Insurance policy hosting method and system

Abstract

The invention discloses an insurance policy hosting method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring insurance policy data of a plurality of customers; identifying each insurance policy data to obtain a plurality of customer groups, and extracting family insurance policy data of each customer group; constructing a family insurance policy relation graph; calculating the family insurance coverage rate of each customer group in combination with the family information of each first-level node, the member information of each second-level node and the insurance policy information of each third-level node in the family insurance policy relationship graph; identifying repeated guarantee items and guarantee gap items of each customer group, and matching insurance products of the repeated guarantee items and the guarantee gap items of each customer group in a pre-established insurance product library; and calculating the improvement amplitude of each insurance product on the family insurance coverage rate, and if the improvement amplitude of the coverage rate is greater than an amplitude threshold value, sending the insurance products to the customer group. By implementing the technical scheme provided by the invention, the effect of improving the insurance policy trusteeship efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically 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. Policy custody services are designed to help customers better manage and optimize their insurance protection, improve the efficiency of insurance resource utilization, and provide insurance companies with opportunities for value-added services.

[0003] At present, the existing policy custody method mainly collects customers' personal information and policy data through insurance brokers, analyzes the coverage of each policy one by one, and then makes policy optimization suggestions based on the customer's personal needs and risk status to recommend insurance products.

[0004] However, in actual applications, existing policy custody methods mostly focus on the analysis of individual customers. When family policy analysis is required, the workload increases significantly. Insurance brokers often need to spend a lot of time and find it 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 have the effect of improving the efficiency of policy custody.

[0006] In a first aspect, the present application provides a policy trusteeship method, comprising: 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, the duplicate protection items and protection gap items of each customer group are identified, and the 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 improvement of the coverage rate of the insurance product is greater than the range threshold, the insurance product will be sent to the corresponding customer group.

[0007] In a second aspect of the present application, a policy escrow system is provided, the system comprising: 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; 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 insurance policy relationship map, 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 of the family insurance coverage rate by each insurance product, and for each insurance product, if the improvement of the coverage rate of the insurance product is greater than the range threshold, the insurance product will be sent to the corresponding customer group.

[0008] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a policy custody method when loaded and executed by the processor.

[0009] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein 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.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solutions, the insurance policy data of multiple customers can be centrally acquired and identified, customer groups with the same family relationship can be accurately divided, and a multi-level family policy relationship map can be constructed based on family policy data. Combining family information, member information and policy information, the family insurance coverage rate is systematically calculated, duplicate protection and protection gaps are automatically identified, and products are intelligently matched and optimized in the preset insurance product library. Finally, by evaluating the extent to which insurance products can increase coverage, the most suitable insurance products can be accurately recommended, the family insurance portfolio can be optimized, and resource waste and insufficient protection can be reduced. Compared with traditional insurance brokers who need to spend a lot of time analyzing policy data one by one, the processing time is shortened, thereby improving the efficiency of policy custody. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of a policy trusteeship method provided in an embodiment of the present application; Figure 2 It is a structural diagram of an insurance policy escrow system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field 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 drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The present application embodiment provides a policy trusteeship method. In one embodiment, please refer to Figure 1 , Figure 1 This is a flowchart of the insurance policy custody method provided in the embodiment of the present application. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on an insurance policy custody system based on the von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain insurance policy data of multiple customers; identify each insurance policy data to obtain multiple customer groups with the same family relationship, and extract the family insurance policy data of each customer group.

[0017] Among them, policy data refers to detailed information related to the insurance product purchased by the customer. In the embodiment of the present application, it can be understood as all data information related to the specific policy, such as policyholder information, insured person information, insurance company information, insurance period, insurance amount, insurance premium, etc., which is used to describe the details of the insurance product purchased by the customer.

[0018] A customer group refers to the result of grouping multiple customers by family relationships. In the embodiment of the present application, it can be understood as the classification of multiple family members with relatives who purchase insurance products and their corresponding policy data, which is used for the subsequent extraction of family policy data and family insurance needs analysis.

[0019] Family policy data refers to the collection of policy information of all customers belonging to the same family. In the embodiment of the present application, it can be understood as the result of aggregating the policy data of all customers belonging to the same customer group, which is used to describe the overall insurance purchasing situation and protection status of a family.

[0020] Specifically, in order to realize the automated hosting service of family insurance policies, it is necessary to obtain the insurance policy data of multiple customers. These insurance policy data can come from the business system database of the insurance company, or the insurance agent can collect the insurance policy data uploaded by the customer through a web form or other means. After obtaining the customer's insurance policy data, it is necessary to identify the insurance policy data, mainly identifying the policyholder information and the insured information in the insurance policy data. This identification can be achieved by text extraction of the insurance policy data, extracting the name and certificate information of the policyholder, the name and certificate information of the insured, etc. Then, it is necessary to determine whether there is a family relationship between different insurance policies based on the extracted policyholder information and insured information. Specifically, a customer relationship network can be established, with the policyholder and the insured as nodes. If the insured of the policyholder is not himself, a family relationship connection is established between the two nodes. The same policyholder or insured is merged. Finally, the area where multiple connected nodes appear in the relationship network corresponds to a family. After being divided according to the family relationship, all insurance policy data belonging to the same family can be extracted to form family insurance policy data. In this way, the original customer insurance policy data can be divided into multiple family insurance policy data sets, laying the foundation for subsequent family insurance policy analysis and processing.

[0021] Based on the above embodiment, as an optional embodiment, in step 101: identifying each insurance policy data, obtaining multiple customer groups with the same family relationship, and extracting family insurance policy data of each customer group, this step may also include the following steps: Step 201: extract the information of the policyholder and the insured person from each insurance policy data, and build a customer relationship network based on the information of each policyholder and each insured person.

[0022] Among them, the policyholder information and the insured information refer to the relevant personal information used to describe the policyholder and the insured in the policy data. In the embodiment of the present application, it can be understood as the name, ID number, contact information, etc. of the policyholder and the insured that can uniquely identify a person. It is used to determine whether there is a family relationship between the policyholders and the insured between different policies, and ultimately identify the policies belonging to the same family.

[0023] The customer relationship network refers to a network diagram constructed through the association relationship between policyholder information and insured person information. In the embodiment of the present application, it can be understood as a network structure that takes each policyholder and insured person in the policy data as a node and establishes connection relationships between nodes based on their family relationships. It is used to represent family relationships between different customers and identify family customer groups in the network.

[0024] Specifically, after obtaining the information of the policyholder and the insured of each policy data, it is necessary to establish a customer relationship network based on this information. Each independent policyholder and insured can be set as a node. If the policyholder's insured is not himself, a connection line is established between the corresponding two nodes to indicate that there is a family relationship between the two. For the same policyholder or insured that appears in multiple policy data, it is necessary to merge the corresponding multiple nodes into one node. Finally, the multiple interconnected nodes condensed in the relationship network diagram are a family or kinship circle, in which all the policyholders and insureds have family relationships. In this way, the family relationship in the policy data can be identified, laying the foundation for the next step of family policy extraction and analysis.

[0025] Based on the above embodiment, as an optional embodiment, in step 201: building a customer relationship network based on the information of each policyholder and each insured person, this step may also include the following steps: Step 211: The policyholder corresponding to each policyholder information and the insured corresponding to each insured person information are regarded as a node in the customer relationship network.

[0026] Specifically, the first step in building a customer relationship network is to treat each independent policyholder and insured person in the policy data as a node in the network graph, because the policyholder and the insured are the two most critical information points for judging family relationships. Each policyholder and each insured person needs to be added to the network graph as an independent individual in order to determine the family relationship between these nodes through the subsequent node connection relationship. By traversing each extracted policyholder information, a corresponding node is created for each unique policyholder; at the same time, each insured person information is traversed, and a corresponding node is created for each unique insured person. Ultimately, all nodes in the network graph represent all unique policyholder and insured individuals involved in these policy data, which lays the foundation for subsequent judgment of family relationship connections between nodes to identify family customer groups.

[0027] Step 221: Based on preset rules, a connection relationship is established between each node to obtain a customer relationship network; wherein the preset rules are: if the insured of a certain policyholder is not the policyholder, a connection relationship is established between the nodes corresponding to the policyholder and the insured; if there are multiple policy data corresponding to the same policyholder or insured, multiple nodes corresponding to the same policyholder or insured are merged.

[0028] Specifically, when all the insured and insured nodes are ready, it is necessary to determine the connection relationship between these nodes based on the preset family relationship judgment rules, and finally form a customer relationship network. The purpose of this is to determine whether two specific nodes have a family relationship by the existence or non-existence of the connection relationship. Only by constructing a network diagram that correctly reflects the node relationship 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 an insured is not him / herself, it can be determined that the insured and the corresponding insured node have a family relationship, and a connection line needs to be established between the two nodes. If the same insured or insured is found in multiple policy data, these duplicate nodes need to be merged to form a single node representing this common insured / insured. According to such rules, the relationship between each node is judged, and finally a customer relationship network covering all insured and insured persons is formed. In this way, the family relationship between each node is accurately reflected in the form of a network diagram, which provides a basis for subsequent customer group identification and family policy extraction.

[0029] Step 202: In the customer relationship network, identify multiple customer nodes with kinship relationships; cluster the customer nodes with the same kinship relationships to obtain multiple customer groups.

[0030] Among them, multiple customer nodes with kinship refer to network nodes corresponding to multiple customers who are associated through family relationships in the customer relationship network. In the embodiment of the present application, it can be understood as the corresponding node representation in the network diagram of multiple family members who belong to the same family and have purchased insurance products, which is used to represent multiple customers with family relationships, so as to subsequently identify family customer groups.

[0031] Specifically, after constructing a relationship network representing the customer's family relationships, it is necessary to identify customer nodes with kinship relationships in the network and cluster them to form customer groups. The way to identify customer nodes with kinship relationships is to observe the connections between nodes in the relationship network diagram. Any two customer nodes connected by a line indicate that the two customers are related and belong to the same family. Then, cluster all related customer nodes in the relationship network diagram, and use existing community discovery algorithms or clustering algorithms to form different groups of closely connected customer nodes. Each group is a family, and the final customer groups include all family members who belong to the same family and have purchased insurance products.

[0032] Step 203: Merge the insurance policy data of multiple customers in each customer group to obtain the family insurance policy data of each customer group.

[0033] Specifically, after obtaining the customer group representing a specific family, it is necessary to further integrate all customer policy data within each customer group to form family policy data. Family policy data is formed because subsequent policy analysis and insurance needs assessment need to be based on the family rather than the individual as the basic unit. It is not enough to just look at the policy of a certain family member, and you need to have an overall understanding of the family's current protection situation. The specific way to form family policy data is to traverse each identified customer group, extract the policy data of all customers in the group, and integrate them, including summarizing premiums, protection amounts, product types and other information. Ultimately, each customer group corresponds to a set of family policy data representing the insurance situation of the entire family.

[0034] Step 102: Based on the data of each family insurance policy, a family insurance policy relationship map is constructed, where 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.

[0035] Among them, the family insurance policy relationship map refers to a relationship diagram that uses a network diagram to display the insurance policy relationship and protection structure within the family. In the embodiment of the present application, it can be understood as a multi-level network diagram containing family nodes, family member nodes, insurance policy nodes, and the relationship between nodes. It is used to intuitively display the protection status of each family and support subsequent insurance policy analysis and optimization.

[0036] The first-level node refers to the node representing the entire family entity in the family insurance policy relationship graph. In the embodiment 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.

[0037] The second-level nodes refer to the nodes representing family members in the family insurance policy relationship graph. In the embodiment of the present application, they can be understood as nodes representing each specific member of a family, and are used to identify different members within the family in the graph.

[0038] The third-level node refers to the node representing the insurance policy in the family insurance policy relationship graph. In the embodiment of the present application, it can be understood as a node representing each insurance policy purchased by a family member, and is used to identify each specific insurance policy of the family members in the graph.

[0039] Specifically, information is extracted from family insurance policy data to obtain family information, family member information, and insurance policy information. For each extracted family information, a corresponding family node is created in the graph, and for each member information, a member child node is created under its corresponding family node. For each insurance policy information, a policy child node is created under its corresponding member node. Repeat this step until all family insurance policy data are modeled into the relationship graph. Finally, a multi-level network graph containing family nodes, member nodes, and insurance policy nodes and their relationships is formed, which intuitively displays the protection structure of all families. This provides data support for subsequent protection analysis and policy optimization. Through the visualized insurance policy relationship graph, a more comprehensive understanding of the family's protection status can be achieved, and precise insurance policy custody services can be implemented for each family.

[0040] Based on the above embodiment, as an optional embodiment, in step 102: constructing a family insurance policy relationship map based on each family insurance policy data, this step may also include the following steps: Step 301: Perform text recognition on each family insurance policy data to extract family information, member information and insurance policy information from each family insurance policy data.

[0041] Among them, family information, member information and policy information refer to the three types of node information required to construct a family policy relationship graph, which can be understood in the embodiments of the present application as follows: Family information: information describing the basic situation of the family, such as family name, family address, etc. Member information: information describing family members, such as member names, contact information, etc. Policy information: information describing the specific circumstances of the policy, such as policy number, premium, coverage items, etc. The three are used to create family nodes, member nodes and policy nodes in the graph, respectively. Family information reflects an independent family individual, member information reflects the specific members of the family, and policy information reflects the policy purchased by the member.

[0042] Specifically, after obtaining the family insurance policy data, it is necessary to extract family information, member information and policy information from it through text recognition technology. This is because the family insurance policy data contains a large amount of unstructured text information, and it is necessary to identify the structured information required to build the policy graph. By directly extracting these three types of information from the text, node information can be automatically obtained without manually checking the policy text and entering the information one by one. Existing NLP, rule matching and other methods can be used to identify sentences describing the family in the policy text as family information; identify sentences describing the purchaser or the insured as member information; and identify sentences describing the policy details as policy information. Then necessary processing can be performed, 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 massive unstructured policy data, greatly improving the efficiency of information acquisition and providing a structured data source for building a policy relationship graph.

[0043] Step 302: Create a first-level node corresponding to each family information, where each first-level node corresponds to a family unit, create a second-level node corresponding to each member information, where each second-level node corresponds to a family member, and create a third-level node corresponding to each insurance policy information, where each third-level node corresponds to an insurance policy.

[0044] Specifically, after obtaining family information, member information, and insurance policy information, it is necessary to create corresponding three-level nodes in the graph for these three types of information. This is done to establish a three-level node system of family, member, and insurance policy in the graph, accurately reflecting the information entities in the family insurance policy relationship. Traverse each extracted family information, and create a new first-level family node with the family information, so that each first-level node corresponds to a unique family. Similarly, traverse each 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 insurance policy information, and create a new third-level insurance policy node under the second-level node of the corresponding member, so that each third-level node corresponds to an insurance policy. After this processing, a three-level node system representing different information entities is established in the graph, laying the foundation for the next step of connecting node relationships.

[0045] 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 insurance policy relationship graph.

[0046] Specifically, when the three-level nodes are ready, it is necessary to further establish the relationship 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 to form a networked knowledge graph that can fully reflect the internal relationships of family insurance policies. First, traverse each first-level family node and find the corresponding second-level member nodes that belong to this family, and connect the two through the inclusion relationship. Then traverse each second-level member node and find the corresponding third-level policy nodes purchased by the member, and connect the two through the purchase relationship. After this processing, the relationship between information entities of different granularities is established in the graph, forming a network topology from abstract to concrete, that is, the final family insurance policy relationship graph.

[0047] Step 103: In the family insurance policy relationship graph, 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 are combined to calculate the current family insurance coverage rate of each customer group.

[0048] Among them, the family insurance coverage rate refers to an important indicator for evaluating the current protection status of a family. In the embodiment of the present application, it can be understood as the ratio of the insurance premium actually purchased by a family to the recommended premium expenditure level. It is used to determine whether the family has a protection gap and the size of the gap, providing a basis for subsequent policy optimization.

[0049] Specifically, after constructing the family insurance policy relationship map, it is necessary to calculate the current insurance coverage rate of each family based on the map and analyze the family's protection level. This is done because the insurance coverage rate is an important indicator for evaluating the protection status of a family. By calculating the coverage rate of each family, it can be determined whether the family has a protection gap and the size of the gap, providing a basis for subsequent accurate recommendations. The calculation method is: for each first-level family node in the map, the number of third-level policy nodes it contains is counted, and the total premium is calculated; at the same time, the recommended premium expenditure level of the family is calculated by referring to the corresponding family information and member information; finally, the ratio of the actual premium of the third-level policy node to the recommended premium is the insurance coverage rate of the family. After calculation, the current insurance coverage level of each family can be obtained. This can determine the families with protection gaps among high-net-worth customers, and can also discover the situation of over-purchase of insurance, which provides a basis for subsequent policy optimization. Calculating the insurance coverage rate realizes a more intelligent family protection assessment, which helps to tailor a protection plan for each family in the future.

[0050] Based on the above embodiment, as an optional embodiment, in step 103: in the family insurance policy relationship graph, 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 are combined to calculate the current family insurance coverage rate of each customer group. This step may also include the following steps: Step 401: 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 insurance policy information to determine the total package amount of each family unit, the average number of risk types covered by existing insurance policies, and the average number of age stages covered by existing insurance policies.

[0051] Among them, the total annual family income and the total value of family assets refer to two key indicators for evaluating the family's financial status, which can be understood in the embodiment of the present application as: the total annual family income: the sum of all pre-tax income of a family in one year, the total value of family assets: the total value of all assets owned by a family.

[0052] The total package amount, the average number of risk types covered by existing policies, and the average number of age stages covered by existing policies refer to the three key indicators for evaluating the degree of optimization of family policy configuration. In the embodiments of the present application, it can be understood that the total package amount is the sum of the insured amounts of a family's existing policies. The average number of risk types covered by existing policies is the average number of life cycle risk categories covered by existing policies for multiple family members in each family unit. The average number of age stages covered by existing policies is the average number of different age groups covered by existing policies for multiple family members in each family unit. The three are used to comprehensively evaluate whether the configuration of existing policies is optimized and perfect, and are used to compare with the optimized policies to determine the effect of policy optimization.

[0053] 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 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 determine the protection needs; traverse each third-level policy node to obtain information such as the total policy amount and the number of covered risk types to evaluate the current protection configuration. This is done to extract all the feature information required to determine the protection optimization from the graph and conduct accurate 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 policy node to obtain information such as the total 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.

[0054] Step 402: According to a preset risk coefficient table, determine the individual risk coefficients corresponding to the ages and occupations of multiple family members in each family unit.

[0055] Among them, the preset risk coefficient table refers to a coefficient table generated in advance based on statistical data. In the embodiment of the present application, it can be understood as a lookup table containing risk coefficients corresponding to different age groups and different occupations. After determining the age and occupation of each family member, it is used to find the matching risk coefficient and evaluate the insurance risk level faced by the member.

[0056] The personal risk coefficient refers to a quantitative indicator for assessing the degree of insurance risk faced by a family member. In the embodiment of the present application, it can be understood as a risk coefficient corresponding to a certain age group and occupation determined in advance based on statistical analysis, which is used to indicate the current risk level of the member and provide a basis for subsequently determining his reasonable protection goals.

[0057] Specifically, after obtaining the age and occupation information of each family member, it is necessary to determine the risk coefficient corresponding to each member according to the preset risk coefficient table. This is done because the insurance risk coefficients corresponding to different age groups and occupations are also different. It is necessary to obtain the standard risk coefficients by looking up the table to provide basic data for the subsequent calculation of personalized protection. A statistically derived 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 map, find the age attributes and occupational attributes of each member, find the matching risk coefficient in the coefficient table and record it. In the end, each member node will correspond to a risk coefficient, reflecting the risk level of the member, providing an important basis for calculating personalized protection goals. By looking up the table to obtain the standardized risk coefficient, the protection calculation of each family member can provide an objective risk assessment basis, making the protection optimization more accurate.

[0058] Step 403: 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 a 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, N irepresents 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.

[0059] The preset formula refers to a mathematical formula used to calculate the current family insurance coverage rate of each customer group. In the embodiment of the present application, the preset formula can be understood as a multivariate analysis formula that includes factors such as 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 package amount of each family unit, the average number of risk types covered by existing insurance policies, and the average number of age stages covered by existing insurance policies. The preset formula is used for quantitative calculation and evaluation of family insurance coverage rate. By substituting actual test data, the current family insurance coverage rate of each customer group can be calculated.

[0060] Specifically, after obtaining the family financial information, member risk coefficients and current protection configuration indicators, they need to be substituted into the preset formula to calculate the insurance coverage rate of each family. This is done to use the preset calculation model to evaluate and judge the protection status of each family, and obtain accurate and objective insurance coverage results. Substitute the total family income, total family assets, member risk coefficients, total policy amount, number of covered risk categories, etc. extracted from each family into the formula, and after calculation, the coverage rate value of the family can be obtained. The formula comprehensively considers factors such as income, assets, risks and current protection, and calculates a coverage rate result that is of reference significance. After obtaining the coverage rate, it can be determined which families have protection gaps and which families have excessive protection, providing a basis for subsequent policy configuration optimization.

[0061] The formula consists of three parts. The first part describes the impact of the average number of risk types covered by existing policies on the current home insurance coverage of the customer group. It represents the ratio of the average number of risk types covered by existing insurance policies to the number of benchmark risk types. This ratio reflects the breadth of coverage of family insurance policies on different risk types. iIndicates the average number of risk types covered by the family's current insurance policy. For example, fire, theft, natural disasters, etc. D0 is the set benchmark number of risk types for comparison and standardization. The more risk types a family's insurance policy covers, the higher the corresponding ratio, and the greater the contribution to the insurance coverage rate. This part of the indicator helps evaluate the comprehensiveness and effectiveness of the policy.

[0062] For example, assume that the number of baseline risk types D0 is 5, indicating that general insurance policies usually cover 5 risk types. Family A: Its insurance policy covers 3 risk types (fire, theft, and flood), namely D i is 3, =0.6. Family B: Its insurance policy covers 6 types of risks (fire, theft, flood, earthquake, lightning, and storm), namely D i is 6, then The ratio of Family A is 0.6, which is lower than the benchmark, meaning that its policy covers fewer risk types and has a lower coverage rate, while the ratio of Family B is 1.2, which is higher than the benchmark, meaning that its policy covers more risk types and has a higher coverage rate.

[0063] The second section describes the extent to which the average number of age groups covered by existing policies affects the current household insurance coverage of the customer group. It indicates the ratio of the average number of age groups covered by existing insurance policies to the number of base age groups. This part reflects the coverage of insurance policies for different age groups. i Indicates the average number of age groups covered by the current family insurance policy, for example, children, youth, middle-aged, and elderly. N0 indicates the number of benchmark age groups set for standardized comparison. The more age groups covered by the policy, the higher the proportion, and the greater the contribution to the insurance coverage rate. This part of the indicator helps evaluate the comprehensiveness and adaptability of the policy.

[0064] For example, assume that the number of base age stages N0 is 4, which means that the insurance policy usually covers 4 age stages, such as children, youth, middle-aged, and elderly. Family A: Its insurance policy covers 3 age stages (children, youth, and middle-aged), that is, N i If is 3, is 0.75. Family B: Its insurance policy covers two age groups (youth and middle-aged), namely N i is 2, then The ratio of Family A is 0.75, which means that its insurance policy covers more age groups than Family B, which helps to improve the insurance coverage rate. The ratio of Family B is 0.5, which means that its insurance policy covers fewer age groups than Family A, which has a smaller impact on the coverage rate.

[0065] The second part describes the comprehensive impact of the total annual income of each family unit, the total value of family assets, the individual risk coefficients of multiple family members in each family unit, and the total package amount of each family unit on the current family insurance coverage rate of the customer group. Represents the comprehensive consideration of economics and risk in home insurance coverage. i It represents the total insurance amount of the family, that is, the investment amount of insurance, which determines the family's investment in insurance. A higher insurance amount usually means better protection, that is, a higher family insurance coverage rate. On the contrary, the lower the total insurance amount, the lower the family insurance coverage rate. ∑R i,j It represents the sum of the individual risk factors of family members. The risk factor is based on factors such as health and occupation. i Represents the number of family members, used to calculate the average risk coefficient, It is the average risk factor of each family member. The higher the average risk factor, the greater the adjusted insurance amount, reflecting that the family needs more protection. i represents the total annual income of the family, indicating the economic capacity. T represents the policy term, indicating the duration of the insurance. i It represents the total value of household assets and represents financial stability. This ratio takes into account the insured amount, risk and economic foundation. A high ratio indicates that the family still has a strong economic ability to bear high risks, which means that the family insurance coverage rate is larger. On the contrary, if the ratio of this part is smaller, it means that the family's risk-bearing capacity is smaller, which means that the family insurance coverage rate is smaller.

[0066] For example, suppose the total insured amount of family A is C i is 1000000, the sum of risk coefficients ∑R i,j is 10, the number of family members is M i =4, the total annual household income is A i 50,000, the total value of family assets is B i is 200000, and the policy term T is 5. Then it represents the risk-adjusted portion of the insured amount 350,000, representing the family's economic foundation (A i ×T+B i ) is 450000, then The value of the part is 0.75. The ratio of Family A is 0.75, which indicates the ratio of the risk-adjusted insurance amount to the family's economic foundation. The larger the ratio, the greater the family insurance coverage, and the smaller the ratio, the smaller the family insurance coverage.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] An insurance product refers to a protection solution designed by an insurance company for a certain type of risk or age group. In the embodiment of the present application, it can be understood as a commercial insurance included in a pre-established product library for protecting specific risks or age groups. It is used to provide more optimized protection options when duplicate protection items or protection gaps in family insurance policies are identified.

[0073] Specifically, after calculating the insurance coverage rate of each family, it is necessary to identify the duplicate protection items and protection gap items of each family based on the constructed family insurance policy relationship map, and find the corresponding supplementary insurance products in the insurance product library. This is done to point out the problems of each family under the current protection configuration through graph analysis, and to directly match the insurance products required for optimization, providing product support for the subsequent generation of optimization solutions. By counting the attributes of each third-level policy node, the repeatedly purchased protection items can be identified; the distribution of policy nodes can be analyzed to find the stages and risks where there are protection gaps. Then query the product library for insurance products that supplement these items and risks. In the end, duplicate protection and protection gaps, as well as corresponding supplementary products, will be identified under each family node, providing a product basis for the generation of personalized insurance policy combinations. Based on the analysis and product matching of the policy relationship map, the protection problems of each family can be efficiently and accurately pointed out and solutions can be given, realizing precise policy optimization for each customer group.

[0074] Based on the above embodiment, as an optional embodiment, in step 104: according to the family insurance policy relationship map, identify the duplicate protection items and protection gap items of each customer group. This step may also include the following steps: Step 501: Traverse the third-level nodes and the second-level nodes in the family insurance policy relationship map to determine multiple protection items in each customer group and the protection amount of each protection item.

[0075] Among them, the protection item refers to the specific content of the insurance policy that protects a certain type of risk or age group. In the embodiment of the present application, it can be understood as a specific protection clause in a certain insurance product corresponding to the third-level node in the family insurance policy relationship map.

[0076] The coverage amount refers to the amount of money paid by the insurance policy for a certain coverage item when an insurance accident occurs. In the embodiment of the present application, it can be understood as the payment amount specified in the insurance product corresponding to the third-level node in the family insurance policy relationship map, which is used to indicate the coverage strength and level of the coverage item.

[0077] 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 fully extract the existing protection configuration of each family. Specifically, by reading the attributes of each policy node, you can obtain the policy's protection items, protection period, protection amount and other information; by reading the attributes of each member node, you can know the member's age, occupation, risk status and other information. Through traversal, you can accurately obtain a family's existing detailed protection information, including the purchased products and the corresponding protection content, to provide information support for the subsequent generation of new protection plans. This step obtains a comprehensive existing protection configuration and lays the foundation for formulating the best solution based on individual information.

[0078] Step 502: For each customer group, traverse each protection item. If there are overlapping protection items, the overlapping protection items are regarded as duplicate protection items of the customer group; if there are protection items whose protection amount is less than the protection amount threshold, the protection items are regarded as protection gap items.

[0079] Specifically, after obtaining the existing protection items and protection amounts for 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 that exist for each family at the level of specific protection content and to provide guidance for subsequent policy optimization. Specifically, by traversing the protection items of each family, if there are two policies that provide protection for the same risk and age group, it will be judged as a duplicate protection item; if the protection amount of a certain item is lower than the preset minimum protection amount threshold, it will be identified as a protection gap item. Ultimately, the duplicate protection items and protection gap items of each family can be clarified at the project level to guide subsequent policy adjustments and optimizations. This step judges the policy configuration status from the protection content level, so that the optimization plan can more accurately solve specific protection problems.

[0080] Step 105: Calculate the extent to which each insurance product improves the family insurance coverage rate. For each insurance product, if the extent to which the insurance product's coverage rate is improved is greater than an extent threshold, the insurance product is sent to the corresponding customer group.

[0081] Among them, the improvement rate 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 candidate product to the family's existing insurance policy and the combined coverage rate of the original insurance policy, which is used to evaluate the effect of the candidate product on improving family protection.

[0082] Specifically, after matching the available insurance products, it is necessary to calculate the improvement of each product on the family insurance coverage rate, and select the products with larger improvement to recommend to the corresponding families. This is done to select the products that have the greatest effect on improving the current protection from the optional products for recommendation, so that the policy optimization is more accurate and effective. You can simulate adding each candidate product to the family's existing policy portfolio, recalculate the coverage rate based on the formula, and compare it with the original coverage rate to get the improvement value. If the preset improvement threshold is met, the product is recommended to the family. In the end, each family will only get product recommendations that are suitable for them and have the greatest effect on improving protection, realizing personalized policy optimization. This step selects the best solution based on the quantitative effect, making the policy optimization results more intelligent.

[0083] Based on the above embodiment, as an optional embodiment, in step 105: calculating the improvement of the family insurance coverage rate by each insurance product, this step may also include the following steps: Step 601: Obtain the estimated household insurance coverage rate of each customer group after the corresponding insurance product is adjusted.

[0084] Among them, the estimated family insurance coverage rate refers to the new insurance coverage rate level that the family will have after the policy optimization and adjustment. In the embodiment of the present application, it can be understood as the new insurance coverage rate value of the family predicted by the calculation model after replacing duplicate protection products and adding supplementary products. It is used to evaluate the effect of the optimization plan and show the coverage level that can be achieved after adjustment.

[0085] 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 combination. This is done to evaluate the protection effect of the recommended plan and verify the specific improvement achieved by the optimization. By removing duplicate protection products, adding supplementary products, simulating the new adjusted policy combination, and substituting it into the formula for calculation, you can get the adjusted estimated coverage rate. By comparison, it can be seen that compared with the original plan, the new policy combination significantly improves the family protection coverage rate and achieves the optimization effect. This step verifies the optimization effect, ensures the effectiveness of the plan, and provides customers with intuitive planning expectations.

[0086] Step 602: Calculate the coverage difference between the estimated family insurance coverage rate and the family insurance coverage rate corresponding to each customer group; divide the coverage difference corresponding to each customer group by the family insurance coverage rate to obtain the improvement of the family insurance coverage rate by each insurance product.

[0087] Specifically, after calculating the estimated coverage rate for each family, it is necessary to further obtain the increase compared to the original coverage rate. This is done to intuitively evaluate the effectiveness of the optimization plan by quantifying the increment. Specifically, the estimated coverage rate value after adjustment can be subtracted from the original coverage rate value to obtain the absolute increase in the coverage rate of the family; then the increment can be divided by the original coverage rate value to calculate the percentage increase in coverage rate brought by the adjustment plan. This can quantitatively show the increase in the coverage rate of each family after the policy is optimized, which is convenient for evaluating the effect.

[0088] Reference Figure 2 , is a policy custody system provided in an embodiment of the present application, the system comprising: a data acquisition module, a relationship map determination module, a coverage determination module, and an insurance product push module, wherein: The data acquisition module is used to acquire the insurance policy data of multiple customers; identify each insurance policy data, obtain multiple customer groups with the same family relationship, and extract the family insurance policy data of each customer group; A relationship map determination module, used to construct a family insurance policy relationship map based on each 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 determination module is used to calculate the current family insurance coverage rate of each customer group 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 in the family insurance policy relationship graph; The insurance product push module is used to identify the duplicate protection items and protection gap items of each customer group based on the family insurance policy relationship map, 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 increase in family insurance coverage rate by each insurance product, and for each insurance product, if the increase in the coverage rate of the insurance product is greater than the increase threshold, the insurance product will be sent to the corresponding customer group.

[0089] On the basis of the above-mentioned embodiment, the data acquisition module is also used to extract the policyholder information and the insured person information from each insurance policy data, and build a customer relationship network based on the policyholder information and the insured person information; in the customer relationship network, identify multiple customer nodes with kinship relationships; cluster the customer nodes with the same kinship relationships to obtain multiple customer groups; merge the insurance policy data of multiple customers in each customer group to obtain the family insurance policy data of each customer group.

[0090] On the basis of the above embodiment, the data acquisition module is also used to take the policyholder corresponding to each policyholder information and the insured corresponding to each insured person information as a node in the customer relationship network; based on preset rules, a connection relationship between each node is established to obtain a customer relationship network; wherein the preset rules are: if the insured of a policyholder is not the policyholder, a connection relationship is established between the nodes corresponding to the policyholder and the insured; if there are multiple policy data corresponding to the same policyholder or insured, multiple nodes corresponding to the same policyholder or insured are merged.

[0091] Based on the above embodiment, the relationship graph determination module is also used to perform text recognition on each family insurance policy data, extract family information, member information and policy information in each family insurance policy data; create a first-level node corresponding to each family information, wherein each first-level node corresponds to a family unit, create a second-level node corresponding to each member information, wherein each second-level node corresponds to a family member, create a third-level node corresponding to each policy information, wherein each third-level node corresponds to an insurance 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 insurance policy relationship graph.

[0092] On the basis of the above embodiment, the coverage rate determination module is also used to traverse the information of each family, determine the total annual family income and total family assets of the family units corresponding to multiple customer groups, traverse the information of each member, determine the ages and occupations of multiple family members in each family unit, and traverse the information of each insurance policy to determine the total package amount of each family unit, the average number of risk types covered by existing insurance policies, and the average number of age stages covered by existing insurance policies; determine the individual risk coefficients corresponding to the ages and occupations of multiple family members in each family unit 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 package amount of each family unit, the average number of risk types covered by existing insurance policies, and the average number of age stages covered by existing insurance policies into a 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, 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, Ci 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.

[0093] Based on the above embodiment, the insurance product push module is also used to traverse the third-level nodes and the second-level nodes in the family insurance policy relationship map 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 the overlapping protection items are regarded as duplicate protection items for the customer group; if there are protection items whose protection amount is less than the protection amount threshold, then the protection item is regarded as a protection gap item.

[0094] Based on the above embodiment, the insurance product push module is also used to obtain the estimated family insurance coverage rate of each customer group after the corresponding insurance product is adjusted; calculate the coverage difference between the estimated family insurance coverage rate and the family insurance coverage rate corresponding to each customer group; divide the coverage difference corresponding to each customer group by the family insurance coverage rate to obtain the improvement of the family insurance coverage rate by each insurance product.

[0095] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, 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 embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0096] The present application also discloses an electronic device. Figure 3 , Figure 3 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 .

[0097] The communication bus 302 is used to realize the connection and communication between these components.

[0098] The user interface 303 may include a display interface and a camera interface. The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0099] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0100] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, 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 it can be implemented separately through a chip.

[0101] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (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, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a policy escrow method.

[0102] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program storing a policy custody method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more methods such as in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0105] 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 distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0108] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0109] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

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, the duplicate protection items and protection gap items of each customer group are identified, and the 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 improvement of the coverage rate of the insurance product is greater than the range threshold, the insurance product will be sent to the corresponding customer group.

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 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.

6. 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.

7. 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.

8. 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; 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 insurance policy relationship map, 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 of the family insurance coverage rate by each insurance product, and for each insurance product, if the improvement of the coverage rate of the insurance product is greater than the range threshold, the insurance product will be sent to the corresponding customer group.

9. 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 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, a policy custody method as described in any one of claims 1-7 is executed.

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