A dynamic recommendation system for insurance products based on the user lifecycle

Abstract

This invention discloses a dynamic insurance product recommendation system based on the user lifecycle, comprising: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it then uses collaborative filtering, content filtering, or a hybrid recommendation algorithm to select the most suitable products from the insurance product database. This invention can provide personalized and timely insurance product recommendations based on the user's specific needs and risk status at different life stages. The system utilizes data analysis, machine learning, and the user lifecycle model to ensure the accuracy and relevance of the recommendations.

Description

Technical Field

[0001] This invention relates to the field of health insurance system technology, specifically to a dynamic recommendation system for insurance products based on the user's life cycle. Background Technology

[0002] Health insurance, short for health insurance, refers to insurance provided by insurance companies to cover losses caused by health reasons through methods such as disease insurance, medical insurance, disability income loss insurance, and long-term care insurance. It is regulated by the "Administrative Measures for Health Insurance." Types of health insurance include disease insurance, long-term care insurance, and medical insurance (including reimbursement-type medical insurance and fixed-benefit medical insurance).

[0003] Despite this, with the aging population and accelerated urbanization, the disease spectrum is undergoing profound changes, leading to a rapid increase in personalized and diversified health needs among the public. Regrettably, while there is a huge demand from the public for health insurance and health management services covering medical care, illness, nursing care, and disability, the development of commercial health insurance has lagged behind. Insurance needs dynamically evolve with changes in age, family situation, occupation, and financial status. For example, a 25-year-old single person may focus more on accident and health insurance, while a 40-year-old with children may need to consider life insurance, critical illness insurance, and children's education savings insurance. Developing differentiated health insurance products tailored to the diverse needs of various groups is an urgent priority for the development of health insurance. Therefore, it is necessary to rely on new technologies to generate differentiated, rapid, and rational health insurance products for different groups with varying needs. Summary of the Invention

[0004] The technical problem solved by this invention is to provide a dynamic recommendation system for insurance products based on the user lifecycle, so as to solve the problems mentioned in the background art.

[0005] The technical problem solved by this invention is achieved through the following technical solution: a dynamic insurance product recommendation system based on user lifecycle, comprising: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product database; when a user registers or logs in, they provide basic information, and the system uses this information to initially determine their lifecycle stage. The system analyzes user data, identifies potential protection gaps, and the recommendation engine matches the most suitable products in real time based on the user's lifecycle characteristics and needs, displaying them in a list format. The system periodically tracks changes in user data, automatically updates the user's lifecycle stage and profile, and adjusts the recommended content as needed to achieve "dynamic" recommendations.

[0006] Furthermore, the user lifecycle model defines different lifecycle stages, and associates typical risk characteristics and insurance needs with each stage.

[0007] Furthermore, the data collection and user profiling system collects user data, including age, gender, occupation, income level, family members, and existing insurance policy information, to build a comprehensive user profile and dynamically update this information.

[0008] Furthermore, the user lifecycle model defines key lifecycle events and uses a rule engine to dynamically determine the user's stage based on user data. Different risk preferences and protection requirement weights are set for different stages. When a user's status changes, the system automatically triggers the rule engine to update the user's lifecycle stage and reassess their protection requirements.

[0009] Furthermore, the data collection and user profiling system employs multi-dimensional data fusion: integrating multi-source data such as basic user information, behavioral data, transaction data, and claims data; feature engineering: extracting features that help predict insurance demand; and real-time profile updates: using real-time data stream processing technology to capture real-time user behavior and dynamically update user profile tags to ensure the timeliness of recommendations.

[0010] Furthermore, the dynamic recommendation engine employs a hybrid recommendation algorithm that comprehensively considers the accuracy of the content and the personalized needs of the user. Based on content filtering, it matches insurance products with similar attributes according to the user's current life cycle stage and protection gap analysis results. It uses natural language processing (NLP) technology to analyze the insurance terms, extract product feature tags, and match them with user demand tags.

[0011] Furthermore, the dynamic recommendation engine employs a hybrid recommendation algorithm that comprehensively considers the accuracy of the content and the personalized needs of the user. Based on collaborative filtering, it uses a user-item matrix to identify other users with similar characteristics or purchase history to the current user and recommends products that these "other users" like but the target user has not yet purchased to the target user.

[0012] Furthermore, the dynamic recommendation engine employs a hybrid recommendation algorithm that comprehensively considers the accuracy of content and the personalized needs of users. Based on a deep learning model, it uses a neural network model to combine user profile features and user behavior sequences to predict the likelihood of a user purchasing a specific product.

[0013] Furthermore, the dynamic recommendation engine employs a hybrid recommendation algorithm that comprehensively considers the accuracy of content and the personalized needs of users. It develops a quantitative guarantee gap calculation model based on a quantitative model and systematically evaluates whether the existing guarantees are sufficient by combining typical risks at different stages of the user's lifecycle.

[0014] Compared with existing technologies, the advantages of this invention are: it can provide personalized and timely insurance product recommendations based on the specific needs and risk profiles of users at different stages of their lives. The system utilizes data analysis, machine learning, and user lifecycle models to ensure the accuracy and relevance of the recommendations. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] To make the technical means, creative features, objectives and effects of the present invention easier to understand, the present invention will be further described below with reference to specific illustrations. In the description of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation", "connection" and "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection between the internal parts of two components.

[0017] Example 1

[0018] like Figure 1As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs. It uses collaborative filtering, content filtering, or hybrid recommendation algorithms to filter the most suitable products from the insurance product library. When a user registers or logs in, they provide basic information, and the system uses this information to initially determine their lifecycle stage. The system analyzes user data, identifies potential protection gaps, and the recommendation engine matches the most suitable products in real time based on the user's lifecycle characteristics and needs, displaying them in a list format. The system periodically tracks changes in user data (such as marriage, childbirth, promotion, etc.), automatically updates the user's lifecycle stage and profile, and adjusts the recommended content accordingly to achieve "dynamic" recommendations.

[0019] Example 2

[0020] like Figure 1 As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product library. The user lifecycle model defines different lifecycle stages (e.g., single life, family formation, family growth, family maturity, retirement, etc.). Typical risk characteristics and insurance needs are associated with each stage. The user lifecycle model defines key lifecycle events (e.g., marriage, childbirth, home purchase, retirement), and uses a rule engine (e.g., Drools) to dynamically determine the user's current stage based on user data. For example: IF age > 22 AND age <= 30 AND marital status == "unmarried" THEN stage = "single period" IF have children == True AND children's age < 18 THEN stage = "family growth period" Stage weights and dynamic adjustments: Different risk preferences and protection needs weights are set for different stages. When a user's status changes (e.g., marital status changes from "unmarried" to "married"), the system automatically triggers the rule engine to update the user's lifecycle stage and reassess their protection needs.

[0021] Example 3

[0022] like Figure 1 As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product database. The data collection and user profiling system collects user data, including age, gender, occupation, income level, family members, and existing policy information, to build a comprehensive user profile and dynamically updates this information. The data collection and user profiling system employs multi-dimensional data fusion: integrating user basic information, behavioral data (browsing history, search keywords), transaction data (purchased policies, premium payments), claims data, and other multi-source data. Feature engineering: extracting features that help predict insurance demand, such as "household debt-to-income ratio," "search frequency for specific diseases," and "time since last insurance purchase." Real-time profile updates: Using **real-time data streaming technologies (such as Apache Kafka and Spark Streaming)**, we capture real-time user behavior and dynamically update user profile tags to ensure timely recommendations.

[0023] Example 4

[0024] like Figure 1 As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product database. The dynamic recommendation engine uses a hybrid recommendation algorithm, comprehensively considering the accuracy of the content and the user's personalized needs. Content-based filtering matches insurance products with similar attributes (such as applicable population, coverage scope, and premium budget) based on the user's current lifecycle stage and protection gap analysis results. Natural language processing (NLP) technology is used to analyze insurance terms, extract product feature tags, and match them with user need tags.

[0025] Example 5

[0026] like Figure 1 As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or a hybrid recommendation algorithm to select the most suitable products from the insurance product database. The dynamic recommendation engine uses a hybrid recommendation algorithm, comprehensively considering the accuracy of the content and the user's personalized needs, based on collaborative filtering: using a user-item matrix, it identifies other users (neighbors) with similar characteristics or purchase history to the current user. It then recommends products that these "neighbor" users like but that the target user has not yet purchased to the target user.

[0027] Example 6

[0028] like Figure 1 As shown, a dynamic recommendation system for insurance products based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product database. The dynamic recommendation engine uses a hybrid recommendation algorithm, comprehensively considering the accuracy of the content and the user's personalized needs, based on a deep learning model: using a neural network model (such as a Wide & Deep Model) combined with user profile features (Wide side) and user behavior sequences (Deep side), it predicts the likelihood of the user purchasing a specific product.

[0029] Example 7

[0030] like Figure 1As shown, a dynamic insurance product recommendation system based on the user lifecycle includes: a data collection and user profiling system, a big data processing system, and a front-end display and interactive interface. The big data processing system is connected to the data collection and user profiling system and the front-end display and interactive interface. The big data processing system includes a dynamic recommendation engine, a user lifecycle model, and an insurance product knowledge base. The dynamic recommendation engine combines the user's current lifecycle stage, user profile, and a preset protection gap analysis model to assess the user's protection needs; it uses collaborative filtering, content filtering, or hybrid recommendation algorithms to select the most suitable products from the insurance product library. The dynamic recommendation engine uses a hybrid recommendation algorithm, comprehensively considering the accuracy of the content and the user's personalized needs, based on a quantitative model: developing a quantitative protection gap calculation model, such as based on the "double ten principle" (the insured amount is set at 10 times the annual income, and the premium expenditure accounts for 10% of the annual income) or a more complex financial situation assessment model. Risk assessment: combining typical risks at the user's lifecycle stage (such as the critical illness risk for middle-aged people and the retirement risk for the elderly), systematically assessing whether the existing protection is sufficient.

[0031] This invention enables personalization and precision: recommending insurance products that better meet users' actual needs, improving user satisfaction and purchase conversion rates. It also ensures timeliness by providing timely advice when users require specific coverage (such as when family responsibilities increase). Furthermore, it improves efficiency by automating the recommendation process, reducing the cost of manual consultations. Finally, it increases user loyalty by building long-term customer relationships through the continuous provision of valuable services.

[0032] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A user life cycle based insurance product dynamic recommendation system, characterized in that: It comprises: Data collection and user portrait system, big data processing system, front-end display and interactive interface, the big data processing system is connected with data collection and user portrait system, front-end display and interactive interface, the big data processing system comprises dynamic recommendation engine, user life cycle model, insurance product knowledge base, the dynamic recommendation engine combines the current life cycle stage of user, user portrait and preset gap analysis model of guarantee, evaluates the guarantee demand of user;Adopt collaborative filtering, content filtering or hybrid recommendation algorithm, filter out the most matched product from the insurance product library;When user registers or logs in, provide basic information, the system judges the life cycle stage of user according to the basic information, the system analyzes user data, identifies potential guarantee gap, and the recommendation engine matches the most suitable product according to the life cycle characteristic value and demand of user in real time, and displays in list form, the system regularly tracks the change of user data, automatically updates the life cycle stage and portrait of user, and adjusts the recommended content in time, realizes "dynamic" recommendation.

2. The system according to claim 1, wherein: The user life cycle model defines different life cycle stages, and associates typical risk characteristics and insurance needs for each stage. 3.The user life cycle based insurance product dynamic recommendation system of claim 1, wherein: The data collection and user portrait system collects user data, including age, gender, occupation, income level, family members, existing policy information, establishes a comprehensive user portrait, and dynamically updates these information.

4. The system according to claim 1, wherein: The user life cycle model defines key life cycle events, and uses a rule engine to dynamically determine the stage of the user according to the user data, sets different risk preferences and guarantee demand weights for different stages, and when the user state changes, the system automatically triggers the rule engine to update the user life cycle stage and reevaluate the guarantee demand.

5. The user life cycle based insurance product dynamic recommendation system of claim 1, wherein: The data collection and user portrait system uses multi-dimensional data fusion: integrates user basic information, behavior data, transaction data, claim data and other multi-source data, feature engineering: extracts features that help predict insurance needs, real-time portrait update: uses real-time data stream processing technology to capture user real-time behavior, dynamically updates user portrait tags, and ensures the immediacy of recommendation.

6. The system according to claim 1, wherein: The dynamic recommendation engine uses a hybrid recommendation algorithm, which considers the accuracy of the content and the personalized needs of the user, and is based on content filtering: according to the current life cycle stage of user and the gap analysis result of guarantee, match the insurance products with similar attributes, use natural language processing (NLP) technology to analyze insurance clauses, extract product feature tags, and match with user demand tags.

7. The system according to claim 1, wherein: The dynamic recommendation engine uses a hybrid recommendation algorithm, which considers the accuracy of the content and the personalized needs of the user, and is based on collaborative filtering: use user-item matrix to find other users with similar characteristics or purchase history as the current user, and recommend these "other users" to the target user, who like the product but have not purchased it. 8.The user life cycle based insurance product dynamic recommendation system of claim 1, wherein: The dynamic recommendation engine uses a hybrid recommendation algorithm, which considers the accuracy of the content and the personalized needs of the user, and is based on a deep learning model: use neural network model to combine user portrait features and user behavior sequence to predict the probability of user purchasing a specific product. 9.The user life cycle based insurance product dynamic recommendation system of claim 1, wherein: The dynamic recommendation engine adopts a hybrid recommendation algorithm, comprehensively considers the accuracy of the content and the personalized needs of the user, develops a quantitative guarantee gap calculation model based on a quantitative model, and systematically evaluates whether the existing guarantee is sufficient in combination with the typical risks of the user life cycle stage.

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