Intelligent insurance solution method and system based on big data and block chain
Through big data technology and blockchain technology, combined with machine learning algorithms and smart contracts, the problem that the traditional insurance industry cannot provide personalized solutions is solved, personalized insurance recommendations and data security are achieved, and customers are fully protected.
Patent Information
- Application Number
- CN202411055859.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-02
- Publication Date
- 2024-12-31
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional insurance industry lacks personalized and comprehensive insurance plans and cannot customize recommendations based on customers' specific needs, resulting in limited insurance coverage and difficult to meet customers' specific risk needs.
Big data technology is used to collect customers' financial and medical information, risk assessment is carried out through machine learning algorithms, and automatic execution of smart contracts is achieved by combining blockchain technology, providing personalized insurance solutions and improving the transparency and security of insurance contracts.
It realizes more personalized and comprehensive insurance recommendations, ensures that customers are fully protected, improves operational efficiency and data security, and provides a more real-time and accurate insurance solution.
Smart Images
Figure CN119228548A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of smart insurance technology, and specifically to a smart insurance solution and system based on big data and blockchain. Background Art
[0002] There is a significant problem in the traditional insurance industry, that is, the insurance plans it promotes and provides are usually lacking in personalization and comprehensiveness. This is due to the lack of understanding of customer needs and the lack of detailed risk assessment in the traditional model. In the traditional model, insurance companies often adopt universal plans and fail to make personalized recommendations based on individual customers' financial status, health status, and the mutual influence of family members. This deficiency leads to two problems. First, it is difficult for insurance companies to provide customized insurance plans that meet the specific needs of customers. Customers may purchase unnecessary insurance or ignore some important risks. Second, due to the lack of risk assessment and insurance promotion in the traditional model, the insurance coverage is limited and fails to fully cover the various risks that customers may face.
[0003] With the development of emerging technologies such as big data technology and blockchain, there is now an opportunity to achieve personalized insurance recommendations through more comprehensive and real-time data analysis, more accurate risk assessment, and more efficient information storage and management. Using big data technology, we can gain in-depth understanding of multi-dimensional information such as customers' financial status and medical history, and provide them with more comprehensive and targeted insurance solutions. At the same time, the introduction of blockchain technology can improve the transparency and security of insurance contracts, and automatically execute contract conditions through smart contracts, thereby improving operational efficiency and reducing potential risks. Therefore, the present invention combines technologies such as big data and blockchain to build an intelligent insurance solution based on the actual needs of customers, thereby achieving more personalized and comprehensive insurance recommendations. Summary of the invention
[0004] The purpose of the present invention is to provide a smart insurance solution and system based on big data and blockchain to solve the problems raised in the above background technology.
[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent insurance solution system based on big data and blockchain, the operation method of the system comprises the following steps: Step 1: Integrate financial and medical information by collecting basic information of the insured and his / her family members; Step 2: Implement individual and family overall risk assessment through machine learning algorithms and big data analysis; Step 3: Based on the results of risk analysis, the system recommends personalized insurance plans; Step 4: Upload the customized insurance plan to the blockchain in the form of a smart contract for storage.
[0006] According to the above technical solution, the steps of integrating financial and medical information by collecting basic information of the insured and his family members include: The system establishes a secure data interface with major banks, medical institutions and social media platforms to obtain relevant information of the insured. In terms of financial information, the system obtains the insured's financial data through the insured's bank records, electronic payment information and tax data. The system is integrated with the API of the financial institution to obtain the insured's income, expenditure, assets and debt information in real time. For the insured's basic medical information, the system uses data fusion technology to integrate with the information system of the medical institution to extract the insured's medical data from the electronic health record system of the medical institution, including medical records, examination reports, and medication records. Finally, the system uses social media data integration technology to obtain the insured's public information on social media platforms, such as social circles and interests and hobbies.
[0007] According to the above technical solution, the steps of implementing individual and family overall risk assessment through machine learning algorithms and big data analysis include: Use machine learning algorithms and big data analytics to assess the health and financial risks of policyholders; Based on the individual risk assessment, an overall family risk assessment is conducted, taking into account collaborative risks and family relationships.
[0008] According to the above technical solution, the steps of using machine learning algorithms and big data analysis to assess the health and financial risks of the insured include: The system preprocesses the collected data, including removing duplicate records, processing missing values and outliers. Then the system collects data from bank records, medical records and social media information. Bank records include family members' income and expenditure, medical records include medical history and medication information, and social media information includes interests and hobbies. Then the system extracts numerical features from bank records, such as the mean and standard deviation of income and expenditure, extracts information about chronic diseases and past medical frequency from medical records, and extracts specific keywords from social media information to measure family members' lifestyles and interests. Finally, a random forest learning model is selected to process the diverse data. A random forest containing 10 trees was selected in the random forest, and the maximum depth of each tree was 5. Each time a decision tree was trained, the system randomly selected 70% of the family member data for training, and at each node, the system randomly selected 80% of the features for node splitting. For each member, each tree was trained on a randomly selected subset, and the tree structure was established by maximizing information gain. The 10 trees were integrated into a random forest. For the classification problem of health risks, the final prediction was determined by majority voting, and the validation set was used to evaluate the model performance to obtain accuracy and recall indicators. Finally, the test set was used to verify the performance of the model on new family member data.
[0009] According to the above technical solution, based on the individual risk assessment, the steps of conducting a family overall risk assessment, taking into account collaborative risks and family relationships, include: On the basis of individual risk assessment, the system conducts overall family risk assessment and calculates the overall risk level of the family in terms of health and finance. In the overall family risk assessment stage, the system first establishes a relationship model between family members. Through big data analysis, the system collects and integrates financial information and medical history data to establish a comprehensive family relationship network. Through this relationship model, the system can understand the collaborative risks between family members, such as the impact of a member's health problems on the living standards of other members. At the same time, in the correlation analysis stage, the system deeply explores the correlation between family members through big data analysis, including financial dependence and the mutual influence of health status. In the comprehensive risk assessment process, the system first sets weights for different risk types to reflect the family's relative preferences for health and finance. The weight setting is obtained through risk preference surveys of family members or historical data analysis. Then a comprehensive assessment algorithm is designed, which comprehensively considers the impact of individual risks, collaborative risks, and the relationship between finance and health. Finally, the system uses the designed comprehensive assessment algorithm to evaluate the integrated family information and derive the overall risk level of the family in terms of health and finance, thereby formulating a personalized insurance plan.
[0010] According to the above technical solution, the steps of implementing personalized insurance plan recommendation by the system based on the risk analysis results include: The system combines the individual risk assessment of each policyholder and considers their actual needs and preferences. By using collaborative filtering, the system can customize the most suitable insurance plan for the policyholder based on the policyholder's personal information, risk preferences, and previously purchased insurance product data. Furthermore, the system recommends corresponding family insurance products based on the policyholder's family status and the association rules between family members. When the association analysis shows that the health status of a family member has an impact on other members, the system recommends corresponding family insurance products to ensure that the entire family can be fully protected when facing risks. In addition, the system uses mathematical optimization models to consider the cost, coverage, and deductible factors of different insurance products to maximize the satisfaction of policyholders while keeping the overall premium within a reasonable range.
[0011] According to the above technical solution, the step of uploading the customized insurance plan to the blockchain in the form of a smart contract for storage includes: The system first uses an encryption algorithm to encrypt the insurance plan, and then stores the encrypted insurance plan on the blockchain through a smart contract. In this process, the distributed characteristics of the blockchain are used to store the information of the insurance plan in a distributed manner on multiple nodes. In addition, the system uses a hash function to generate a unique identifier for the plan and associates the identifier with the smart contract address to facilitate subsequent retrieval and verification. Once the insurance plan is uploaded to the blockchain, the smart contract will be responsible for automatically executing the conditions specified in the contract. When an insurance accident occurs to the insured, the smart contract can automatically trigger the claims process according to the terms of the contract. The system has pre-set logic embedded in the smart contract, and automatically determines whether the claims conditions are met based on real-time data input and status changes.
[0012] According to the above technical solution, the system includes: The information collection module is used to obtain the basic financial and medical information of the insured, and interact with major banks, medical institutions, and social media platforms by establishing a secure data interface to ensure comprehensive collection of relevant data; The risk assessment module is used to assess the individual health risks of the insured through machine learning algorithms and big data analysis, and to conduct overall family risk assessment based on the relationship model, providing a comprehensive and accurate risk level; The personalized insurance plan module is used to combine individual risk assessment and policyholder needs to provide each policyholder with a personalized and most suitable insurance plan through collaborative filtering, family association rule application and mathematical optimization model.
[0013] According to the above technical solution, the information collection module includes: The data source interface establishment module is used to establish a secure data interface with major banks, medical institutions, and social media platforms to ensure the acquisition of relevant information about the policyholder; Financial information collection module, which uses financial institution APIs to obtain the policyholder's income, expenditure, assets and debt information in real time; The medical information integration module is used to extract the insured person's medical data, including medical records, examination reports, and medication records, from the electronic health record system of medical institutions through data fusion technology.
[0014] According to the above technical solution, the risk assessment module includes: The data preprocessing and feature extraction module is used to preprocess the collected data, including removing duplicate records, processing missing values and outliers, and extracting numerical features; A machine learning model training module, which is used to classify individual health risks by comprehensively processing financial, medical and social media information through random forest learning models; The family overall risk assessment module is used to establish a family relationship model based on individual risk assessment and conduct overall family risk assessment in terms of health and finance.
[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the present invention, first, the system integrates the insured information from banks, medical institutions and social media in real time through big data technology, ensuring the accuracy and completeness of family information. In step two, the system uses machine learning and big data analysis to accurately evaluate the insured's health risks through a random forest model, establish a relationship model and conduct correlation analysis, and fully understand the collaborative risks of family members. Subsequently, based on the risk analysis results, the system uses collaborative filtering, association rules, mathematical optimization models and other methods to provide each insured with a personalized insurance plan to ensure that the whole family is fully protected when facing risks. Finally, the system securely uploads the customized insurance plan to the blockchain through smart contracts, and uses the distributed characteristics of the blockchain and the automatic execution of smart contracts to improve operational efficiency while ensuring the security and transparency of the data. The entire system integrates big data analysis and blockchain technology to provide policyholders with more real-time, comprehensive and accurate personalized insurance solutions to ensure the security and credibility of the data. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flowchart of a smart insurance solution based on big data and blockchain provided in Embodiment 1 of the present invention; Figure 2A schematic diagram of the module composition of an intelligent insurance solution system based on big data and blockchain provided in Example 2 of the present invention. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0018] Embodiment 1: Figure 1 This is a flowchart of a smart insurance solution based on big data and blockchain provided in Example 1 of the present invention. This embodiment can be applied to the scenario of insurance customization. The method can be executed by a smart insurance solution system based on big data and blockchain provided in this embodiment. Figure 1 As shown, the method specifically comprises the following steps: Step 1: Integrate financial and medical information by collecting basic information of the insured and his / her family members; In the embodiment of the present invention, the system collects the financial information and basic medical information of the insured person through big data technology, including data on income, expenditure, assets, debts, health status, family members, etc.; Exemplarily, the system establishes a secure data interface with major banks, medical institutions and social media platforms to obtain relevant information of the insured. In terms of financial information, the system obtains the insured's financial data through the insured's bank records, electronic payment information, tax data, etc. The system is integrated with the API of the financial institution to obtain key financial indicators such as the insured's income, expenditure, assets and debts in real time. For the insured's basic medical information, the system uses data fusion technology to integrate with the information system of the medical institution to extract the insured's medical data from the electronic health record system of the medical institution, including medical records, examination reports, medication records, etc. Finally, the system uses social media data integration technology to obtain the insured's public information on social media platforms, such as social circles, interests and hobbies, etc. This information can provide a more accurate basis for customized insurance plans.
[0019] Step 2: Implement individual and family overall risk assessment through machine learning algorithms and big data analysis; In the embodiment of the present invention, machine learning algorithms and big data analysis are used to assess the risk of the policyholder, and then based on the individual risk assessment, the system conducts an overall risk assessment of the family; Exemplarily, the system preprocesses the collected data, including removing duplicate records, processing missing values and outliers, and then collects data from bank records, medical records and social media information, where bank records include family members' income and expenditure, medical records include medical history and medication information, and social media information includes interests and hobbies. The system then extracts numerical features from bank records, such as the mean and standard deviation of income and expenditure, extracts information about chronic diseases and past medical frequency from medical records, and extracts specific keywords from social media information to measure family members' lifestyles and interests. Finally, a random forest learning model is selected to process the diverse data, and a random forest is selected. A random forest with 10 trees, each with a maximum depth of 5. Each time a decision tree is trained, the system randomly selects 70% of the family member data for training, and at each node, the system randomly selects 80% of the features for node splitting. For each member, each tree is trained on a randomly selected subset, and the tree structure is established by maximizing information gain. The 10 trees are integrated into a random forest. For the classification of health risks, the final prediction is determined by majority voting. The validation set is used to evaluate the model performance, and indicators such as accuracy and recall are obtained. Finally, the test set is used to verify the performance of the model on new family member data to ensure that the model can robustly assess health risks in practical applications. Exemplarily, based on individual risk assessment, the system conducts an overall family risk assessment, and the system calculates the overall risk level of the family in terms of health and finance. In the overall family risk assessment stage, the system first establishes a relationship model between family members. Through big data analysis, the system collects and integrates multi-dimensional data such as financial information and medical history to establish a comprehensive family relationship network. Through this relationship model, the system can understand the collaborative risks between family members, such as the impact that a member's health problems may have on the living standards of other members. At the same time, in the correlation analysis stage, the system deeply explores the possible correlations between family members through big data analysis, including financial dependence and the mutual influence of health status. In the comprehensive risk assessment process, the system first sets weights for different risk types to reflect the family's relative preferences for health and finance. The weight setting is obtained through a risk preference survey of family members or historical data analysis. Then a comprehensive assessment algorithm is designed, which comprehensively considers the impact of individual risks, collaborative risks, and the relationship between finance and health. Finally, the system uses the designed comprehensive assessment algorithm to evaluate the integrated family information and obtain the overall risk level of the family in terms of health and finance. This assessment result provides the system with an accurate data basis for formulating personalized insurance plans to ensure that the risk needs of the family in different aspects are covered.
[0020] Step 3: Based on the results of risk analysis, the system recommends personalized insurance plans; In the embodiment of the present invention, based on the results of risk analysis, the system provides each policyholder with a personalized insurance plan, which can be adjusted according to the actual needs of the policyholder; Exemplarily, the system combines the individual risk assessment of each policyholder and considers their actual needs and preferences. By using collaborative filtering, the system can customize the most suitable insurance plan for the policyholder based on the policyholder's personal information, risk preferences, and previously purchased insurance products. Furthermore, the system recommends corresponding family insurance products based on the policyholder's family status and the association rules between family members. When the association analysis shows that the health status of a family member has an impact on other members, the system recommends corresponding family insurance products to ensure that the entire family is fully protected when facing risks. In addition, the system uses mathematical optimization models to consider factors such as the cost, coverage, and deductible of different insurance products to maximize the policyholder's needs while keeping the overall premium within a reasonable range, which ensures that the customized plan meets the actual needs of the policyholder.
[0021] Step 4: Upload the customized insurance plan to the blockchain in the form of a smart contract for storage.
[0022] In an embodiment of the present invention, the customized insurance plan is uploaded to the blockchain in the form of a smart contract for storage, and the smart contract on the blockchain automatically executes the conditions specified in the contract; Exemplarily, the system first uses an encryption algorithm to encrypt the insurance plan, and then stores the encrypted insurance plan on the blockchain through a smart contract. In this process, the distributed characteristics of the blockchain are used to distribute the information of the insurance plan on multiple nodes. In addition, the system uses a hash function to generate a unique identifier for the plan and associates the identifier with the smart contract address to facilitate subsequent retrieval and verification. Once the insurance plan is uploaded to the blockchain, the smart contract will be responsible for automatically executing the conditions specified in the contract. When an insurance accident occurs to the insured, the smart contract can automatically trigger the claims process according to the terms of the contract. The system has pre-set logic embedded in the smart contract, and automatically determines whether the claims conditions are met based on real-time data input and status changes. This automatic execution feature not only improves operational efficiency, but also reduces risks and human errors during operations.
[0023] Embodiment 2: Embodiment 2 of the present invention provides an intelligent insurance solution system based on big data and blockchain. Figure 2 A schematic diagram of the module composition of a smart insurance solution system based on big data and blockchain provided in Embodiment 2 of the present invention is shown in FIG. Figure 2 As shown, the system includes: The information collection module is used to obtain the basic financial and medical information of the insured, and interact with major banks, medical institutions, and social media platforms by establishing a secure data interface to ensure comprehensive collection of relevant data; The risk assessment module is used to assess the individual health risks of the insured through machine learning algorithms and big data analysis, and to conduct overall family risk assessment based on the relationship model, providing a comprehensive and accurate risk level; Personalized insurance solution module, which combines individual risk assessment and policyholder needs to provide each policyholder with a personalized and most suitable insurance solution through collaborative filtering, family association rule application and mathematical optimization model; In some embodiments of the present invention, the information collection module includes: The data source interface establishment module is used to establish a secure data interface with major banks, medical institutions, and social media platforms to ensure the acquisition of relevant information about the policyholder; Financial information collection module, which uses financial institution APIs to obtain key financial indicators such as the insured’s income, expenses, assets and debts in real time; The medical information integration module is used to extract the insured's medical data, including medical records, examination reports, medication records, etc., from the electronic health record system of medical institutions through data fusion technology; In some embodiments of the present invention, the risk assessment module includes: The data preprocessing and feature extraction module is used to preprocess the collected data, including removing duplicate records, processing missing values and outliers, and extracting numerical features; A machine learning model training module, which is used to classify individual health risks by comprehensively processing financial, medical and social media information through random forest learning models; The family overall risk assessment module is used to establish a family relationship model based on individual risk assessment and conduct overall family risk assessment in terms of health and finance; In some embodiments of the present invention, the personalized insurance solution module includes: The collaborative filtering recommendation module is used to combine individual risk assessment and the actual needs of the policyholder, and use the collaborative filtering recommendation algorithm to customize the most suitable insurance plan for each policyholder; The family association rule application module is used to recommend corresponding family insurance products based on the association analysis results to ensure that the entire family is fully protected when facing risks; The mathematical optimization model module is used to use mathematical optimization models to consider factors such as cost, coverage, deductibles, etc. of different insurance products to maximize the satisfaction of policyholders while keeping the overall premium within a reasonable range.
[0024] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0025] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A smart insurance solution based on big data and blockchain, characterized by: The method comprises the following steps: Step 1: Integrate financial and medical information by collecting basic information of the insured and his / her family members; Step 2: Implement individual and family overall risk assessment through machine learning algorithms and big data analysis; Step 3: Based on the results of risk analysis, the system recommends personalized insurance plans; Step 4: Upload the customized insurance plan to the blockchain in the form of a smart contract for storage.
2. According to claim 1, a smart insurance solution based on big data and blockchain is characterized by: The steps of integrating financial and medical information by collecting basic information of the insured and his / her family members include: The system acquires the information data of the insured by establishing data interfaces with banks, medical institutions and social media platforms; Obtain the insured's financial data through the insured's bank record data, electronic payment information data, and tax data. The financial data includes the use of API integration with financial institutions to obtain the insured's income, expenditure, assets, and debt information in real time; Extract the basic medical information of the insured by using data fusion, that is, extract the medical data of the insured including medical record data, examination report data, and medication record data through the information system integration terminal of the medical institution; Social media data integration is further used to obtain the public information data of the insured on the social media platform, including data related to social circles and interests and hobbies.
3. According to claim 2, a smart insurance solution based on big data and blockchain is characterized in that: The steps of implementing the overall risk assessment of individuals and families through machine learning algorithms and big data analysis include: Use machine learning algorithms and big data analytics to assess the health and financial risks of policyholders; Based on the individual risk assessment, an overall family risk assessment is conducted, taking into account collaborative risks and family relationships.
4. According to claim 3, a smart insurance solution based on big data and blockchain is characterized in that: The steps of using machine learning algorithms and big data analysis to assess the health and financial risks of policyholders include: The system preprocesses the collected data, including removing duplicate records, processing missing values and outliers. Then the system collects data from bank records, medical records and social media information. Bank records include family members' income and expenditure, medical records include medical history and medication information, and social media information includes interests and hobbies. Then the system extracts numerical features from bank records, such as the mean and standard deviation of income and expenditure, extracts information about chronic diseases and past medical frequency from medical records, and extracts specific keywords from social media information to measure family members' lifestyles and interests. Finally, a random forest learning model is selected to process the diverse data. A random forest containing 10 trees was selected in the random forest, and the maximum depth of each tree was 5. Each time a decision tree was trained, the system randomly selected 70% of the family member data for training, and at each node, the system randomly selected 80% of the features for node splitting. For each member, each tree was trained on a randomly selected subset, and the tree structure was established by maximizing information gain. The 10 trees were integrated into a random forest. For the classification problem of health risks, the final prediction was determined by majority voting, and the validation set was used to evaluate the model performance to obtain accuracy and recall indicators. Finally, the test set was used to verify the performance of the model on new family member data.
5. According to claim 4, a smart insurance solution based on big data and blockchain is characterized in that: Based on the individual risk assessment, a family risk assessment is conducted to consider the collaborative risks and family relationships, including: Based on individual risk assessment, the system conducts an overall family risk assessment and calculates the overall risk level of the family in terms of health and finance. In the overall family risk assessment stage, the system first establishes a relationship model between family members. Through big data analysis, the system collects and integrates financial information and medical history data to establish a comprehensive family relationship network. Through this relationship model, the system can understand the collaborative risks between family members, such as the impact of a member's health problems on the living standards of other members. At the same time, in the correlation analysis stage, the system deeply explores the correlation between family members through big data analysis, including financial dependence and the mutual influence of health conditions. In the comprehensive risk assessment process, the system first sets weights for different risk types to reflect the family's relative preferences for health and finance. The weights are set through risk preference surveys of family members or historical data analysis.
6. The smart insurance solution based on big data and blockchain according to claim 5 is characterized by: The steps of implementing personalized insurance plan recommendation by the system based on the risk analysis results include: The system uses collaborative filtering to customize insurance plans for policyholders based on their personal information, risk preferences, and previously purchased insurance product data. Furthermore, the system recommends corresponding family insurance products based on the policyholder's family status and association rules between family members. When association analysis shows that the health status of a family member has an impact on other members, the system 7. The smart insurance solution based on big data and blockchain according to claim 6 is characterized by: The step of uploading the customized insurance plan to the blockchain in the form of a smart contract for storage includes: The system first uses an encryption algorithm to encrypt the insurance plan, and then stores the encrypted insurance plan on the blockchain through a smart contract. In this process, the distributed characteristics of the blockchain are used to store the information of the insurance plan in a distributed manner on multiple nodes. In addition, the system uses a hash function to generate a unique identifier for the plan and associates the identifier with the smart contract address to facilitate subsequent retrieval and verification. Once the insurance plan is uploaded to the blockchain, the smart contract will be responsible for automatically executing the conditions specified in the contract. When an insurance accident occurs to the insured, the smart contract can automatically trigger the claims process according to the terms of the contract. The system has pre-set logic embedded in the smart contract, and automatically determines whether the claims conditions are met based on real-time data input and status changes.
8. An intelligent insurance solution system based on big data and blockchain, characterized by: The system comprises: The information collection module is used to obtain the basic financial and medical information of the insured, and interact with major banks, medical institutions, and social media platforms by establishing a secure data interface to ensure comprehensive collection of relevant data; The risk assessment module is used to assess the individual health risks of the insured through machine learning algorithms and big data analysis, and to conduct overall family risk assessment based on the relationship model, providing a comprehensive and accurate risk level; The personalized insurance plan module is used to combine individual risk assessment and policyholder needs to provide each policyholder with a personalized and most suitable insurance plan through collaborative filtering, family association rule application and mathematical optimization model.
9. The smart insurance solution system based on big data and blockchain according to claim 8 is characterized by: The information collection module includes: The data source interface establishment module is used to establish a secure data interface with major banks, medical institutions, and social media platforms to ensure the acquisition of relevant information about the policyholder; Financial information collection module, which uses financial institution APIs to obtain the policyholder's income, expenditure, assets and debt information in real time; The medical information integration module is used to extract the insured person's medical data, including medical records, examination reports, and medication records, from the electronic health record system of medical institutions through data fusion technology.
10. The smart insurance solution system based on big data and blockchain according to claim 9 is characterized by: The risk assessment module includes: The data preprocessing and feature extraction module is used to preprocess the collected data, including removing duplicate records, processing missing values and outliers, and extracting numerical features; A machine learning model training module, which is used to classify individual health risks by comprehensively processing financial, medical and social media information through random forest learning models; The family overall risk assessment module is used to establish a family relationship model based on individual risk assessment and conduct overall family risk assessment in terms of health and finance.