condition of the user according to the credit data of the user by using a second preset model;
evaluating the income level of the user according to the long-term consumption level and the credit condition of the user;
evaluating a short-term consumption hierarchy of the user according to the short-term consumption data of the user;
predicting the financial product demand of the user according to the income level and the short-term consumption level of the user;
determining a target financial product according to the financial product requirements of the user;
pushing the target financial product to the user.
2. The big-data based financial product recommendation method of claim 1, wherein said first pre-set model is trained from long-term consumption data and consumption level tags of known product consumers as a data set; and the consumption level label is obtained by carrying out clustering analysis on the product consumers according to the long-term consumption data.
3. The big-data based financial product recommendation method of claim 1, wherein said second pre-set model is trained from credit data of known product consumers as a data set.
4. The big-data based financial product recommendation method of claim 1, wherein predicting the user's financial product demand based on the user's income tier and short-term consumption tier comprises screening one or more known financial products among a plurality of known financial products as target financial products using a content correlation algorithm and/or a collaborative filtering algorithm.
5. The big data-based financial product recommendation method according to claim 1, wherein predicting the user's financial product demand according to the user's income tier and short term consumption tier comprises predicting one or more product attributes of the user's demanded financial product according to the income tier and short term consumption tier using a fifth preset model, wherein the product attributes include one or more of loan interest rate, repayment deadline, income rate, redemption deadline, and risk level; the determining a target financial product based on the financial product requirements of the user may include generating the target financial product based on one or more product attributes.
6. A big data based financial product recommendation system, comprising:
a data layer comprising persistently stored user consumption data, credit data, and user information;
the platform layer is used for building a distributed cluster server, providing a distributed file management system for storing user payment information, transaction information and credit product consumption information, providing a distributed resource YARN manager for flexibly scheduling calculation tasks, providing a deep learning calculation packet and building a data processing machine learning platform;
a model layer including a consumption level evaluation model for evaluating a consumption level of the user, a credit condition evaluation model for evaluating a credit condition of the user, an income level evaluation model for evaluating an income level of the user, a product demand evaluation model for evaluating a financial product demand of the user, and a product generation model for generating a target financial product according to the financial product demand of the user;
the application layer comprises a recommendation program and is used for calling a model of the model layer to realize the evaluation of the long-term consumption level of the user according to the long-term consumption data of the user; judging the credit condition of the user according to the credit data of the user; evaluating the income level of the user according to the long-term consumption level and the credit condition of the user; evaluating a short-term consumption hierarchy of the user according to the short-term consumption data of the user; predicting the financial product demand of the user according to the income level and the short-term consumption level of the user; determining a target financial product according to the financial product requirements of the user; pushing the target financial product to the user.
7. The big-data based financial product recommendation system according to claim 6, wherein the recommendation module is further configured to filter one or more known financial products among a plurality of known financial products as the target financial product using a content correlation algorithm and/or a collaborative filtering algorithm.
8. The big data-based financial product recommendation system according to claim 6, wherein the recommendation module further configured to predict the financial product demand of the user based on the income hierarchy and the short term consumption hierarchy of the user comprises predicting one or more product attributes of the financial product demanded by the user based on the income hierarchy and the short term consumption hierarchy using a fifth predetermined model, wherein the product attributes comprise one or more of loan interest rate, repayment duration, profitability, redemption duration, and risk level; the determining a target financial product based on the financial product requirements of the user may include generating the target financial product based on the one or more product attributes.
9. A computer device comprising a memory having a computer program stored therein and a processor that when executed implements the steps of the big-data based financial product recommendation method of any one of claims 1 to 5.
10. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, performs the steps of the big-data based financial product recommendation method according to any one of claims 1 to 5.
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| CN113011972A (en) * | 2021-04-28 | 2021-06-22 | 阜阳市星启链数据科技有限公司 | Financial security transaction system based on supply chain |
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Application publication date: 20210416 |