Multi-platform insurance recommendation analysis system based on user demands

Abstract

The invention relates to the technical field of insurance recommendation analysis systems, in particular to a multi-platform insurance recommendation analysis system based on user requirements, which comprises the following system modules: a data acquisition and processing (terminal): accessing an insurance type data information base of an insurance company, collecting detailed information of insurance products, and combining the insurance type of the insurance company to obtain an insurance recommendation result; constructing an insurance product database; the data acquisition and processing (user) is used for collecting personal information, preference and purchase history data of the user, and performing preprocessing and cleaning to ensure the quality and integrity of the data; data analysis and processing: the system processes and mines the collected data by using a big data analysis technology, establishes a recommendation model and continuously optimizes the recommendation model so as to improve the recommendation accuracy; and searching relevance and characteristics between the user demand and the insurance product. Personalized recommendation: personalized insurance recommendation is provided for the user by analyzing the user demand and behavior, and the accuracy and satisfaction degree of the purchase decision of the user are improved.

Description

Technical Field

[0001] This invention relates to the field of insurance recommendation and analysis systems, and in particular to a multi-platform insurance recommendation and analysis system based on user needs. Background Technology

[0002] With the increasing variety of insurance businesses, significant differences exist between different insurance categories, meaning that the individualization of each type of insurance business is very high. Each category of insurance business also encompasses several different types of insurance products. Because traditional insurance business is conducted offline, insurance applications are typically submitted by sales personnel based on analysis of insurance business processes. Due to the vast number of insurance business types, accurate business implementation is difficult.

[0003] With the development of the internet and the application of big data, insurance business data for target user groups has gradually shifted from offline, manual processes to online workflows. This has mitigated the drawbacks of manual data entry and improved timeliness. Traditional insurance sales models often center on insurance companies promoting their own products, neglecting the personalized needs of users. Furthermore, the market offers a vast array of insurance products, making it difficult for users to choose the right one, resulting in inefficient insurance purchasing. Therefore, developing a multi-platform insurance recommendation and analysis system based on user needs is crucial for improving insurance purchasing efficiency and user satisfaction.

[0004] To address this, a multi-platform insurance recommendation and analysis system based on user needs is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-platform insurance recommendation and analysis system that utilizes big data and artificial intelligence technologies, focuses on user needs, and provides personalized insurance product recommendations and analysis to help users make more informed insurance purchase decisions, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-platform insurance recommendation and analysis system based on user needs, comprising the following system modules:

[0007] Data collection and processing (terminal): Access the insurance company's insurance product data database, collect detailed information on insurance products, and build an insurance product database based on the insurance company's insurance product types;

[0008] Data collection and processing (user): This involves collecting users' personal information, preferences, and purchase history data, and preprocessing and cleaning the data to ensure its quality and integrity.

[0009] Data analysis and processing: The system uses big data analytics to process and mine the collected data, build recommendation models and continuously optimize them to improve the accuracy of recommendations; and find the correlation and characteristics between user needs and insurance products.

[0010] Dynamic update unit: mainly used to dynamically update insurance product data, including insurance product price fluctuation information, insurance product type, insurance claims data, and insurance claims time;

[0011] Personalized recommendation module: Based on user profiles and big data analysis results, we develop personalized recommendation algorithms and use machine learning and data mining technologies to provide each user with insurance product recommendations that meet their needs;

[0012] Real-time analysis and iterative optimization: The system can monitor user feedback and behavioral data in real time, continuously improve and optimize the recommendation model, and enhance the accuracy and precision of the system.

[0013] Preferably, the data collection and processing (user) involves collecting and analyzing users' personal information, preferences, and purchasing behavior data to establish user profiles and accurately understand user needs.

[0014] Preferably, the data collection and processing (user) includes user behavior patterns and insurance product characteristics, providing data support for the recommendation system.

[0015] Preferably, the dynamic update unit is not limited to dynamically updating insurance database information, but also collects, processes and feeds back user demand information to the insurance company's information feedback center, so that the insurance company can specify the type of insurance based on user demand.

[0016] Preferably, the data collection and processing (user) involves the user inputting insurance product keywords, which are then combined with data analysis and processing to recommend insurance types to the user through a personalized recommendation module. This includes the frequency of user keyword input, the user's insurance type click-through rate, and the user's insurance type browsing data.

[0017] Preferably, the optimization steps of the personalization algorithm in the personalized recommendation module are as follows:

[0018] S1. Control the number of recommendations: Limit the number of recommendations based on user preferences and time constraints;

[0019] S2. Add a filtering mechanism: Based on user preferences and historical behavior, add a filtering mechanism to screen recommended content.

[0020] Preferably, in step S1, controlling the number of recommended insurance products adjusts the number of personalized recommendations based on the user's settings, on a weekly or daily basis.

[0021] Preferably, in step S2, a filtering mechanism is added to optimize keyword information based on user-input keywords and past input records, and to filter the recommended insurance product information system in a priority manner.

[0022] Preferably, the system includes hardware devices, which consist of a processor, a memory, a communication bus, a network interface, and a user interface, wherein the processor may be a CPU.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] Personalized recommendations: By analyzing user needs and behaviors, personalized insurance recommendations are provided to users, improving the accuracy and satisfaction of user purchase decisions.

[0025] Improve insurance purchase efficiency: The system can quickly find suitable products for users from a vast number of insurance products, thereby improving the user's purchase efficiency.

[0026] Cross-platform support: Users can use the system on different devices, increasing user convenience and experience.

[0027] Data-driven decision-making: The system analyzes and makes recommendations based on big data and user profiles, providing data-driven decision support and reducing the interference of subjective factors.

[0028] Wide range of applications: This system is applicable to various insurance companies and insurance sales platforms, and can provide users and insurance companies with comprehensive insurance recommendations and analysis services.

[0029] Reduce information overload: By providing users with relatively reasonable and targeted information through personalized recommendations, the problem of information overload can be avoided.

[0030] Improve user satisfaction: Make recommendations based on users' personalized needs to improve user satisfaction and user experience. Attached Figure Description

[0031] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a block diagram of the multi-platform insurance recommendation and analysis system based on user needs according to the present invention;

[0033] Figure 2 This is a flowchart of the personalized recommendation algorithm optimization of the present invention;

[0034] Figure 3 This is a block diagram of the dynamic update unit of the present invention;

[0035] Figure 4 This is a diagram of the data acquisition and processing (user) module of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figures 1 to 4 The present invention provides a technical solution:

[0038] A multi-platform insurance recommendation and analysis system based on user needs, consisting of the following system modules:

[0039] Data collection and processing (terminal): Access the insurance company's insurance product data database, collect detailed information on insurance products, and build an insurance product database based on the insurance company's insurance product types;

[0040] Data collection and processing (user): This involves collecting users' personal information, preferences, and purchase history data, and preprocessing and cleaning the data to ensure its quality and integrity.

[0041] Data analysis and processing: The system uses big data analytics to process and mine the collected data, build recommendation models and continuously optimize them to improve the accuracy of recommendations; and find the correlation and characteristics between user needs and insurance products.

[0042] Dynamic update unit: mainly used to dynamically update insurance product data, including insurance product price fluctuation information, insurance product type, insurance claims data, and insurance claims time;

[0043] Personalized recommendation module: Based on user profiles and big data analysis results, we develop personalized recommendation algorithms and use machine learning and data mining technologies to provide each user with insurance product recommendations that meet their needs;

[0044] Real-time analysis and iterative optimization: The system can monitor user feedback and behavioral data in real time, continuously improve and optimize the recommendation model, and enhance the accuracy and precision of the system.

[0045] The data collection and processing (user) involves collecting and analyzing users' personal information, preferences, and purchasing behavior data to create user profiles and accurately understand user needs.

[0046] The data collection and processing (users) includes user behavior patterns and insurance product characteristics, providing data support for the recommendation system.

[0047] The dynamic update unit is not limited to dynamically updating insurance database information, but also collects, processes and feeds back user demand information to the insurance company's information feedback center, so that the insurance company can formulate insurance product types based on user needs.

[0048] The data collection and processing (user) involves users inputting insurance product keywords, which are then analyzed and processed to recommend insurance types to users through a personalized recommendation module. This includes the frequency of user keyword input, the click-through rate of user insurance types, and user browsing data for insurance types.

[0049] The optimization steps of the personalization algorithm in the personalized recommendation module are as follows:

[0050] S1. Control the number of recommendations: Limit the number of recommendations based on user preferences and time constraints;

[0051] S2. Add a filtering mechanism: Based on user preferences and historical behavior, add a filtering mechanism to screen recommended content.

[0052] S1, controlling the number of recommendations, adjusts the number of personalized insurance recommendations based on the user's settings, on a weekly or daily basis.

[0053] S2 adds a filtering mechanism to optimize keyword information based on user-input keywords and past input records, and filters the recommended insurance product information system in a priority manner.

[0054] The system includes hardware devices, which consist of a processor, memory, communication bus, network interface, and user interface, wherein the processor may be a CPU.

[0055] Personalized recommendations: By analyzing user needs and behaviors, personalized insurance recommendations are provided to users, improving the accuracy and satisfaction of user purchase decisions.

[0056] Improve insurance purchase efficiency: The system can quickly find suitable products for users from a vast number of insurance products, thereby improving the user's purchase efficiency.

[0057] Cross-platform support: Users can use the system on different devices, increasing user convenience and experience.

[0058] Data-driven decision-making: The system analyzes and makes recommendations based on big data and user profiles, providing data-driven decision support and reducing the interference of subjective factors.

[0059] Wide range of applications: This system is applicable to various insurance companies and insurance sales platforms, and can provide users and insurance companies with comprehensive insurance recommendations and analysis services.

[0060] Reduce information overload: By providing users with relatively reasonable and targeted information through personalized recommendations, the problem of information overload can be avoided.

[0061] Improve user satisfaction: Make recommendations based on users' personalized needs to improve user satisfaction and user experience.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-platform insurance recommendation and analysis system based on user needs, characterized in that: It consists of the following system modules: Data collection and processing (terminal): Access the insurance company's insurance product data database, collect detailed information on insurance products, and build an insurance product database based on the insurance company's insurance product types; Data collection and processing (user): This involves collecting users' personal information, preferences, and purchase history data, and preprocessing and cleaning the data to ensure its quality and integrity. Data analysis and processing: The system uses big data analytics to process and mine the collected data, build recommendation models and continuously optimize them to improve the accuracy of recommendations; and find the correlation and characteristics between user needs and insurance products. Dynamic update unit: mainly used to dynamically update insurance product data, including insurance product price fluctuation information, insurance product type, insurance claims data, and insurance claims time; Personalized recommendation module: Based on user profiles and big data analysis results, we develop personalized recommendation algorithms and use machine learning and data mining technologies to provide each user with insurance product recommendations that meet their needs; Real-time analysis and iterative optimization: The system can monitor user feedback and behavioral data in real time, continuously improve and optimize the recommendation model, and enhance the accuracy and precision of the system.

2. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The data collection and processing (user) involves collecting and analyzing users' personal information, preferences, and purchasing behavior data to create user profiles and accurately understand user needs.

3. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The data collection and processing (users) includes user behavior patterns and insurance product characteristics, providing data support for the recommendation system.

4. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The dynamic update unit is not limited to dynamically updating insurance database information, but also collects, processes and feeds back user demand information to the insurance company's information feedback center, so that the insurance company can formulate insurance product types based on user needs.

5. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The data collection and processing (user) involves users inputting insurance product keywords, which are then analyzed and processed to recommend insurance types to users through a personalized recommendation module. This includes the frequency of user keyword input, the click-through rate of user insurance types, and user browsing data for insurance types.

6. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The optimization steps of the personalization algorithm in the personalized recommendation module are as follows: S1. Control the number of recommendations: Limit the number of recommendations based on user preferences and time constraints; S2. Add a filtering mechanism: Based on user preferences and historical behavior, add a filtering mechanism to screen recommended content.

7. The multi-platform insurance recommendation and analysis system based on user needs according to claim 6, characterized in that: S1, controlling the number of recommendations, adjusts the number of personalized insurance recommendations based on the user's settings, on a weekly or daily basis.

8. The multi-platform insurance recommendation and analysis system based on user needs according to claim 6, characterized in that: S2 adds a filtering mechanism to optimize keyword information based on user-input keywords and past input records, and filters the recommended insurance product information system in a priority manner.

9. The multi-platform insurance recommendation and analysis system based on user needs according to claim 1, characterized in that: The system includes hardware devices, which consist of a processor, memory, communication bus, network interface, and user interface, wherein the processor may be a CPU.

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