CN111951113A - Investment service method and device based on big data - Google Patents
Investment service method and device based on big data Download PDFInfo
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Abstract
The invention provides an investment service method and device based on big data, and the method comprises the following steps: acquiring user demand information; carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system; according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined; establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model; determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The invention realizes investment risk control and provides safe and efficient investment service.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to an investment service method and device based on big data.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The overseas wealth management requirements brought by overseas learning of children of high-net-value clients of banks and the global asset allocation requirements brought by global vision provide catalysts for the development of the field of overseas financial investment, and matched cross-border consultation services are generated. The client will consult the cross-border service and learn about the cross-border investment projects through multiple channels. The users consult, sign contract and arrange the translation project data through the commercial consultation service mechanism, all need to be handled manually, and then it is loaded down with trivial details and time, and because client's data is secret and risk requirement, the overseas investment case has the characteristics that the reference value is high and difficult to obtain, causes investment service risk control to be difficult to guarantee.
Therefore, how to provide a new solution, which can solve the above technical problems, is a technical problem to be solved in the art.
Disclosure of Invention
The embodiment of the invention provides an investment service method based on big data, which realizes investment risk control and provides safe and efficient investment service, and the method comprises the following steps:
acquiring user demand information;
carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system;
according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined;
establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model;
determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information;
and determining an investment service scheme according to the investment risk bearing capacity of the user.
The embodiment of the invention also provides an investment service device based on big data, which comprises:
the user demand information acquisition module is used for acquiring user demand information;
the characteristic extraction module is used for extracting the characteristics of the user demand information, determining the characteristics of the user demand and transmitting the characteristics to the service background system;
the big data matching module is used for matching the database of the service background system by using a big data technology according to the user requirement characteristics to determine a matching investment scheme;
the artificial intelligence learning model training module is used for establishing an artificial intelligence learning model, training the artificial intelligence learning model by taking the matched investment scheme as input data until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model;
the user investment risk bearing capacity determining module is used for determining the user investment risk bearing capacity according to the trained artificial intelligent learning model and the user requirement information;
and the investment service scheme determining module is used for determining the investment service scheme according to the investment risk bearing capacity of the user.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the investment service method based on big data is implemented.
An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the big data based investment service method.
The embodiment of the invention provides an investment service method and device based on big data, which comprises the steps of firstly obtaining user demand information; carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system; according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined; establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model; determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The embodiment of the invention determines the characteristics of the user requirements by extracting the characteristics of the user requirement information, thereby ensuring that the information is not leaked when the user consults and ensuring the confidentiality of investment service; then, matching is carried out on a database of the business background system by utilizing a big data technology, a matching investment scheme is determined, an artificial intelligence learning model is established, the matching investment scheme is used as input data, the artificial intelligence learning model is trained until the artificial intelligence learning model converges, and the trained artificial intelligence learning model is determined; training an artificial intelligent learning model by using the existing matching investment scheme, not leaking the existing matching investment scheme, ensuring the safety of the existing matching investment scheme in a database, simultaneously predicting the risk condition of the user consultation service by using a similar investment scheme by training the artificial intelligent learning model, and then determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user requirement information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The embodiment of the invention automatically outputs the user investment service scheme through a big data technology and an artificial intelligence learning model, realizes investment risk control and provides safe and efficient investment service.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic diagram of an investment service method based on big data according to an embodiment of the present invention.
Fig. 2 is a main flow chart illustrating steps of performing an investment service by using a big data-based investment service method according to an embodiment of the present invention.
Fig. 3 is a detailed process diagram of the steps of performing investment service by using a big data-based investment service method according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a computer device for operating a big data based investment service method implemented by the present invention.
Fig. 5 is a schematic diagram of an investment service device based on big data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a schematic diagram of an investment service method based on big data according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides an investment service method based on big data, which implements investment risk control and provides safe and efficient investment service, and the method includes:
step 101: acquiring user demand information;
step 102: carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system;
step 103: according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined;
step 104: establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model;
step 105: determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information;
step 106: and determining an investment service scheme according to the investment risk bearing capacity of the user.
The investment service method based on big data provided by the embodiment of the invention comprises the following steps of firstly, acquiring user demand information; carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system; according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined; establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model; determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The embodiment of the invention determines the characteristics of the user requirements by extracting the characteristics of the user requirement information, thereby ensuring that the information is not leaked when the user consults and ensuring the confidentiality of investment service; then, matching is carried out on a database of the business background system by utilizing a big data technology, a matching investment scheme is determined, an artificial intelligence learning model is established, the matching investment scheme is used as input data, the artificial intelligence learning model is trained until the artificial intelligence learning model converges, and the trained artificial intelligence learning model is determined; training an artificial intelligent learning model by using the existing matching investment scheme, not leaking the existing matching investment scheme, ensuring the safety of the existing matching investment scheme in a database, simultaneously predicting the risk condition of the user consultation service by using a similar investment scheme by training the artificial intelligent learning model, and then determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user requirement information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The embodiment of the invention automatically outputs the user investment service scheme through a big data technology and an artificial intelligence learning model, realizes investment risk control and provides safe and efficient investment service.
The investment service method based on big data provided by the embodiment of the invention can be implemented specifically as follows:
acquiring user demand information; carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system; according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined; establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model; determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information; and determining an investment service scheme according to the investment risk bearing capacity of the user.
In the embodiment of the invention, the big data technology is a data set which is large in scale and greatly exceeds the capability range of a traditional database software tool in the aspects of acquisition, storage, management and analysis, and has the four characteristics of large data scale, rapid data circulation, various data types and low value density. The clustering algorithm is to process a large amount of data and cluster the data according to the similarity of the data.
When the method for investment service based on big data provided by the embodiment of the present invention is implemented specifically, in an embodiment, the obtaining of the user requirement information at least includes: and acquiring subjective risk bearing capacity of the user, product investment preference of the user, credit information of the user and an investment mode of the user.
In the embodiment, a user provides a consultation request to a commercial consultation service mechanism through a cross-border investment scene platform, and the commercial consultation service mechanism refines the consultation request into subjective risk bearing capacity of the user, investment product preference of the user, credit information of the user and an investment mode of the user, so that user demand information is obtained.
When the investment service method based on big data provided by the embodiment of the present invention is implemented specifically, in an embodiment, the aforementioned feature extraction of the user demand information to determine the user demand feature includes:
carrying out feature extraction on user demand information, and determining risk types, profitability, safety factors, risk factors and financial risks;
and integrating the risk type, the yield, the safety factor, the risk factor and the financial risk to determine the user demand characteristics.
In an embodiment, the extracting the characteristics of the user requirement information and determining the user requirement characteristics may include: carrying out feature extraction on user demand information, and determining risk types, profitability, safety factors, risk factors and financial risks; and integrating the risk type, the yield, the safety factor, the risk factor and the financial risk to determine the user demand characteristics. The feature extraction ensures that information is not leaked when the user consults, and ensures the confidentiality of investment service.
When the big data-based investment service method provided by the embodiment of the present invention is implemented specifically, in an embodiment, the matching is performed in the database of the service background system by using the big data technology according to the user requirement characteristics, and the determining of the matching investment scheme includes:
and matching the investment scheme matched with the user demand characteristics with the stored investment scheme set in a database of a service background system by utilizing a decision tree algorithm and a clustering algorithm of a big data technology according to the user demand characteristics, taking the investment scheme matched with the user demand characteristics out of the database, and determining the investment scheme as a matched investment scheme.
In the embodiment, an investment scheme set consisting of a large number of investment schemes is stored in a database of a business background system of a bank, but in view of the aspects of data confidentiality, user privacy protection, investment strategies and the like, the large number of investment schemes cannot be acquired by anyone other than the person to whom the investment schemes belong; but inside the bank, the investment scheme can be utilized to carry out feature analysis, and non-sensitive information is extracted and used as a reference of a new investment scheme; therefore, according to the user demand characteristics, the decision tree algorithm and the clustering algorithm of the big data technology can be utilized to match with the stored investment scheme set in the database of the service background system, and the investment scheme matched with the user demand characteristics is taken out from the database and determined as a matched investment scheme. The matching investment scheme is an investment scheme with higher similarity to the user demand information, and has higher reference value.
Specifically, when the big data-based investment service method provided by the embodiment of the present invention is implemented, in an embodiment, the establishing of the artificial intelligence learning model, taking the matching investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model includes:
establishing an artificial intelligence learning model, and dividing a matching investment scheme into a training set and a verification set;
training the artificial intelligence learning model by taking the training set as input data, correcting parameters of the artificial intelligence learning model according to a training result, verifying by using the verification set until the result of the accuracy of the verification set is converged, and storing the artificial intelligence learning model at the moment as the trained artificial intelligence learning model.
Training an artificial intelligent learning model by using the existing matching investment scheme, not leaking the existing matching investment scheme, ensuring the safety of the existing matching investment scheme in a database, and simultaneously predicting the risk condition of the user consultation service by using a similar investment scheme by training the artificial intelligent learning model, so that the artificial intelligent learning model is established, and the matching investment scheme is divided into a training set and a verification set; training an artificial intelligence learning model by taking the training set as input data, correcting parameters of the artificial intelligence learning model according to a training result, verifying by using the verification set, detecting the accuracy of the verification set in the training process until the result of the accuracy of the verification set is converged, and storing the artificial intelligence learning model at the moment as the trained artificial intelligence learning model.
The embodiment of the invention automatically outputs the user investment service scheme through a big data technology and an artificial intelligence learning model, realizes investment risk control and provides safe and efficient investment service.
When the method for investment service based on big data provided by the embodiment of the present invention is implemented specifically, in an embodiment, the determining the investment risk tolerance of the user according to the trained artificial intelligence learning model and the user requirement information includes: and inputting the user demand information into the trained artificial intelligent learning model to obtain the user investment risk bearing capacity, wherein the user investment risk bearing capacity is divided into high wind direction bearing capacity and low risk bearing capacity.
When the big data-based investment service method provided by the embodiment of the present invention is implemented specifically, in an embodiment, the determining an investment service scheme according to the investment risk tolerance of the user includes: determining an investment service scheme according to the investment risk bearing capacity of a user; an investment service plan comprising: a high-risk investment strategy and a low-risk investment strategy; recommending a high-risk investment strategy when the investment risk bearing capacity of the user is the high wind direction bearing capacity; recommending a low-risk investment strategy when the investment risk bearing capacity of the user is the low wind direction bearing capacity; the high-risk investment strategy analyzes the performance of the enterprise or the valuation of the project, evaluates the future value of the enterprise or the project, further judges a reasonable interval and calculates a low-valuation site. The low-risk investment strategy can quantify the difference between different investment markets as much as possible, such as population base, consumption capacity and the like, and calculate the investment difference space between the local market and the investment market.
FIG. 2 is a main flow chart illustrating the steps of performing investment services by using a big data based investment service method according to an embodiment of the present invention; fig. 3 is a detailed process diagram of the steps of performing investment service by using a big data-based investment service method according to an embodiment of the present invention. As shown in fig. 2 and fig. 3, there is further provided a step of performing an investment service using a big data based investment service method provided by an embodiment of the present invention, including:
a) and the user provides a consultation request to the business consultation service mechanism through the cross-border investment scene platform.
b) According to the requirements of users, the cross-border investment scene platform can accurately calculate the investment risk and preference of each user by using a big data method and an artificial intelligence algorithm;
mainly comprises the following steps: and according to contents such as the risk bearing capacity, product preference and the like of the subjective filling of the user, combining with multi-dimensional user data analysis such as the credit condition of the user, the daily investment mode condition and the like, comprehensively judging the risk bearing capacity of the user and recommending a more appropriate project scheme. And judging the feasibility condition of the pre-pushed project scheme through an algorithm, and rejecting the relatively poor project scheme. And the cross-border investment scene platform deduces the investment risk and preference of the user by utilizing a decision tree algorithm and a clustering algorithm of big data and an artificial intelligence enumeration method and a reduction method. The big data and artificial intelligence algorithm classifies data such as risk types, profitability, safety factors, political risk factors, financial risks and the like provided by users and tries to find the internal structure of the data. And inputting data to be predicted in the original prediction model through deep learning and model training to finally obtain the recommended project scheme.
c) The business consultation service mechanism provides the project-related files and the investment strategy for the user;
mainly comprises the following steps: the high-risk investment strategy analyzes the performance of the enterprise or the valuation of the project, evaluates the future value of the enterprise or the project, further judges a reasonable interval and calculates a low-valuation site. The low-risk investment strategy can quantify the difference between different investment markets as much as possible, such as population base, consumption capacity and the like, and calculate the investment difference space between the local market and the investment market.
d) After the user pays the consultation fee, the consultation service mechanism carries out due investigation on the project party according to the requirements of the user.
e) After the investigation is finished, the consultation service mechanism feeds back the relevant information translation of the contract agreement to the user.
f) And after the user signs the contract, paying.
Fig. 4 is a schematic diagram of a computer device for executing a big data-based investment service method implemented by the present invention, and as shown in fig. 4, an embodiment of the present invention further provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the big data-based investment service method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the method for implementing big data based investment service.
The embodiment of the invention also provides an investment service device based on big data, and the investment service device is described in the following embodiment. Because the principle of the device for solving the problems is similar to the investment service method based on the big data, the implementation of the device can refer to the implementation of the investment service method based on the big data, and repeated parts are not described again.
Fig. 5 is a schematic diagram of an investment service device based on big data according to an embodiment of the present invention, and as shown in fig. 5, an investment service device based on big data according to an embodiment of the present invention is further provided, and the implementation may include:
a user requirement information obtaining module 501, configured to obtain user requirement information;
the feature extraction module 502 is configured to perform feature extraction on the user demand information, determine a user demand feature, and transmit the user demand feature to the service background system;
the big data matching module 503 is configured to perform matching by using a big data technology in a database of the service background system according to the user demand characteristics, and determine a matching investment scheme;
an artificial intelligence learning model training module 504, configured to establish an artificial intelligence learning model, train the artificial intelligence learning model using the matching investment scheme as input data until the artificial intelligence learning model converges, and determine the trained artificial intelligence learning model;
a user investment risk tolerance determining module 505, configured to determine the user investment risk tolerance according to the trained artificial intelligent learning model and the user requirement information;
and an investment service scheme determining module 506, configured to determine an investment service scheme according to the user investment risk tolerance.
In an embodiment of the invention, when the investment service device based on big data provided by the embodiment of the present invention is implemented specifically, the user requirement information obtaining module is specifically configured to: and acquiring subjective risk bearing capacity of the user, product investment preference of the user, credit information of the user and an investment mode of the user.
In an embodiment of the invention, when the investment service apparatus based on big data provided by the embodiment of the present invention is implemented specifically, the feature extraction module is specifically configured to:
carrying out feature extraction on user demand information, and determining risk types, profitability, safety factors, risk factors and financial risks;
and integrating the risk type, the yield, the safety factor, the risk factor and the financial risk to determine the user demand characteristics.
In an embodiment of the invention, when the investment service apparatus based on big data provided by the embodiment of the present invention is specifically implemented, the big data matching module is specifically configured to:
and matching the investment scheme matched with the user demand characteristics with the stored investment scheme set in a database of a service background system by utilizing a decision tree algorithm and a clustering algorithm of a big data technology according to the user demand characteristics, taking the investment scheme matched with the user demand characteristics out of the database, and determining the investment scheme as a matched investment scheme.
In an embodiment of the invention, when the investment service apparatus based on big data provided by the embodiment of the present invention is implemented specifically, the artificial intelligence learning model training module is specifically configured to:
establishing an artificial intelligence learning model, and dividing a matching investment scheme into a training set and a verification set;
training the artificial intelligence learning model by taking the training set as input data, correcting parameters of the artificial intelligence learning model according to a training result, verifying by using the verification set until the result of the accuracy of the verification set is converged, and storing the artificial intelligence learning model at the moment as the trained artificial intelligence learning model.
To sum up, the investment service method and device based on big data provided by the embodiment of the invention firstly obtain the user requirement information; carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system; according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined; establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model; determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information; and determining an investment service scheme according to the investment risk bearing capacity of the user.
The embodiment of the invention determines the characteristics of the user requirements by extracting the characteristics of the user requirement information, thereby ensuring that the information is not leaked when the user consults and ensuring the confidentiality of investment service; then, matching is carried out on a database of the business background system by utilizing a big data technology, a matching investment scheme is determined, an artificial intelligence learning model is established, the matching investment scheme is used as input data, the artificial intelligence learning model is trained until the artificial intelligence learning model converges, and the trained artificial intelligence learning model is determined; training an artificial intelligent learning model by using the existing matching investment scheme, not leaking the existing matching investment scheme, ensuring the safety of the existing matching investment scheme in a database, simultaneously predicting the risk condition of the user consultation service by using a similar investment scheme by training the artificial intelligent learning model, and then determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user requirement information; and determining an investment service scheme according to the investment risk bearing capacity of the user. The embodiment of the invention automatically outputs the user investment service scheme through a big data technology and an artificial intelligence learning model, realizes investment risk control and provides safe and efficient investment service.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A big data based investment service method, comprising:
acquiring user demand information;
carrying out feature extraction on the user demand information, determining user demand features, and transmitting the user demand features to a service background system;
according to the user demand characteristics, matching is carried out on a database of a service background system by utilizing a big data technology, and a matching investment scheme is determined;
establishing an artificial intelligence learning model, taking the matched investment scheme as input data, training the artificial intelligence learning model until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model;
determining the investment risk bearing capacity of the user according to the trained artificial intelligent learning model and the user demand information;
and determining an investment service scheme according to the investment risk bearing capacity of the user.
2. The method of claim 1, wherein the obtaining the user requirement information at least comprises: and acquiring subjective risk bearing capacity of the user, product investment preference of the user, credit information of the user and an investment mode of the user.
3. The method of claim 1, wherein the step of performing feature extraction on the user requirement information to determine the user requirement features comprises the steps of:
carrying out feature extraction on user demand information, and determining risk types, profitability, safety factors, risk factors and financial risks;
and integrating the risk type, the yield, the safety factor, the risk factor and the financial risk to determine the user demand characteristics.
4. The method of claim 1, wherein matching using big data technology in a database of a business backend system according to user demand characteristics to determine a matching investment scenario comprises:
and matching the investment scheme matched with the user demand characteristics with the stored investment scheme set in a database of a service background system by utilizing a decision tree algorithm and a clustering algorithm of a big data technology according to the user demand characteristics, taking the investment scheme matched with the user demand characteristics out of the database, and determining the investment scheme as a matched investment scheme.
5. The method of claim 1, wherein building an artificial intelligence learning model, training the artificial intelligence learning model using the matching investment plan as input data until the artificial intelligence learning model converges, and determining the post-training artificial intelligence learning model comprises:
establishing an artificial intelligence learning model, and dividing a matching investment scheme into a training set and a verification set;
training the artificial intelligence learning model by taking the training set as input data, correcting parameters of the artificial intelligence learning model according to a training result, verifying by using the verification set until the result of the accuracy of the verification set is converged, and storing the artificial intelligence learning model at the moment as the trained artificial intelligence learning model.
6. An investment service based on big data, comprising:
the user demand information acquisition module is used for acquiring user demand information;
the characteristic extraction module is used for extracting the characteristics of the user demand information, determining the characteristics of the user demand and transmitting the characteristics to the service background system;
the big data matching module is used for matching the database of the service background system by using a big data technology according to the user requirement characteristics to determine a matching investment scheme;
the artificial intelligence learning model training module is used for establishing an artificial intelligence learning model, training the artificial intelligence learning model by taking the matched investment scheme as input data until the artificial intelligence learning model converges, and determining the trained artificial intelligence learning model;
the user investment risk bearing capacity determining module is used for determining the user investment risk bearing capacity according to the trained artificial intelligent learning model and the user requirement information;
and the investment service scheme determining module is used for determining the investment service scheme according to the investment risk bearing capacity of the user.
7. The apparatus of claim 6, wherein the user requirement information obtaining module is specifically configured to: and acquiring subjective risk bearing capacity of the user, product investment preference of the user, credit information of the user and an investment mode of the user.
8. The apparatus of claim 6, wherein the feature extraction module is specifically configured to:
carrying out feature extraction on user demand information, and determining risk types, profitability, safety factors, risk factors and financial risks;
and integrating the risk type, the yield, the safety factor, the risk factor and the financial risk to determine the user demand characteristics.
9. The apparatus of claim 6, wherein the big data matching module is specifically configured to:
and matching the investment scheme matched with the user demand characteristics with the stored investment scheme set in a database of a service background system by utilizing a decision tree algorithm and a clustering algorithm of a big data technology according to the user demand characteristics, taking the investment scheme matched with the user demand characteristics out of the database, and determining the investment scheme as a matched investment scheme.
10. The apparatus of claim 6, wherein the artificial intelligence learning model training module is specifically configured to:
establishing an artificial intelligence learning model, and dividing a matching investment scheme into a training set and a verification set;
training the artificial intelligence learning model by taking the training set as input data, correcting parameters of the artificial intelligence learning model according to a training result, verifying by using the verification set until the result of the accuracy of the verification set is converged, and storing the artificial intelligence learning model at the moment as the trained artificial intelligence learning model.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing a method according to any one of claims 1 to 5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798608A (en) * | 2017-10-19 | 2018-03-13 | 深圳市耐飞科技有限公司 | A kind of investment product combined recommendation method and system |
| CN109727136A (en) * | 2018-04-12 | 2019-05-07 | 平安证券股份有限公司 | The configuration method and device of financial asset |
| CN109785140A (en) * | 2018-12-05 | 2019-05-21 | 上海拍拍贷金融信息服务有限公司 | A kind of financing investment planning method and device based on intelligent sound |
| CN110276668A (en) * | 2019-07-01 | 2019-09-24 | 中国工商银行股份有限公司 | The method and system that finance product intelligently pushing, matching degree determine |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
| CN111178767A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Risk control method and system, computer device and computer-readable storage medium |
-
2020
- 2020-08-17 CN CN202010825581.8A patent/CN111951113A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798608A (en) * | 2017-10-19 | 2018-03-13 | 深圳市耐飞科技有限公司 | A kind of investment product combined recommendation method and system |
| CN109727136A (en) * | 2018-04-12 | 2019-05-07 | 平安证券股份有限公司 | The configuration method and device of financial asset |
| CN109785140A (en) * | 2018-12-05 | 2019-05-21 | 上海拍拍贷金融信息服务有限公司 | A kind of financing investment planning method and device based on intelligent sound |
| CN110276668A (en) * | 2019-07-01 | 2019-09-24 | 中国工商银行股份有限公司 | The method and system that finance product intelligently pushing, matching degree determine |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
| CN111178767A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Risk control method and system, computer device and computer-readable storage medium |
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