A policy custody method and system

Abstract

A policy custody method and system, relating to the technical field of data processing. The method includes: obtaining policy data of multiple customers; identifying the policy data of each customer to obtain multiple customer groups, and extracting the household policy data of each customer group; constructing a household policy relationship graph; in the household policy relationship graph, combining the household information of each first-level node, the member information of each second-level node, and the policy information of each third-level node, calculating the household insurance coverage rate of each customer group; identifying the duplicate protection items and protection gap items of each customer group, and in a pre-established insurance product library, matching the insurance products for the duplicate protection items and protection gap items of each customer group; calculating the improvement amplitude of each insurance product on the household insurance coverage rate, and if the coverage rate improvement amplitude is greater than the amplitude threshold, sending the insurance product to the customer group. Implementing the technical solution provided by this application achieves the effect of improving the efficiency of policy custody.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly relates to a policy custody method, system, electronic device and storage medium. Background Art

[0002] With the rapid development of the insurance market and the continuous improvement of people's insurance awareness, policy custody services have gradually become an important part of the insurance industry. The policy custody service aims to help customers better manage and optimize their insurance protection, improve the utilization efficiency of insurance resources, and at the same time provide opportunities for insurance companies to offer value-added services.

[0003] Currently, existing policy custody methods mainly collect customers' personal information and policy data through insurance brokers, analyze the protection content of each policy one by one, and then provide policy optimization suggestions based on customers' personal needs and risk status for the recommendation of insurance products.

[0004] However, in actual applications, existing policy custody methods mostly focus on the analysis of individual customers. When it comes to the analysis of family policies, the workload increases significantly. Insurance brokers often need to spend a lot of time and it is difficult to quickly analyze the policy data of a large number of customers to recommend insurance products to customers, resulting in low efficiency of policy custody. Summary of the Invention

[0005] The present application provides a policy custody method, system, electronic device and storage medium, which has the effect of improving the efficiency of policy custody.

[0006] In a first aspect, the present application provides a policy custody method, including:

[0007] Obtain policy data of multiple customers;

[0008] Identify each of the policy data to obtain multiple customer groups with the same family relationship, and extract the family policy data of each of the customer groups;

[0009] Based on each of the family policy data, construct a family policy relationship graph, where the family policy relationship graph includes multiple first-level nodes representing family units, multiple second-level nodes representing family members, and multiple third-level nodes representing policies;

[0010] In the family policy relationship graph, combine the family information corresponding to each of the first-level nodes, the member information corresponding to each of the second-level nodes, and the policy information corresponding to each of the third-level nodes, and calculate the current family insurance coverage rate of each of the customer groups;

[0011] Based on the family insurance policy relationship graph, identify the duplicate protection items and protection gap items of each customer group, and in the pre-established insurance product library, match the insurance products corresponding to the duplicate protection items and protection gap items of each customer group; calculate the improvement rate of each insurance product on the family insurance coverage rate. For each insurance product, if the coverage rate improvement rate of the insurance product is greater than the rate threshold, send the insurance product to the corresponding customer group.

[0012] In the second aspect of the present application, a policy custody system is provided. The system includes:

[0013] A data acquisition module, configured to acquire the policy data of multiple customers; identify each piece of the policy data to obtain multiple customer groups with the same family relationship, and extract the family policy data of each customer group;

[0014] A relationship graph determination module, configured to construct a family insurance policy relationship graph based on each piece of the family policy data. The family insurance policy relationship graph includes multiple first-level nodes representing family units, multiple second-level nodes representing family members, and multiple third-level nodes representing insurance policies;

[0015] A coverage rate determination module, configured to calculate the current family insurance coverage rate of each customer group in the family insurance policy relationship graph by combining the family information corresponding to each first-level node, the member information corresponding to each second-level node, and the policy information corresponding to each third-level node;

[0016] An insurance product push module, configured to identify the duplicate protection items and protection gap items of each customer group according to the family insurance policy relationship graph, and in the pre-established insurance product library, match the insurance products corresponding to the duplicate protection items and protection gap items of each customer group; calculate the improvement rate of each insurance product on the family insurance coverage rate. For each insurance product, if the coverage rate improvement rate of the insurance product is greater than the rate threshold, send the insurance product to the corresponding customer group.

[0017] In the third aspect of the present application, an electronic device is provided, including a memory, a processor, and a program stored on the memory and executable on the processor. When the program is loaded and executed by the processor, a policy custody method can be implemented.

[0018] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a policy custody method.

[0019] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] By adopting the above technical solutions, the policy data of multiple customers is centrally obtained and identified, customer groups with the same family relationship are accurately divided, and a multi-level family policy relationship graph is constructed based on the family policy data. Combining family information, member information, and policy information, the family insurance coverage rate is systematically calculated, duplicate guarantees and guarantee gaps are automatically identified, and optimized products are intelligently matched in a preset insurance product library. Finally, by evaluating the improvement amplitude of the insurance product on the coverage rate, the most suitable insurance product is accurately recommended, the family insurance portfolio is optimized, and resource waste and insufficient guarantees are reduced. Compared with the traditional insurance brokers who need to spend a lot of time analyzing policy data one by one, the processing time is shortened, thus improving the efficiency of policy trusteeship. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flowchart of a policy trusteeship method provided by an embodiment of the present application;

[0022] Figure 2 is a schematic structural diagram of a policy trusteeship system provided by an embodiment of the present application;

[0023] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0024] Description of the reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0026] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0027] In the description of the embodiments of the present application, the term "plural" means two or more. For example, plural systems refer to two or more systems, and plural screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] The embodiments of the present application provide a policy custody method. In one embodiment, please refer to Figure 1 , Figure 1 which is a schematic flow chart of the policy custody method provided by the embodiments of the present application. This method can be implemented depending on a computer program, which can be integrated in an application or run as an independent tool-class application. This method can also be implemented depending on a single-chip microcomputer and can run on a policy custody system based on the von Neumann architecture. Specifically, this method may include the following steps:

[0029] Step 101: Obtain the policy data of multiple customers; identify each policy data to obtain multiple customer groups with the same family relationship, and extract the family policy data of each customer group.

[0030] Among them, the policy data refers to the detailed information related to the insurance products purchased by customers. In the embodiments of the present application, it can be understood as all data information related to a specific policy, such as the policyholder information, the insured information, the insurance company information, the insurance period, the insurance amount, the insurance premium, etc., which is used to describe the detailed situation of the insurance products already purchased by customers.

[0031] The customer group refers to the result of grouping multiple customers through family relationships. In the embodiments of the present application, it can be understood as the classification of multiple members with kinship within the family that purchases insurance products and their corresponding policy data, which is used for subsequent extraction of family policy data and analysis of family insurance needs.

[0032] The family policy data refers to the set of policy information of all customers belonging to the same family. In the embodiments of the present application, it can be understood as the result of summarizing the policy data of all customers belonging to the same customer group, which is used to describe the overall insurance purchase situation and protection status of a family.

[0033] Specifically, to implement the automated custody service for household insurance policies, it is necessary to obtain the insurance policy data of multiple customers. These insurance policy data can be sourced from the business system database of the insurance company or collected by insurance agents through web forms or other means for customers to upload their insurance policy data. After obtaining the customers' insurance policy data, it is necessary to identify the insurance policy data, mainly identifying the applicant information and the insured information in the insurance policy data. This identification can be achieved through text extraction from the insurance policy data, extracting the applicant's name, ID information, the insured's name, ID information, etc. Then, based on the extracted applicant information and insured information, it is necessary to determine whether there is a family relationship between different insurance policies. Specifically, a customer relationship network can be established, with the applicant and the insured as nodes. If the insured of the applicant is not the applicant himself / herself, a connection representing a family relationship is established between the two nodes. Nodes with the same applicant or insured are merged. Finally, the area in the relationship network where multiple connected nodes appear corresponds to a family. After partitioning according to the family relationship, all the insurance policy data belonging to the same family can be extracted to form household insurance policy data. In this way, the original customers' insurance policy data can be divided into multiple household insurance policy data sets, laying a foundation for subsequent household insurance policy analysis and processing.

[0034] Based on the above embodiments, as an alternative embodiment, in step 101: identifying each insurance policy data to obtain multiple customer groups with the same family relationship and extracting the household insurance policy data of each customer group. This step may further include the following steps:

[0035] Step 201: Extract the applicant information and the insured information from each insurance policy data and construct a customer relationship network based on the applicant information and the insured information.

[0036] Among them, the applicant information and the insured information refer to the relevant personal information used to describe the applicant and the insured in the insurance policy data. In the embodiments of the present application, it can be understood as information such as the names, ID numbers, and contact information of the applicant and the insured that can uniquely identify a person, used to determine whether there is a family relationship between the applicants and the insured of different insurance policies and ultimately identify the insurance policies belonging to the same family.

[0037] The customer relationship network refers to a network diagram constructed through the association relationship between the applicant information and the insured information. In the embodiments of the present application, it can be understood as a network structure that takes each applicant and insured in the insurance policy data as nodes and establishes a connection relationship between the nodes according to their family relationship, used to represent the family relationship between different customers and identify family customer groups in the network.

[0038] Specifically, after obtaining the applicant and insured information of each insurance policy data, a customer relationship network needs to be established based on this information. Each independent applicant and insured can be set as a node. If the insured of an applicant is not the applicant himself / herself, a connection line is established between the corresponding two nodes, indicating that there is a family relationship between the two. For the same applicant or insured who appears in multiple insurance policy data, the corresponding multiple nodes need to be merged into one node. Finally, the multiple interconnected nodes condensed in the relationship network diagram are a family or kinship circle, and all the applicants and insureds inside have family relationships. In this way, the family relationships in the insurance policy data can be identified, laying a foundation for the extraction and analysis of family insurance policies in the next step.

[0039] Based on the above embodiments, as an optional embodiment, in step 201: Based on the applicant information and the insured information, a customer relationship network is constructed. This step may further include the following steps:

[0040] Step 211: Take the applicant corresponding to each applicant information and the insured corresponding to each insured information as a node in the customer relationship network.

[0041] Specifically, the first step in constructing the customer relationship network is to take each independent applicant and insured in the insurance policy data as a node in the network diagram, because the applicant and the insured are the two most critical information points for judging family relationships. Each applicant and each insured need to be added to the network diagram as independent individuals in order to judge the family relationships between these nodes through the subsequent node connection relationships. By traversing each piece of applicant information extracted, a corresponding node is created for each unique applicant; at the same time, by traversing each piece of insured information, a corresponding node is also created for each unique insured. Finally, all the nodes in the network diagram represent all the unique applicants and insured individuals involved in these insurance policy data, laying a foundation for subsequent judgment of the family relationship connections between nodes to identify family customer groups.

[0042] Step 221: Based on preset rules, establish the connection relationships between the nodes to obtain the customer relationship network; wherein, the preset rules are: If the insured of an applicant is not the applicant, establish a connection relationship between the nodes corresponding to the applicant and the insured; if there are multiple insurance policy data corresponding to the same applicant or insured, merge the multiple nodes corresponding to the same applicant or insured.

[0043] Specifically, when all policyholders and insured nodes are ready, it is necessary to determine the connection relationships among these nodes based on a preset family relationship judgment rule, and finally form a customer relationship network. The purpose of doing this is to determine whether there is a family relationship between two specific nodes based on the existence or non-existence of the connection relationship. Only by constructing a network diagram that correctly reflects the node relationships can it be used as the basis for subsequent identification of family customer groups. The preset family relationship judgment rule is: If the insured of a policyholder is not himself / herself, it can be determined that there is a family relationship between the policyholder and the corresponding insured node, and a connection line needs to be established between these two nodes. If it is found that there are the same policyholders or insureds in multiple policy data, these duplicate nodes need to be merged to form a single node representing this common policyholder / insured. Judging the relationships between each node according to such rules, a customer relationship network covering all policyholders and insureds is finally formed. In this way, the family relationships between each node are accurately reflected in the form of a network diagram, providing a basis for subsequent customer group identification and family policy extraction.

[0044] Step 202: In the customer relationship network, identify multiple customer nodes with kinship; cluster the customer nodes with the same kinship to obtain multiple customer groups.

[0045] Among them, multiple customer nodes with kinship refer to the network nodes corresponding to multiple customers associated through family relationships in the customer relationship network. In the embodiments of the present application, it can be understood as the corresponding nodes in the network diagram of multiple family members who have purchased insurance products belonging to the same family, used to represent multiple customers with family relationships, so as to identify family customer groups subsequently.

[0046] Specifically, after constructing the relationship network representing the customer family relationships, it is necessary to identify the customer nodes with kinship in the network and cluster them to form customer groups. The way to identify customer nodes with kinship is to observe the connections between nodes in the relationship network diagram. Any two customer nodes connected by a connection line indicate that these two customers have a kinship and belong to the same family. Then, cluster all the connected customer nodes in the relationship network diagram using existing community discovery algorithms or clustering algorithms, so that the closely connected customer nodes form different groups, and each group is a family. Finally, among the obtained customer groups, all family members who have purchased insurance products belonging to the same family are included.

[0047] Step 203: Merge the policy data of multiple customers in each customer group to obtain the family policy data of each customer group.

[0048] Specifically, after obtaining the customer groups representing specific families, it is necessary to further integrate all the customer policy data within each customer group to form family policy data. The reason for forming family policy data is that subsequent policy analysis and insurance demand assessment need to take the family rather than the individual as the basic unit. Looking only at the policies of a single family member is not enough; it is necessary to comprehensively understand the current overall protection situation of this family. The specific way to form family policy data is to traverse each identified customer group, extract the policy data of all customers within the group, and integrate them, including summarizing information such as premium, protection amount, and product type. Eventually, each customer group corresponds to a family policy data representing the entire family's insurance situation.

[0049] Step 102: Based on each family policy data, construct a family policy relationship graph. The family policy relationship graph includes multiple first-level nodes representing family units, multiple second-level nodes representing family members, and multiple third-level nodes representing policies.

[0050] Among them, the family policy relationship graph refers to a relationship graph that uses the form of a network diagram to display the policy relationships and protection structures within a family. In the embodiments of the present application, it can be understood as a multi-level network diagram including family nodes, family member nodes, policy nodes, and the relationships between nodes, which is used to intuitively display the protection status of each family and support subsequent policy analysis and optimization.

[0051] The first-level node refers to the node in the family policy relationship graph that represents the entire family entity. In the embodiments of the present application, it can be understood as an abstract node that identifies each unique family and is used to distinguish different families in the graph.

[0052] The second-level node refers to the node in the family policy relationship graph that represents family members. In the embodiments of the present application, it can be understood as a node that represents each specific member belonging to a certain family and is used to identify different members within the family in the graph.

[0053] The third-level node refers to the node in the family policy relationship graph that represents policies. In the embodiments of the present application, it can be understood as a node that represents each policy purchased by a family member and is used to identify the specific policies of family members within the family in the graph.

[0054] Specifically, information extraction is performed on the household insurance policy data to obtain household information, family member information, and insurance policy information. For each extracted household information, a corresponding household node is created in the graph. For each member information, a member sub-node is created under its corresponding household node. For each insurance policy information, an insurance policy sub-node is created under its corresponding member node. This step is repeated until all household insurance policy data is modeled into the relationship graph. Eventually, a multi-level network graph containing household nodes, member nodes, insurance policy nodes, and their relationships is formed, visually showing the protection structure of all households. This provides data support for subsequent protection analysis and insurance policy optimization. Through the visual insurance policy relationship graph, the protection status of households can be more comprehensively understood, and precise insurance policy trusteeship services can be realized for each household.

[0055] Based on the above embodiments, as an alternative embodiment, in step 102: constructing a household insurance policy relationship graph based on each household insurance policy data. This step may further include the following steps:

[0056] Step 301: Perform text recognition on each household insurance policy data to extract the household information, member information, and insurance policy information in each household insurance policy data.

[0057] Among them, the household information, member information, and insurance policy information refer to three types of node information required for constructing the household insurance policy relationship graph. In the embodiments of the present application, it can be understood that household information: information describing the basic situation of the household, such as household name, household address, etc. Member information: information describing family members, such as member name, contact information, etc. Insurance policy information: information describing the specific situation of the insurance policy, such as policy number, premium, protection items, etc. The three are respectively used to create household nodes, member nodes, and insurance policy nodes in the graph. The household information reflects an independent household entity, the member information reflects the specific members within this household, and the insurance policy information reflects the insurance policies purchased by this member.

[0058] Specifically, after obtaining the family policy data, text recognition technology is needed to extract family information, member information, and policy information from it. This is because the family policy data contains a large amount of unstructured text information, and the structured information required for constructing the policy graph needs to be identified from it. By directly extracting these three types of information from the text, node information can be obtained automatically without manually checking the policy text and entering information one by one. Existing NLP, rule matching, etc. can be used to identify the sentences describing the family in the policy text as family information, the sentences describing the purchaser or the insured as member information, and the sentences describing the policy details as policy information. Then necessary processing can be carried out, such as removing irrelevant content and standardizing names. Finally, structured family information, member information, and policy information are obtained. Through text recognition technology, structured node information can be quickly extracted from a large amount of unstructured policy data, greatly improving the information acquisition efficiency and providing a structured data source for constructing the policy relationship graph.

[0059] Step 302: Create first-level nodes corresponding to each family information, where each first-level node corresponds to a family unit, create second-level nodes corresponding to each member information, where each second-level node corresponds to a family member, and create third-level nodes corresponding to each policy information, where each third-level node corresponds to a policy.

[0060] Specifically, after obtaining the family information, member information, and policy information, three-level nodes corresponding to these three types of information need to be created in the graph. This is to establish a three-level node system of family, member, and policy in the graph to accurately reflect the information entities in the family policy relationship. Traverse each piece of extracted family information and create a new first-level family node with this family information, so that each first-level node corresponds to a unique family. Similarly, traverse each piece of member information and create a new second-level member node under the first-level node of the corresponding family, so that each second-level node corresponds to a family member. Finally, traverse each piece of policy information and create a new third-level policy node under the second-level node of the corresponding member, so that each third-level node corresponds to a policy. After this processing, a three-level node system representing different information entities is established in the graph, laying a foundation for connecting node relationships in the next step.

[0061] Step 303: Associate each first-level node with the corresponding second-level node, and associate the second-level node with the corresponding third-level node to generate a family policy relationship graph.

[0062] Specifically, after the three-level nodes are prepared, it is necessary to further establish the relationships between the nodes, and finally generate a family insurance policy relationship graph. This is done to connect the logical relationships between different information entities in the graph, forming a networked knowledge graph that can comprehensively reflect the internal relationships of family insurance policies. First, traverse each first-level family node, find all the second-level member nodes corresponding to it, that is, belonging to this family, and connect the two through an inclusion relationship. Then, traverse each second-level member node, find all the third-level insurance policy nodes corresponding to it, that is, the insurance policies purchased by this member, and connect the two through a purchase relationship. After this processing, the relationships between information entities of different granularities are established in the graph, forming a network topology structure from abstract to concrete, that is, the final family insurance policy relationship graph.

[0063] Step 103: In the family insurance policy relationship graph, combine the family information corresponding to each first-level node, the member information corresponding to each second-level node, and the insurance policy information corresponding to each third-level node, and calculate the current family insurance coverage rate of each customer group.

[0064] Among them, the family insurance coverage rate is an important indicator for evaluating the current protection status of a family. In the embodiments of the present application, it can be understood as the ratio of the actual insurance premium purchased by a family to the recommended premium expenditure level, which is used to judge whether there is a protection gap in the family and the size of the gap, providing a basis for subsequent insurance policy optimization.

[0065] Specifically, after constructing the family insurance policy relationship graph, it is necessary to calculate the current insurance coverage rate of each family based on the graph and analyze the protection level of the family. This is because the insurance coverage rate is an important indicator for evaluating the protection status of a family. Calculating the coverage rate of each family can judge whether there is a protection gap in the family and the size of the gap, providing a basis for subsequent accurate recommendations. The calculation method is as follows: For each first-level family node in the graph, count the number of third-level insurance policy nodes it contains and calculate the total premium; at the same time, refer to the corresponding family information and member information to calculate the recommended premium expenditure level of this family; finally, the ratio of the actual premium of the third-level insurance policy node to the recommended premium is the insurance coverage rate of this family. After calculation, the current insurance coverage level of each family can be obtained. This can identify families with protection gaps among high-net-worth customers and also discover situations of over-purchasing insurance, providing a basis for subsequent insurance policy optimization. Calculating the insurance coverage rate realizes a more intelligent family protection assessment, which helps to customize protection plans for each family subsequently.

[0066] Based on the above embodiments, as an alternative embodiment, in step 103: In the family policy relationship graph, combining the family information corresponding to each first-level node, the member information corresponding to each second-level node, and the policy information corresponding to each third-level node, calculate the current family insurance coverage rate of each customer group. This step may further include the following steps:

[0067] Step 401: Traverse each family information to determine the total annual income and total family assets of the family units corresponding to multiple customer groups, traverse each member information to determine the ages and occupations of multiple family members in each family unit, and traverse each policy information to determine the total sum insured, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies for each family unit.

[0068] Among them, the total annual income and total family assets refer to two key indicators for evaluating the family's financial situation. In the embodiments of the present application, it can be understood as: the total annual income: the sum of all pre-tax incomes of a family in one year, and the total family assets: the total value of all assets owned by a family.

[0069] The total sum insured, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies refer to three key indicators for evaluating the optimization degree of the family policy configuration. In the embodiments of the present application, it can be understood as: the total sum insured is the sum of the insured amounts of the existing policies of a family. The average number of risk types covered by existing policies is the average number of life cycle risk categories covered by the existing policies of multiple family members in each family unit. The average number of age stages covered by existing policies is the average number required for different age groups covered by the existing policies of multiple family members in each family unit. The three comprehensively evaluate whether the configuration of the existing policies is optimized and perfect, and are used to compare with the optimized policies to judge the effect of policy optimization.

[0070] Specifically, after constructing the family insurance policy relationship graph, it is necessary to traverse each first-level family node in the graph to obtain information such as the total family income and total asset value to evaluate the financial status of each family; traverse each second-level member node to obtain information such as member age and occupation to judge the protection needs; traverse each third-level insurance policy node to obtain information such as the total insurance policy amount and the number of covered risk types to evaluate the current protection configuration. Doing so is to extract all the characteristic information required for judging protection optimization from the graph and conduct precise protection analysis for each family. Count the attributes of each family node to obtain family income and asset information; count the attributes of each member node to obtain age and occupation information; count the attributes of each insurance policy node to obtain information such as the total insurance policy amount and the number of covered risk types. After the above traversal and extraction, the financial information, population information, and protection configuration information of each family can be accurately obtained from the relationship graph, and the protection status of each family can be comprehensively evaluated.

[0071] Step 402: According to the preset risk coefficient table, determine the personal risk coefficients corresponding to the ages and occupations of multiple family members in each family unit.

[0072] Among them, the preset risk coefficient table refers to a coefficient table generated in advance based on statistical data. In the embodiments of the present application, it can be understood as a look-up table containing risk coefficients corresponding to different age groups and different occupations, which is used to find the matching risk coefficients after determining the ages and occupations of each family member and evaluate the insurance risk level faced by this member.

[0073] The personal risk coefficient refers to a quantitative indicator for evaluating the degree of insurance risk faced by a family member. In the embodiments of the present application, it can be understood as the risk coefficient corresponding to a certain age group and occupation determined in advance based on statistical analysis, which is used to represent the current risk level of this member and provide a basis for determining its reasonable protection goal in the future.

[0074] Specifically, after obtaining the age and occupation information of each family member, it is necessary to determine the corresponding risk coefficient for each member according to a preset risk coefficient table. This is because the insurance risk coefficients corresponding to different age stages and occupations are also different, and it is necessary to obtain the standard risk coefficients by looking up the table to provide basic data for subsequent calculation of personalized protection. A statistically obtained risk coefficient table is preset, which contains the risk coefficients corresponding to different age groups and different occupations. Traverse all member nodes in the family insurance policy graph, find the age attribute and occupation attribute of each member, and find and record the matching risk coefficient in the coefficient table. Eventually, each member node will correspond to a risk coefficient, reflecting the risk level of the member, providing an important basis for calculating the personalized protection goal. By looking up the table to obtain the standardized risk coefficients, an objective risk assessment basis can be provided for the protection calculation of each family member, making the protection optimization more accurate.

[0075] Step 403: Substitute the total annual income and total family assets of each family unit, the personal risk coefficients corresponding to multiple family members in each family unit, the total amount of each family unit, the average number of risk types covered by the existing insurance policies, and the average number of age stages covered by the existing insurance policies into a preset formula to obtain the current family insurance coverage rate of each customer group; where the preset formula is:

[0076]

[0077] In the formula, P i represents the current family insurance coverage rate of the i-th customer group, ω1 represents the preset first weight coefficient, D i represents the average number of risk types covered by the existing insurance policies of the i-th family unit, D0 represents the benchmark number of risk types, ω2 represents the preset second weight coefficient, N i represents the average number of age stages covered by the existing insurance policies of the i-th family unit, N0 represents the benchmark number of age stages, ω3 represents the preset third weight coefficient, C i represents the total insurance amount of the i-th family unit, R i,j represents the personal risk coefficient corresponding to the j-th family member in the i-th family unit, M i represents the number of family members in the i-th family unit, A i represents the total annual income of the i-th family unit, T represents the preset insurance policy term, B i represents the total family assets of the i-th family unit.

[0078] Among them, the preset formula refers to a mathematical formula used to calculate the current household insurance coverage rate of each customer group. In the embodiments of the present application, the preset formula can be understood as a multivariate analysis formula that includes the total annual household income and total household assets of each household unit, the individual risk coefficients corresponding to multiple family members in each household unit, as well as the total package amount of each household unit, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies. The preset formula is used for quantitative calculation and evaluation of the household insurance coverage rate. By substituting actual detection data, the current household insurance coverage rate of each customer group can be calculated.

[0079] Specifically, after obtaining the household financial information, member risk coefficients, and current protection configuration indicators, it is necessary to substitute them into the preset formula to calculate the insurance coverage rate of each household. This is to use the preset calculation model to evaluate and judge the protection status of each household, and obtain accurate and objective insurance coverage rate results. Substitute the total household income, total household assets, member risk coefficients, policy total package amount, number of covered risk categories, etc. extracted from each household into the formula, and after calculation, the coverage rate value of the household can be obtained. The formula comprehensively considers factors such as income, assets, risks, and current protection, calculates a coverage rate result with reference significance, and after obtaining the coverage rate, it can be judged which households have protection gaps and which households have excessive protection, providing a basis for optimizing the subsequent policy configuration.

[0080] The formula consists of three parts. The first part describes the degree of influence of the average number of risk types covered by existing policies on the current household insurance coverage rate of the customer group. Among them, represents the ratio of the average number of risk types covered by existing policies to the number of benchmark risk types. This ratio reflects the coverage breadth of the household policy in different risk types. D i represents the average number of risk types covered by the household's current policy. For example, fire, theft, natural disasters, etc. D0 is the set number of benchmark risk types for comparison and standardization. The more risk types covered by the household policy, the higher the corresponding ratio, and the greater the contribution to the insurance coverage rate. This part of the indicator helps to evaluate the comprehensiveness and effectiveness of the policy.

[0081] Exemplarily, assume that the number of benchmark risk types D0 is 5, indicating that a general policy usually covers 5 risk types. Household A: Its policy covers 3 risk types (fire, theft, flood), that is, D i is 3, is 0.6. Household B: Its policy covers 6 risk types (fire, theft, flood, earthquake, lightning, storm), that is, D i is 6, then is 1.2. The ratio of Family A is 0.6, lower than the benchmark, meaning it covers fewer risk types with a lower coverage rate, while the ratio of Family B is 1.2, higher than the benchmark, indicating it covers more risk types with a higher coverage rate.

[0082] The second part describes the degree of influence of the average number of age stages covered by existing policies on the current family insurance coverage rate of the customer group. Represents the ratio of the average number of age stages covered by existing policies to the benchmark number of age stages. This part reflects the coverage range of policies for different age stages. N i Represents the average number of age stages covered by the family's current policy. For example, there are four age stages: children, youth, middle age, and old age. N0 represents the set benchmark number of age stages for standardized comparison. The more age stages covered by the policy, the higher the ratio and the greater the contribution to the insurance coverage rate. This part of the indicator helps evaluate the comprehensiveness and adaptability of the policy.

[0083] Exemplarily, assume the benchmark number of age stages N0 is 4, indicating that usually a policy covers 4 age stages, such as children, youth, middle age, and old age. Family A: Its policy covers 3 age stages (children, youth, middle age), that is, N i is 3, then is 0.75. Family B: Its policy covers 2 age stages (youth, middle age), that is, N i is 2, then is 0.5. The ratio of Family A is 0.75, indicating that its policy covers more age stages compared to Family B, which helps improve the insurance coverage rate. The ratio of Family B is 0.5, indicating that compared to Family A, its policy covers fewer age stages and has a smaller impact on the coverage rate.

[0084] The second part describes the comprehensive degree of influence of the total annual household income, total household assets of each household unit, the individual risk coefficients corresponding to multiple family members in each household unit, and the total package amount of each household unit on the current family insurance coverage rate of the customer group. Represents the comprehensive consideration of economy and risk in the family insurance coverage rate. C i Represents the total household insurance amount, that is, the investment amount of insurance, which determines the family's investment in insurance. A higher insurance amount usually indicates better protection, that is, a higher family insurance coverage rate. Conversely, the lower the total insurance amount, the smaller the family insurance coverage rate. ∑R i,j Represents the sum of the individual risk coefficients of family members. The risk coefficient is considered based on factors such as health and occupation. M i Represents the number of family members, which is used to calculate the average risk coefficient. is the average risk coefficient of each family member. The higher the average risk coefficient, the greater the adjusted insurance amount, indicating that the family needs more protection. A i represents the total annual income of the family, indicating the economic capacity. T represents the policy term, indicating the duration of the insurance. B i represents the total value of the family assets, representing the financial stability. This ratio comprehensively considers the insurance amount, risk, and economic foundation. A high ratio indicates that the family still has strong economic affordability under high risks, that is, it reflects a larger family insurance coverage rate. On the contrary, if the ratio of this part is smaller, it means that the family's risk-bearing ability is smaller, that is, it reflects a smaller family insurance coverage rate.

[0085] Exemplarily, assume that the total insurance amount C of family A i is 1,000,000, the sum of risk coefficients ∑R i,j is 10, the number of family members M i is 4, the total annual income of the family A i is 50,000, the total value of the family assets B i is 200,000, and the policy term T is 5. Then the part representing the adjusted insurance amount by risk is 350,000, the part representing the family economic foundation (A i ×T + B i ) is 450,000, then the value of the part is 0.75. The ratio of family A is 0.75, indicating the ratio of the adjusted insurance amount by risk to the family economic foundation. The larger this ratio, the greater the family insurance coverage rate it reflects, and the smaller this ratio, the smaller the family insurance coverage rate it reflects.

[0086] It should be noted that the weight coefficients ω1, ω2 and ω3 in the formula are used to balance the impact of different factors on the total insurance coverage rate. Each coefficient reflects the importance of the factor in the overall calculation, and the sum of the three weight coefficients is 1. The setting of the weight coefficient is determined by collecting a large amount of past policy data for existing regression analysis and other methods, and analyzing the total annual income and total value of family assets of each family unit, the individual risk coefficients corresponding to multiple family members in each family unit, the total amount of each family unit, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies. The degree of change in the family risk coverage rate is determined. For example, an insurance company determined the following weights based on market research and data analysis: ω1 is 0.4, the weight of risk type coverage is high because it directly affects the probability of claims, ω2 is 0.3, the weight of age stage coverage is moderate, considering the differences in needs of different ages, and ω3 is 0.3, the weight of economic factors, which is used to assess the family's ability to pay and financial stability. Among different families, young families may pay more attention to risk type coverage, so the weight of ω1 can be increased, while high-income families may pay more attention to economic factors, so the weight of ω3 can be appropriately increased. In multi-generational families, family members cover multiple age groups and need protection for different ages, so the weight of ω2 can be increased. This can increase the coverage of insurance policies at different age groups and meet the needs of specific family structures.

[0087] Step 104: Based on the family insurance policy relationship map, duplicate protection items and protection gap items of each customer group are identified, and insurance products corresponding to the duplicate protection items and protection gap items of each customer group are matched in the pre-established insurance product library.

[0088] Among them, duplicate protection items refer to the situation where multiple insurance policies within a family provide duplicate protection for the same type of risk and age group. In the embodiment of the present application, it can be understood as the situation where multiple insurance policy nodes corresponding to the same type of life cycle risk and age group appear in the family insurance policy relationship map.

[0089] The protection gap item refers to the situation where the family has insufficient protection at certain life cycle stages or risk types. In the embodiment of the present application, it can be understood as the situation where there is no policy node corresponding to the age group or risk type in the family policy relationship map, which is used to point out the gaps and missing problems in family protection.

[0090] The pre-established insurance product library refers to a database that contains information on various insurance products. In the embodiment of the present application, it can be understood as a structured data set containing detailed information on multiple insurance products. It is used to quickly find matching insurance products as alternatives or supplements after identifying duplicate coverage items and coverage gaps in family insurance policies.

[0091] An insurance product refers to a protection solution designed by an insurance company for a certain type of risk or age group. In the embodiments of this application, it can be understood as a commercial insurance included in a pre-established product library, which is used to protect specific risks or age groups and provide more optimized protection options when duplicate protection items or protection gaps are identified in a household insurance policy.

[0092] Specifically, after calculating the insurance coverage rate of each household, it is necessary to identify the duplicate protection items and protection gap items of each household based on the constructed household insurance policy relationship graph, and find the corresponding supplementary insurance products in the insurance product library. Doing so is to point out the problems existing in each household under the current protection configuration through graph analysis, and can directly match the insurance products required for optimization, providing product support for the subsequent generation of optimization plans. The duplicate purchased protection items can be identified by counting the attributes of each third-level insurance policy node; by analyzing the distribution of insurance policy nodes, the stages and risks with protection gaps can be found. Then, query the insurance products in the product library to supplement these items and risks. Finally, duplicate protection and protection gaps, as well as the corresponding supplementary products, will be marked under each household node, providing a product basis for generating personalized insurance policy combinations. Based on the analysis of the insurance policy relationship graph and product matching, the protection problems of each household can be pointed out efficiently and accurately, and solutions can be given to achieve precise insurance policy optimization for each customer group.

[0093] Based on the above embodiments, as an alternative embodiment, in step 104: According to the household insurance policy relationship graph, identifying the duplicate protection items and protection gap items of each customer group, this step may further include the following steps: Step 501: Traverse the third-level nodes and second-level nodes in the household insurance policy relationship graph to determine multiple protection items in each customer group and the protection amount of each protection item.

[0094] Among them, the protection item refers to the specific content of the insurance policy for protecting a certain type of risk or age group. In the embodiments of this application, it can be understood as a specific protection clause in a certain insurance product corresponding to the third-level node in the household insurance policy relationship graph.

[0095] The protection amount refers to the amount of money paid by the insurance policy when an insurance accident occurs for a certain protection item. In the embodiments of this application, it can be understood as the payment amount stipulated in the insurance product corresponding to the third-level node in the household insurance policy relationship graph, which is used to represent the protection strength and protection level of this protection item.

[0096] Specifically, after matching the insurance products required for optimization, it is necessary to traverse each third-level policy node and second-level member node in the family policy relationship graph to obtain the current protection items and their corresponding protection amounts. This is done to comprehensively extract the existing protection configurations of each family. Specifically, by reading the attributes of each policy node, information such as the protection items, protection period, and protection amount of the policy can be obtained; by reading the attributes of each member node, information such as the age, occupation, and risk status of the member can be learned. Through traversal, the detailed existing protection information of a family can be accurately obtained, including the purchased products and the corresponding protection content, providing information support for generating a new protection plan. This step of obtaining the comprehensive existing protection configuration lays the foundation for formulating the optimal solution based on individual information.

[0097] Step 502: For each customer group, traverse each protection item. If there are overlapping protection items, the overlapping protection items are regarded as the duplicate protection items of the customer group; if there is a protection item with a protection amount less than the protection amount threshold, the protection item is regarded as a protection gap item.

[0098] Specifically, after obtaining the existing protection items and protection amounts of each family, it is necessary to traverse each protection item to determine whether there are duplicate protections and protection gaps. This is done to identify the configuration problems existing in each family at the level of specific protection content, providing guidance for subsequent policy optimization. Specifically, by traversing the protection items of each family, if there are two policies providing protection for the same risk and age group, it is determined as a duplicate protection item; if the protection amount of a certain item is lower than the preset minimum protection amount threshold, it is determined as a protection gap item. Finally, the duplicate protection items and protection gap items of each family can be clarified at the item level, guiding subsequent policy adjustment and optimization. This step of judging the policy configuration status from the level of protection content enables the optimization plan to more precisely solve specific protection problems.

[0099] Step 105: Calculate the improvement amplitude of each insurance product on the family insurance coverage rate. For each insurance product, if the improvement amplitude of the insurance product's coverage rate is greater than the amplitude threshold, the insurance product is sent to the corresponding customer group.

[0100] Among them, the improvement amplitude refers to the growth ratio of the family insurance coverage rate relative to the original coverage rate after adding a certain insurance product. In the embodiment of the present application, it can be understood as the percentage of the difference between the coverage rate after adding a certain candidate product to the family's existing policies and the original policy portfolio coverage rate in the original coverage rate, which is used to evaluate the effect size of the candidate product on improving the family's protection.

[0101] Specifically, after matching the available insurance products, it is necessary to calculate the improvement range of each product on the family insurance coverage rate, and select the product with a larger improvement and recommend it to the corresponding family. This is done to select the product with the greatest improvement in the current protection from the optional products for recommendation, making the policy optimization more accurate and effective. It is possible to simulate adding each candidate product to the existing policy portfolio of the family, recalculate the coverage rate based on the formula, and compare it with the original coverage rate to obtain the improvement value. If the preset improvement range threshold is met, the product is recommended to the family. Eventually, each family will only receive a product recommendation that suits itself and has the greatest improvement in protection, achieving personalized policy optimization. This step selects the optimal solution from the perspective of quantifying the effect, making the policy optimization result more intelligent.

[0102] Based on the above embodiments, as an optional embodiment, in step 105: calculating the improvement range of each insurance product on the family insurance coverage rate, this step may further include the following steps:

[0103] Step 601: Obtain the estimated family insurance coverage rate of each customer group after adjusting the corresponding insurance products.

[0104] Among them, the estimated family insurance coverage rate refers to the new insurance coverage rate level that the family is predicted to have after the policy optimization adjustment. In the embodiments of the present application, it can be understood as the new insurance coverage rate value of the family predicted by using the calculation model after replacing duplicate protection products and adding supplementary products, which is used to evaluate the effect of the optimization plan and show the coverage rate level that can be achieved after the adjustment.

[0105] Specifically, after recommending personalized insurance products for each family, it is necessary to obtain the estimated coverage rate of each family based on the adjusted policy portfolio. This is done to evaluate the protection effect of the recommended plan and verify the specific improvement achieved by the optimization. It is possible to simulate the new policy portfolio after the adjustment by removing duplicate protection products and adding supplementary products, substitute it into the formula for calculation, and the adjusted estimated coverage rate can be obtained. By comparison, it can be seen that compared with the original plan, the new policy portfolio significantly improves the family protection coverage rate, achieving the optimization effect. This step verifies the optimization effect, ensures the effectiveness of the plan, and provides an intuitive planning expectation for customers.

[0106] Step 602: Calculate the coverage rate difference between the estimated family insurance coverage rate corresponding to each customer group and the family insurance coverage rate; divide the coverage rate difference corresponding to each customer group by the family insurance coverage rate to obtain the improvement range of each insurance product on the family insurance coverage rate.

[0107] Specifically, after calculating the estimated coverage rate of each family, it is necessary to further obtain the improvement rate compared to the original coverage rate. The purpose of doing this is to intuitively evaluate the effectiveness of the optimization plan by quantifying the increment. Specifically, the difference can be calculated between the adjusted estimated coverage rate value and the original coverage rate value to obtain the absolute increment of the coverage rate of the family; then, dividing this increment by the original coverage rate value can calculate the percentage improvement rate of the coverage rate brought by the adjustment plan. This can quantitatively show the improvement ratio of the protection coverage rate of each family after the policy optimization, facilitating the evaluation of the effect.

[0108] Referring to Figure 2 , a policy custody system provided by an embodiment of the present application, the system includes: a data acquisition module, a relationship graph determination module, a coverage rate determination module, and an insurance product push module, where:

[0109] The data acquisition module is used to obtain the policy data of multiple customers; identify each policy data to obtain multiple customer groups with the same family relationship, and extract the family policy data of each customer group;

[0110] The relationship graph determination module is used to construct a family policy relationship graph based on each family policy data. The family policy relationship graph includes multiple first-level nodes representing family units, multiple second-level nodes representing family members, and multiple third-level nodes representing policies;

[0111] The coverage rate determination module is used to calculate the current family insurance coverage rate of each customer group in the family policy relationship graph by combining the family information corresponding to each first-level node, the member information corresponding to each second-level node, and the policy information corresponding to each third-level node;

[0112] The insurance product push module is used to identify the duplicate protection items and protection gap items of each customer group according to the family policy relationship graph, and match the insurance products corresponding to the duplicate protection items and protection gap items of each customer group in the pre-established insurance product library; calculate the improvement rate of each insurance product on the family insurance coverage rate. For each insurance product, if the coverage rate improvement rate of the insurance product is greater than the rate threshold, the insurance product is sent to the corresponding customer group.

[0113] Based on the above embodiment, the data acquisition module is further used to extract the applicant information and the insured information in each policy data, and construct a customer relationship network based on each applicant information and each insured information; identify multiple customer nodes with kinship in the customer relationship network; cluster each customer node with the same kinship to obtain multiple customer groups; merge the policy data of multiple customers in each customer group to obtain the family policy data of each customer group.

[0114] Based on the above embodiments, the data acquisition module is further configured to use the policyholder corresponding to each policyholder information and the insured corresponding to each insured information as a node in the customer relationship network; based on a preset rule, establish a connection relationship between each node to obtain a customer relationship network; wherein, the preset rule is: if the insured of a certain policyholder is not the policyholder, establish a connection relationship between the nodes corresponding to the policyholder and the insured; if there are multiple policy data corresponding to the same policyholder or insured, merge the multiple nodes corresponding to the same policyholder or insured.

[0115] Based on the above embodiments, the relationship graph determination module is further configured to perform text recognition on each family policy data, extract family information, member information, and policy information in each family policy data; create first-level nodes corresponding to each family information, where each first-level node corresponds to a family unit, create second-level nodes corresponding to each member information, where each second-level node corresponds to a family member, create third-level nodes corresponding to each policy information, where each third-level node corresponds to a policy; associate each first-level node with the corresponding second-level node, and associate the second-level node with the corresponding third-level node to generate a family policy relationship graph.

[0116] Based on the above embodiments, the coverage rate determination module is further configured to traverse each family information to determine the total annual family income and total family assets of the family units corresponding to multiple customer groups, traverse each member information to determine the ages and occupations of multiple family members in each family unit, and traverse each policy information to determine the total premium amount, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies in each family unit; according to a preset risk coefficient table, determine the personal risk coefficients corresponding to the ages and occupations of multiple family members in each family unit; substitute the total annual family income and total family assets of each family unit, the personal risk coefficients corresponding to multiple family members in each family unit, and the total premium amount, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies in each family unit into a preset formula to obtain the current family insurance coverage rate of each customer group; wherein, the preset formula is:

[0117]

[0118] In the formula, P i represents the current family insurance coverage rate of the i-th customer group, ω1 represents a preset first weight coefficient, D i represents the average number of risk types covered by existing policies of the i-th family unit, D0 represents the benchmark number of risk types, ω2 represents a preset second weight coefficient, N irepresents the average number of age stages covered by the existing insurance policies of the i-th household unit, N0 represents the benchmark number of age stages, ω3 represents the preset third weight coefficient, C i represents the total insured amount of the i-th household unit, R i,j represents the personal risk coefficient corresponding to the j-th family member in the i-th household unit, M i represents the number of family members in the i-th household unit, A i represents the total annual household income of the i-th household unit, T represents the preset insurance policy term, B i represents the total value of the household assets of the i-th household unit.

[0119] Based on the above embodiments, the insurance product push module is further configured to traverse the third-level nodes and the second-level nodes in the family insurance policy relationship graph to determine multiple protection items in each customer group and the protection amount of each protection item; for each customer group, traverse each protection item, if there are overlapping protection items, then regard the overlapping protection items as the duplicate protection items of the customer group; if there are protection items with a protection amount less than the protection amount threshold, then regard the protection item as a protection gap item.

[0120] Based on the above embodiments, the insurance product push module is further configured to obtain the expected family insurance coverage rate of each customer group after the corresponding insurance product is adjusted; calculate the coverage rate difference between the expected family insurance coverage rate corresponding to each customer group and the family insurance coverage rate; divide the coverage rate difference corresponding to each customer group by the family insurance coverage rate to obtain the improvement amplitude of each insurance product on the family insurance coverage rate.

[0121] It should be noted that: when the device provided in the above embodiments realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0122] This application also discloses an electronic device. Refer to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0123] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0124] Among them, the user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0125] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0126] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server using various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, the processor 301 performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one of the following hardware forms: digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 301 may integrate one or a combination of several of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface graphics, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0127] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may further be at least one storage device located far from the aforementioned processor 301. Refer toFigure 3 In the memory 305, which is a computer storage medium, an operating system, a network communication module, a user interface module, and an application program for a policy custody method may be included.

[0128] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input data and obtain the data input by the user; and the processor 301 may be used to call the application program for a policy custody method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute the method as described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps may be in other sequences or performed simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0129] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0130] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some service interfaces. The indirect couplings or communication connections of the devices or units can be in electrical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, in each embodiment of this application, the functional units can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0133] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0134] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the practice of the disclosure.

[0135] This application aims to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include well-known common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary.

Claims

1. A policy trusteeship method, characterized in that: include: Get policy data for multiple customers; Identify each of the insurance policy data to obtain multiple customer groups with the same family relationship, and extract family insurance policy data of each of the customer groups; Based on each of the family insurance policy data, construct a family insurance policy relationship map, wherein the family insurance policy relationship map includes a plurality of first-level nodes representing family units, a plurality of second-level nodes representing family members, and a plurality of third-level nodes representing insurance policies; In the family insurance policy relationship graph, the current family insurance coverage rate of each customer group is calculated by combining the family information corresponding to each of the first-level nodes, the member information corresponding to each of the second-level nodes, and the insurance policy information corresponding to each of the third-level nodes; According to the family insurance policy relationship map, duplicate protection items and protection gap items of each customer group are identified, and insurance products corresponding to the duplicate protection items and protection gap items of each customer group are matched in the pre-established insurance product library; the improvement of the family insurance coverage rate by each insurance product is calculated, and for each insurance product, if the coverage rate improvement of the insurance product is greater than the range threshold, the insurance product is sent to the corresponding customer group; The calculating the current family insurance coverage rate of each customer group by combining the family information corresponding to each of the first-level nodes, the member information corresponding to each of the second-level nodes, and the insurance policy information corresponding to each of the third-level nodes includes: Traverse each of the family information to determine the total annual family income and total family asset value of the family units corresponding to the multiple customer groups, traverse each of the member information to determine the ages and occupations of multiple family members in each of the family units, and traverse each of the insurance policy information to determine the total package amount of each of the family units, the average number of risk types covered by the existing insurance policies, and the average number of age stages covered by the existing insurance policies; Determining individual risk coefficients corresponding to the ages and occupations of multiple family members in each of the family units according to a preset risk coefficient table; Substitute the total annual family income and total family assets of each family unit, the individual risk coefficients corresponding to multiple family members in each family unit, the total insurance amount of each family unit, the average number of risk types covered by the existing insurance policy, and the average number of age stages covered by the existing insurance policy into the preset formula to obtain the current family insurance coverage rate of each customer group; wherein, the preset formula is: Where P i represents the current household insurance coverage rate of the i-th customer group, ω1 represents the preset first weight coefficient, and D i represents the average number of risk types covered by the existing insurance policy of the ith family unit, D0 represents the number of benchmark risk types, ω2 represents the preset second weight coefficient, and N i represents the average number of age stages covered by the existing insurance policy of the ith family unit, N0 represents the number of base age stages, ω3 represents the preset third weight coefficient, C i represents the total insurance coverage of the ith family unit, R i，j represents the personal risk coefficient corresponding to the jth family member in the i-th family unit, M i represents the number of family members in the ith family unit, A i represents the total annual income of the ith family unit, T represents the preset policy term, and B i represents the total value of household assets of the ith household unit.

2. The insurance policy trusteeship method according to claim 1, characterized in that: The step of identifying each of the insurance policy data to obtain a plurality of customer groups having the same family relationship, and extracting the family insurance policy data of each of the customer groups, includes: Extracting the information of the policyholder and the insured from each of the insurance policy data, and building a customer relationship network based on the information of each of the policyholders and the information of each of the insured; In the customer relationship network, identifying a plurality of customer nodes having a kinship relationship; Clustering the customer nodes having the same kinship relationship to obtain multiple customer groups; The insurance policy data of multiple customers in each of the customer groups are merged to obtain the family insurance policy data of each of the customer groups.

3. The insurance policy trusteeship method according to claim 2, characterized in that: The step of constructing a customer relationship network based on the information of each policyholder and each insured person includes: The insured corresponding to each insured person's information and the insured corresponding to each insured person's information are regarded as a node in the customer relationship network; Based on preset rules, a connection relationship between the nodes is established to obtain a customer relationship network; The preset rules are: If the insured of a certain policyholder is not the policyholder, then the connection relationship is established between the nodes corresponding to the policyholder and the insured; If there are multiple insurance policy data corresponding to the same policyholder or insured person, the multiple nodes corresponding to the same policyholder or insured person are merged.

4. The insurance policy trusteeship method according to claim 1, characterized in that: The method of constructing a family insurance policy relationship map based on each family insurance policy data includes: Performing text recognition on each of the family insurance policy data to extract family information, member information and insurance policy information from each of the family insurance policy data; Creating a first-level node corresponding to each of the family information, wherein each of the first-level nodes corresponds to a family unit, creating a second-level node corresponding to each of the member information, wherein each of the second-level nodes corresponds to a family member, and creating a third-level node corresponding to each of the insurance policy information, wherein each of the third-level nodes corresponds to an insurance policy; Associate each of the first-level nodes with the corresponding second-level nodes, and associate the second-level nodes with the corresponding third-level nodes to generate a family insurance policy relationship graph.

5. The insurance policy trusteeship method according to claim 1, characterized in that: The step of identifying duplicate protection items and protection gap items of each customer group according to the family insurance policy relationship map includes: Traversing the third-level nodes and the second-level nodes in the family insurance policy relationship graph, determining a plurality of insurance items in each of the customer groups and the insurance amount of each of the insurance items; For each of the customer groups, traverse the security items, and if there are overlapping security items, take the overlapping security items as duplicate security items for the customer group; If there is a protection project whose protection amount is less than the protection amount threshold, the protection project will be regarded as a protection gap project.

6. The insurance policy trusteeship method according to claim 1, characterized in that: The calculation of the improvement of the household insurance coverage rate by each of the insurance products includes: Obtaining the estimated household insurance coverage rate of each customer group after adjustment of the corresponding insurance product; Calculating the coverage difference between the estimated family insurance coverage rate and the family insurance coverage rate corresponding to each of the customer groups; The coverage rate difference corresponding to each of the customer groups is divided by the family insurance coverage rate to obtain the improvement in the family insurance coverage rate by each of the insurance products.

7. An insurance policy escrow system, characterized in that: The system comprises: A data acquisition module is used to acquire the insurance policy data of multiple customers; identify each of the insurance policy data, obtain multiple customer groups with the same family relationship, and extract the family insurance policy data of each of the customer groups; A relationship map determination module, configured to construct a family insurance policy relationship map based on each of the family insurance policy data, wherein the family insurance policy relationship map includes a plurality of first-level nodes representing family units, a plurality of second-level nodes representing family members, and a plurality of third-level nodes representing insurance policies; A coverage rate determination module, configured to calculate the current family insurance coverage rate of each customer group in the family insurance policy relationship graph by combining the family information corresponding to each of the first-level nodes, the member information corresponding to each of the second-level nodes, and the insurance policy information corresponding to each of the third-level nodes; An insurance product push module is used to identify the duplicated insurance items and the insurance gap items of each customer group according to the family insurance policy relationship map, and match the insurance products corresponding to the duplicated insurance items and the insurance gap items of each customer group in the pre-established insurance product library; calculate the improvement of the family insurance coverage rate by each insurance product, and for each insurance product, if the coverage rate improvement of the insurance product is greater than the range threshold, send the insurance product to the corresponding customer group; The calculating the current family insurance coverage rate of each customer group by combining the family information corresponding to each of the first-level nodes, the member information corresponding to each of the second-level nodes, and the insurance policy information corresponding to each of the third-level nodes includes: Traverse each of the family information to determine the total annual family income and total family asset value of the family units corresponding to the multiple customer groups, traverse each of the member information to determine the ages and occupations of multiple family members in each of the family units, and traverse each of the insurance policy information to determine the total package amount of each of the family units, the average number of risk types covered by the existing insurance policies, and the average number of age stages covered by the existing insurance policies; Determining individual risk coefficients corresponding to the ages and occupations of multiple family members in each of the family units according to a preset risk coefficient table; Substitute the total annual family income and total family assets of each family unit, the individual risk coefficients corresponding to multiple family members in each family unit, the total insurance amount of each family unit, the average number of risk types covered by the existing insurance policy, and the average number of age stages covered by the existing insurance policy into the preset formula to obtain the current family insurance coverage rate of each customer group; wherein, the preset formula is: Where P i represents the current household insurance coverage rate of the i-th customer group, ω1 represents the preset first weight coefficient, and D i represents the average number of risk types covered by the existing insurance policy of the ith family unit, D0 represents the number of benchmark risk types, ω2 represents the preset second weight coefficient, and N i represents the average number of age stages covered by the existing insurance policy of the ith family unit, N0 represents the number of base age stages, ω3 represents the preset third weight coefficient, C i represents the total insurance coverage of the ith family unit, R i，j represents the personal risk coefficient corresponding to the jth family member in the i-th family unit, M i represents the number of family members in the ith family unit, A i represents the total annual income of the ith family unit, T represents the preset policy term, and B i represents the total value of household assets of the ith household unit.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a policy custody method as described in any one of claims 1-6.

Images