CN119417618A

CN119417618A - A policy trusteeship method and system

Info

Publication number: CN119417618A
Application number: CN202411437475.7A
Authority: CN
Inventors: 殷文浩; 朱嘉威; 吴执牛; 浦静; 孙超
Original assignee: Neem Garden Technology Development Co ltd
Current assignee: Neem Garden Technology Development Co ltd
Priority date: 2024-10-15
Filing date: 2024-10-15
Publication date: 2025-02-11
Anticipated expiration: 2044-10-15
Also published as: CN119417618B

Abstract

A policy trusteeship method and system, relating to the field of data processing technology. The method includes: obtaining policy data of multiple customers; identifying each policy data to obtain multiple customer groups, and extracting family policy data of each customer group; constructing a family policy relationship map; in the family policy relationship map, combining the family 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 family insurance coverage rate of each customer group; identifying the duplicate protection items and protection gap items of each customer group, and matching the insurance products of the duplicate protection items and protection gap items of each customer group in the pre-established insurance product library; calculating the improvement of the family insurance coverage rate of each insurance product, and if the coverage rate improvement is greater than the threshold, the insurance product is sent to the customer group. The technical solution provided by the present application is implemented to achieve the effect of improving the efficiency of policy trusteeship.

Description

Policy escrow method and system

Technical Field

The application relates to the technical field of data processing, in particular to a policy escrow method, a policy escrow system, electronic equipment and a storage medium.

Background

With the rapid development of insurance markets and the continuous improvement of people's insurance consciousness, policy escrow services gradually become an important component of the insurance industry. The policy escrow service aims to help clients to better manage and optimize the insurance guarantee, improve the utilization efficiency of insurance resources and provide value-added service opportunities for insurance companies.

At present, the existing policy hosting method mainly collects personal information and policy data of clients through an insurance broker, analyzes the guarantee content of each policy one by one, and then proposes policy optimization suggestions according to the personal requirements and risk conditions of the clients so as to recommend insurance products.

However, in practical applications, the existing policy hosting method is mostly concentrated on analysis of individual clients, when home policy analysis is required, workload is increased significantly, insurance brokers often need to consume a lot of time, and it is difficult to analyze policy data of a lot of clients rapidly to recommend insurance products to clients, so that policy hosting efficiency is low.

Disclosure of Invention

The application provides a policy escrow method, a policy escrow system, electronic equipment and a storage medium, which have the effect of improving the policy escrow efficiency.

In a first aspect, the present application provides a policy escrow method, including:

Acquiring policy data of a plurality of clients;

Identifying each policy data to obtain a plurality of client groups with the same family relationship, and extracting family policy data of each client group;

Constructing a family policy relation map based on the family policy data, wherein the family policy relation map comprises 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 policies;

In the family insurance policy relation map, calculating the current family insurance coverage rate of each client group by combining family information corresponding to each first-level node, member information corresponding to each second-level node and policy information corresponding to each third-level node;

and calculating the lifting amplitude of the coverage rate of the insurance products to the family insurance, and for each insurance product, if the lifting amplitude of the coverage rate of the insurance product is greater than an amplitude threshold, sending the insurance product to the corresponding customer group.

In a second aspect of the application there is provided a policy escrow system, the system comprising:

the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring policy data of a plurality of clients, identifying each policy data to obtain a plurality of client groups with the same family relationship, and extracting family policy data of each client group;

The relation map determining module is used for constructing a family policy relation map based on the family policy data, wherein the family policy relation map comprises 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 policies;

The coverage rate determining module is used for calculating the current family insurance coverage rate of each client group by combining family information corresponding to each first-level node, member information corresponding to each second-level node and policy information corresponding to each third-level node in the family policy relation map;

The insurance product pushing module is used for identifying repeated guarantee items and guarantee gap items of the client groups according to the family policy relation map, matching insurance products corresponding to the repeated guarantee items and the guarantee gap items of the client groups in a pre-established insurance product library, calculating the lifting amplitude of the family insurance coverage rate of the insurance products, and sending the insurance products to the corresponding client groups if the coverage rate lifting amplitude of the insurance products is larger than an amplitude threshold value for the insurance products.

In a third aspect of the present application, an electronic device is provided that includes a memory, a processor, and a program stored on the memory and executable on the processor, the program being capable of implementing a policy escrow method when loaded and executed by the processor.

In a fourth aspect of the application, a computer readable storage medium is provided, the computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a policy escrow method.

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

By adopting the technical scheme, the policy data of a plurality of clients are acquired and identified in a centralized way, the client groups with the same family relationship are divided accurately, and a multi-level family policy relationship map is constructed based on the family policy data. And combining family information, member information and policy information, systematically calculating the family insurance coverage, automatically identifying repeated guarantees and guarantee gaps, and intelligently matching and optimizing products in a preset insurance product library. Finally, by evaluating the coverage rate improvement range of the insurance products, the most suitable insurance products are accurately recommended, the family insurance combination is optimized, and the resource waste and the guarantee deficiency are reduced. Compared with the traditional insurance broker which needs to spend a great deal of time to analyze the policy data one by one, the processing time is shortened, and the policy hosting efficiency is improved.

Drawings

Fig. 1 is a schematic flow chart of a policy hosting method according to an embodiment of the present application;

Fig. 2 is a schematic structural diagram of a policy hosting system according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Reference numerals illustrate 300, electronic devices, 301, processors, 302, communication buses, 303, user interfaces, 304, network interfaces, 305, memories.

Detailed Description

In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.

In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.

In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The embodiment of the application provides a policy escrow method. In one embodiment, please refer to fig. 1, fig. 1 is a flowchart illustrating a policy hosting method according to an embodiment of the present application, which may be implemented by a computer program, and the computer program may be integrated into an application or may be run as a separate tool application. The method can be realized by depending on a singlechip, and can also be operated in a policy escrow system based on a von Neumann system. Specifically, the method may include the steps of:

Step 101, acquiring policy data of a plurality of clients, identifying each policy data to obtain a plurality of client groups with the same family relationship, and extracting family policy data of each client group.

The policy data refers to detailed information related to insurance products purchased by the clients, and in the embodiment of the present application, the policy data may be understood as all data information related to a specific policy, such as applicant information, insured person information, insurance company information, insurance period, insurance amount, insurance expense, etc., and is used to describe details of insurance products purchased by the clients.

The client group refers to a result of grouping a plurality of clients through family relations, and in the embodiment of the application, the classification of a plurality of members with relatives and corresponding policy data in a family purchasing insurance products can be understood to be used for the subsequent extraction of the family policy data and the analysis of the family insurance needs.

The family policy data refers to a set of policy information of all clients belonging to the same family, and in the embodiment of the present application, the result obtained by summarizing the policy data of all clients belonging to the same client group is used to describe the purchase condition and the guarantee condition of the whole family.

Specifically, to implement an automated hosting service of a home policy, policy data of a plurality of clients needs to be acquired. The policy data may originate from the business system database of the insurance company, or may be collected by the insurance agent via web forms or other means. After the client policy data is obtained, the policy data needs to be identified, and the applicant information and the insured person information in the policy data are mainly identified. The identification can be realized through text extraction of the policy data, and the name and the certificate information of the applicant, the name and the certificate information of the insured person and the like are extracted. Then, it is necessary to judge whether a family relationship exists between different policies according to the extracted applicant information and insured person information. Specifically, a customer relationship network may be established with the insured and insured as nodes, and if the insured of the insured is not itself, a connection of the home relationship between the two nodes is established. Node merging is performed by the same applicant or insured. Finally, a region where a plurality of connected nodes appear in the relational network corresponds to one home. After division according to the family relationship, all the policy data belonging to the same family can be extracted to form family policy data. Thus, the original customer policy data can be divided into a plurality of family policy data sets, and a foundation is laid for subsequent analysis and processing of the family policy.

On the basis of the above embodiment, as an optional embodiment, in step 101, identifying each policy data to obtain a plurality of client groups having the same family relationship, and extracting family policy data of each client group, the step may further include the following steps:

And 201, extracting the applicant information and the insured person information in the policy data, and constructing a customer relationship network based on the applicant information and the insured person information.

The information of the applicant and the information of the insured refer to relevant personal information used for describing the applicant and the insured in the policy data, and in the embodiment of the application, the information can be understood as information which can uniquely identify one person, such as names, identification numbers, contact ways and the like of the applicant and the insured, is used for judging whether family relations exist between the applicant and the insured among different policies, and finally identifies the policy belonging to the same family.

The client relationship network refers to a network diagram constructed by the association relationship between the applicant information and the insured person information, and in the embodiment of the present application, it can be understood that each applicant and insured person in the policy data are taken as nodes, and a network structure of the connection relationship between the nodes is established according to the family relationship, so as to represent the family relationship between different clients, and identify the family client group in the network.

Specifically, after information of the insurer and insured person of each policy data is acquired, a customer relationship network needs to be established based on the information. Each independent insurant and insured person can be set as a node, and if the insured person of the insurant is not the insured person, a connection line is established between the two corresponding nodes to indicate that a family relationship exists between the two nodes. For the same applicant or insured who appears in a plurality of policy data, it is necessary to combine its corresponding plurality of nodes into one node. Finally, a plurality of interconnected nodes aggregated in the relational network graph are a family or a relative circle, and all insurers and insured persons in the nodes have family relations, so that the family relations in the policy data can be identified, and a foundation is laid for the next step of family policy extraction and analysis.

On the basis of the above embodiment, as an optional embodiment, in step 201, a client relationship network is constructed based on each applicant information and each insured person information, which may further include the following steps:

Step 211, using the insurantor corresponding to each insurantor information and the insured corresponding to each insured information as a node in the customer relation network.

Specifically, the first step in constructing a customer relationship network is to take each individual applicant and insured in the policy data as one node in the network diagram, since the applicant and insured are the two most critical information points in determining the family relationship. Each insurer and each insured need to be added to the network graph as an independent individual to determine the family relationship between the nodes through subsequent node connection relationships. Creating a corresponding node for each unique applicant by traversing each piece of extracted applicant information; at the same time, each piece of insured information is traversed, and a corresponding node is also created for each unique insured. Finally, all nodes in the network diagram represent all unique insurers and insured individuals involved in the policy data, which lays a foundation for subsequent determination of the home relationship connections between the nodes to identify the home client group.

Step 221, establishing a connection relation between nodes to obtain a customer relation network based on a preset rule, wherein the preset rule is that if an insured person of a certain insurer is not the insured person, the connection relation is established between the insured person and the nodes corresponding to the insured person, and if a plurality of policy data correspond to the same insured person or the insured person, the same insured person or a plurality of nodes corresponding to the insured person are combined.

Specifically, when all the nodes of the insured person and the insured person are ready, the connection relationship between the nodes needs to be determined based on a preset family relationship judging rule, and finally a customer relationship network is formed. The purpose of this is to determine whether a particular two nodes have a home relationship by the presence or absence of a connection relationship. Only if a network diagram which correctly reflects the node relation is constructed, the network diagram can be used as the basis for the subsequent identification of the family client group. The preset family relationship judging rule is that if the insured person of an insurer is not himself/herself, it can be judged that the insurer and the corresponding insured person node have a family relationship, and a connecting line needs to be established between the two nodes. If the same applicant or insured is found to exist in multiple policy data, then the repeated nodes need to be merged to form a single node representing the common applicant/insured. And judging the relation between each node according to the rule, and finally forming a client relation network covering all the insurers and the insured. Thus, the family relation among each node is accurately reflected in the form of a network diagram, and a basis is provided for subsequent customer group identification and family policy extraction.

Step 202, identifying a plurality of client nodes with relatives in a client relationship network, and clustering the client nodes with the same relatives to obtain a plurality of client groups.

The plurality of client nodes with relatives refer to network nodes corresponding to a plurality of clients associated through family relations in a client relation network, and in the embodiment of the present application, the corresponding node representation of a plurality of family members who belong to the same family and purchase insurance products in a network diagram is understood to be used for representing a plurality of clients with family relations, so as to identify a family client group later.

Specifically, after a relationship network representing a family relationship of clients is constructed, it is necessary to identify client nodes having a relationship in the network and cluster them to form a client group. The method for identifying the relative client nodes is to observe the connection between the nodes in the relation network diagram, and the method is that all the two client nodes connected through one connecting line show that the two clients have relative relationships and belong to a family. And then, clustering all relevant client nodes in the relational network graph, and using the existing community discovery algorithm or clustering algorithm to enable the closely connected client nodes to form different groups, wherein each group is a family, and all family members which belong to the same family and purchase insurance products are contained in all the finally obtained client groups.

And 203, merging the policy data of a plurality of clients in each client group to obtain the home policy data of each client group.

Specifically, after obtaining the client groups representing the specific families, it is necessary to further integrate all the client policy data in each client group to form the family policy data. Home policy data is formed because subsequent policy analysis and insurance requirement assessment requires home as a basic unit rather than individual. It is not enough to see the policy of a certain family member, and it is necessary to know the current all security conditions of the family. The specific mode of forming the home policy data is to traverse each identified customer group, extract the policy data of all customers in the group, integrate the policy data, including information such as summarized insurance fee, insurance amount, product type, etc., and finally, each customer group corresponds to one home policy data representing the whole home insurance situation.

Step 102, constructing a family policy relation map based on the family policy data, wherein the family policy relation map comprises 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 policies.

The family policy relation map refers to a relation map for displaying the policy relation and the security structure inside the family by using a network map, and in the embodiment of the application, the family policy relation map can be understood as a multi-level network map comprising family nodes, family member nodes, policy nodes and the relation among the nodes, and is used for intuitively displaying the security state of each family and supporting subsequent policy analysis and optimization.

The first level node refers to a node representing the whole family entity in the family policy relation map, and in the embodiment of the present application, the node may be understood as an abstract node identifying each unique family, and is used to distinguish different families in the map.

The second level node refers to a node representing family members in a family policy relationship map, and in the embodiment of the present application, may be understood as a node representing each specific member belonging to a certain family, and is used to identify different members in the family in the map.

The third level node refers to a node representing a policy in a family policy relationship map, and in the embodiment of the present application, the node represents each policy purchased by a family member, and is used to identify each specific policy of the family member in the map.

Specifically, information extraction is performed on the family policy data to obtain family information, family member information and policy information. For each piece of extracted family information, a corresponding family node is created in the map, for each piece of member information, a member child node is created under the corresponding family node, for each piece of policy information, a policy child node is created under the corresponding member node, and the steps are repeated until all pieces of family policy data are modeled in the relationship map. Finally, a multi-level network map comprising family nodes, member nodes, policy nodes and relationships thereof is formed, and the security structures of all families are visually displayed. The method provides data support for subsequent guarantee analysis and policy optimization, and can more comprehensively understand the guarantee condition of families through a visualized policy relation map, thereby realizing the accurate policy hosting service for each family.

Based on the above embodiment, as an optional embodiment, in step 102, a family policy relationship map is constructed based on each family policy data, and this step may further include the following steps:

Step 301, performing text recognition on the home policy data, and extracting home information, member information and policy information in the home policy data.

The family information, the member information and the policy information refer to three types of node information required for constructing a family policy relation map, and in the embodiment of the application, the family information is understood to be information describing basic conditions of a family, such as family names, family addresses and the like. Member information, information describing family members, such as member names, contact addresses, etc. And the policy information is information describing specific conditions of the policy, such as policy numbers, premium, guarantee items and the like. The three are respectively used for creating a family node, a member node and a policy node in the map. The family information reflects an individual family, the member information reflects a specific member in the family, and the policy information reflects a policy purchased by the member.

Specifically, after the home policy data is obtained, it is necessary to extract home information, member information, and policy information therefrom by a text recognition technique. This is done because the home policy data contains a large amount of unstructured text information from which the structured information needed to construct the policy map needs to be identified. The three types of information are directly extracted from the text, so that node information can be automatically acquired, and the information is recorded one by one without manually checking the policy text. The method can use the existing NLP, rule matching and other modes to identify sentences describing families in the policy text as family information, identify sentences describing purchasers or insured persons as member information, and identify sentences describing the details of the policy as policy information. Necessary processing may then be performed, such as removing extraneous content, canonical names, and the like. And finally, structured family information, member information and policy information are obtained, structured node information can be rapidly extracted from massive unstructured policy data through a text recognition technology, the information acquisition efficiency is greatly improved, and a structured data source is provided for building a policy relation graph.

Step 302, first level nodes corresponding to each piece of family information are created, wherein each first level node corresponds to one family unit, second level nodes corresponding to each piece of member information are created, each second level node corresponds to one family member, third level nodes corresponding to each piece of policy information are created, and each third level node corresponds to one policy.

Specifically, after the family information, the member information and the policy information are acquired, corresponding three-level nodes need to be created for the three types of information in the map. The method is to establish a node system of three layers of families, members and policy in the map, and accurately reflect information entities in the family policy relationship. Each piece of extracted family information is traversed, and a new first-level family node is created by the family information, so that each first-level node corresponds to a unique family. Similarly, each piece of member information is traversed, and a new second-level member node is created under the first-level node of the corresponding family, so that each second-level node corresponds to one family member. And finally, traversing each piece of policy information, and creating a new third-level policy node under the second-level node of the corresponding member, so that each third-level node corresponds to one policy. Through the processing, a three-level node system representing different information entities is established in the map, and a foundation is laid for the next step of node connection.

Step 303, associating each first level node with a corresponding second level node, and associating the second level node with a corresponding third level node to generate a family policy relation map.

Specifically, when the three-level nodes are ready, the relationship between the nodes needs to be further established, and finally, a family policy relationship map is generated. The method is to connect logic relations among different information entities in the map to form a networked knowledge map, so that the internal relation of the home policy can be comprehensively reflected. Each first-level family node is traversed firstly, and all second-level member nodes corresponding to the first-level family node, namely the family, are found, and the first-level member nodes and the second-level member nodes are connected through the inclusion relation. And traversing each second-level member node, finding out all third-level policy nodes corresponding to the member node, namely all third-level policy nodes purchased by the member, connecting the member node and the third-level policy nodes through a purchase relationship, establishing relationships among information entities with different granularities in the atlas through the processing, and forming an abstract-to-concrete network topological structure, namely a final family policy relationship atlas.

And 103, in the family insurance policy relation map, combining family information corresponding to each first-level node, member information corresponding to each second-level node and policy information corresponding to each third-level node, and calculating the current family insurance coverage of each client group.

The family insurance coverage rate refers to an important index for evaluating the current guarantee state of a family, and in the embodiment of the application, the ratio of insurance premium actually purchased by the family to the recommended premium expenditure level can be understood to judge whether a guarantee gap and the gap size exist in the family or not, so that a basis is provided for the optimization of a follow-up policy.

Specifically, after a family policy relation map is constructed, the current insurance coverage of each family needs to be calculated based on the map, and the level of the security of the family is analyzed. This is done because insurance coverage is an important indicator for evaluating the status of a home security. The coverage rate of each household is calculated, whether the household has a guaranteed gap and the gap size can be judged, and a basis is provided for follow-up accurate recommendation. The method comprises the steps of counting the number of third-level policy nodes contained in each first-level family node in a map, calculating the sum of the premium, simultaneously referring to corresponding family information and member information, calculating the recommended premium expenditure level of the family, and finally obtaining the insurance coverage rate of the family according to the ratio of the actual premium to the recommended premium of the third-level policy nodes. The current insurance coverage level of each household can be obtained through calculation. The method can judge families with guarantee gaps in high-net-value customers, can find out the situation of excessive purchase of insurance, provides basis for follow-up policy optimization, calculates insurance coverage rate, realizes more intelligent family guarantee assessment, and is beneficial to follow-up custom guarantee plans for each family.

Based on the above embodiment, as an optional embodiment, in step 103, in the family policy relation map, the current family insurance coverage of each client group is calculated by combining family information corresponding to each first level node, member information corresponding to each second level node, and policy information corresponding to each third level node, which may further include the following steps:

Step 401, traversing each family information, determining the total annual income and the total value of family assets of the family units corresponding to the plurality of client groups, traversing each member information, determining the ages and professions of the plurality of family members in each family unit, traversing each policy information, and determining the total package amount of each family unit, the average risk type number of the existing policy coverage and the average age stage number of the existing policy coverage.

The total annual household income and the total household asset value refer to two key indexes for evaluating the financial condition of a household, and in the embodiment of the application, the total annual household income is the sum of all pre-tax incomes in one household in one year, and the total household asset value is the sum of the values of all assets owned by one household.

The total package amount, the average risk type number covered by the existing policy and the average age stage number covered by the existing policy refer to three key indexes for evaluating the configuration optimization degree of the home policy, and in the embodiment of the application, the total package amount is understood to be the sum of the insurance amounts of the existing policy of a home. The average number of risk types for existing policy coverage is the average number of life cycle risk categories for existing policy coverage for a plurality of family members in each home unit. The number of the current insurance policy covered at the average age stage is the average number of the current insurance policy covered at different age stages of a plurality of family members in each family unit, and the three are used for comprehensively evaluating whether the configuration of the current insurance policy is optimized and perfected and comparing the configuration with the optimized insurance policy to judge the effect of the insurance policy optimization.

Specifically, after a family policy relation map is built, each first-level family node needs to be traversed in the map to obtain information such as total household income, total asset value and the like so as to evaluate financial conditions of each family, each second-level member node is traversed to obtain information such as member ages, occupations and the like so as to judge security requirements, and each third-level policy node is traversed to obtain information such as total policy package, total coverage risk type number and the like so as to evaluate current security configuration. In order to extract all the characteristic information required by judging the guarantee optimization from the map, the accurate guarantee analysis is carried out for each family. The method comprises the steps of obtaining the attribute of each family node, obtaining the family income and the asset information, obtaining the attribute of each member node, obtaining the age and the occupation information, obtaining the information of the total package amount, the type number of the coverage risk and the like of each policy node, and obtaining the financial information, the population information and the security configuration information of each family from the relationship map through the traversal extraction to comprehensively evaluate the security current situation of each family.

Step 402, determining the ages and the personal risk coefficients corresponding to the professions of the plurality of family members in each family unit according to a preset risk coefficient table.

The preset risk coefficient table refers to a coefficient table generated in advance according to statistical data, and in the embodiment of the present application, the preset risk coefficient table may be understood as a lookup table including risk coefficients corresponding to different age groups and different professions, and the lookup table is used for searching for a matched risk coefficient after determining the age and the profession of each family member, and evaluating the insurance risk level faced by the member.

The personal risk coefficient refers to a quantitative index for evaluating the insurance risk degree faced by a family member, and in the embodiment of the application, the risk coefficient corresponding to a certain age group and occupation predetermined based on statistical analysis is used for representing the current risk level of the member, so as to provide basis for the subsequent determination of a reasonable guarantee target of the member.

Specifically, after the age and occupation information of each family member are acquired, a risk coefficient corresponding to each member needs to be determined according to a preset risk coefficient table. The method is characterized in that the risk coefficients of insurance corresponding to different age stages and professions are different, and standard risk coefficients are required to be obtained through table lookup, so that basic data is provided for subsequent calculation of personalized guarantee. A risk coefficient table obtained through statistics is preset, and the risk coefficient table comprises risk coefficients corresponding to different age groups and different professions. And traversing all member nodes in the family policy map, searching the age attribute and the occupation attribute of each member, searching the matched risk coefficient in the coefficient table and recording. Finally, each member node corresponds to a risk coefficient, reflects the risk level of the member, provides an important basis for calculating the personalized security target, obtains the standardized risk coefficient through table lookup, and can enable security calculation of each family member to provide an objective risk assessment basis, so that security optimization is more accurate.

Step 403, substituting the total annual income amount and the total asset value of each family unit, the personal risk coefficients corresponding to a plurality of family members in each family unit, the total package amount of each family unit, the average risk type number of the existing policy coverage and the average age stage number of the existing policy coverage into a preset formula to obtain the current family insurance coverage of each customer group, wherein the preset formula is as follows:

Wherein P _i represents the current family insurance coverage of the ith client group, ω ₁ represents a preset first weight coefficient, D _i represents the average risk type number of the existing policy coverage of the ith family unit, D ₀ represents the reference risk type number, ω ₂ represents a preset second weight coefficient, N _i represents the average age group number of the existing policy coverage of the ith family unit, N ₀ represents the reference age group number, ω ₃ represents a preset third weight coefficient, C _i represents the total insurance amount of the ith family unit, R _i,j represents the personal risk coefficient corresponding to the jth family member in the ith family unit, M _i represents the number of family members of the ith family unit, a _i represents the total annual income amount of the ith family unit, T represents the preset policy year, and B _i represents the total value of the household asset of the ith family unit.

The preset formula refers to a mathematical formula for calculating the current family insurance coverage of each client group. In the embodiment of the application, the preset formula can be understood as a multi-element analysis formula containing the total annual income and total household asset value of each household unit, the personal risk coefficient corresponding to a plurality of household members in each household unit, the total package of each household unit, the average risk type number covered by the existing policy, the average age stage number covered by the existing policy and other factors, and the preset formula is used for quantitatively calculating and evaluating the household insurance coverage, and the current household insurance coverage of each customer group can be calculated by substituting the actual detection data.

Specifically, after the family financial information, the member risk coefficient and the current guarantee configuration index are obtained, the family financial information, the member risk coefficient and the current guarantee configuration index are substituted into a preset formula to calculate the insurance coverage rate of each family. The method is to evaluate and judge the guarantee state of each family by using a preset calculation model, so as to obtain an accurate and objective insurance coverage result. Substituting the total household income amount, the total household asset value, the member risk coefficient, the total insurance policy package amount, the number of coverage risk categories and the like extracted by each household into a formula, and obtaining the coverage rate value of the household after calculation. The formula comprehensively considers the factors such as income, assets, risks, current guarantee and the like, calculates coverage rate results with reference significance, and can judge which families have guarantee gaps and which families have excessive guarantee after the coverage rate is obtained, so that a basis is provided for subsequent policy configuration optimization.

The formula consists of three parts, the first part describes the extent to which the average risk type number covered by the existing policy affects the current family insurance coverage of the customer group. Wherein, The ratio of the average risk type number to the reference risk type number of the existing policy coverage is represented, and the ratio reflects the coverage breadth of the home policy on different risk types. D _i represents the average number of risk types covered by the current policy of the household. For example, fire, theft, natural disaster, etc., D ₀ is a set reference risk type number for comparison and standardization. The more risk types the family policy covers, the higher the corresponding ratio, and the greater the contribution to the coverage of the insurance, and the index of the part helps to evaluate the comprehensiveness and effectiveness of the policy.

Illustratively, assume that the benchmark risk type number D ₀ is 5, meaning that a general policy typically covers 5 risk types. Home a, whose policy covers 3 risk types (fire, theft, flood), i.e. D _i is 3,0.6. Family B, whose policy covers 6 risk types (fire, theft, flood, earthquake, lightning strike, storm), i.e. D _i is 6, then1.2. The ratio of family a is 0.6, below the benchmark, meaning that its policy covers fewer risk types, with lower coverage, while the ratio of family B is 1.2, above the benchmark, meaning that its policy covers more risk types, with higher coverage.

The second section describes how the average age-stage number of existing policy coverage affects the current family coverage of the customer group.The ratio of the average age group number to the reference age group number of the existing policy coverage is represented. This reflects in part the coverage of the policy for different age phases. N _i represents the average age group number covered by the current policy of the family, e.g., four age groups of children, young, middle-aged, elderly, N ₀ represents the set reference age group number for standardized comparison. The more age stages of policy coverage, the higher the proportion, and the greater the contribution to insurance coverage, this partial index helps evaluate the comprehensiveness and adaptability of the policy.

Illustratively, assume that the reference age group number N ₀ is 4, meaning that the general policy covers 4 age groups, such as children, young, middle-aged, elderly. Family A, in which the policy covers 3 ages (children, young and middle-aged), i.e. N _i is 30.75. Family B, the policy covers 2 ages (young and middle aged), i.e. N _i is 20.5. The ratio of family A is 0.75, which shows that the coverage of the insurance policy to the age stage is more than that of family B, and is helpful for improving the coverage rate of insurance. The ratio of family B is 0.5, indicating that the policy covers fewer age groups than family a, with less impact on coverage.

The second section describes the total annual household income amount, total household asset value, personal risk coefficients corresponding to a plurality of household members in each household unit, and the total coverage of each household unit on the current household insurance coverage of the customer group.Representing a comprehensive consideration of economy and risk in the coverage of home insurance. C _i represents the total amount of the home insurance, i.e. the investment amount of the insurance, which determines the investment of the home on the insurance. Higher guarantees generally indicate better guarantees, i.e. higher home insurance coverage, whereas lower total guarantees, the smaller home insurance coverage. Σr _i,j represents the sum of individual risk factors of family members, the risk factors being considered based on health, occupation, etc., M _i represents the number of family members, for calculating an average risk factor,The average risk coefficient of each family member is higher, and the adjusted insurance amount is larger, so that the needs of the family are reflected. A _i represents the total annual revenue of the family and represents economic capacity. T represents the policy year and the duration of the insurance. B _i represents the total value of the household asset, representing the financial stability.The ratio comprehensively considers the insurance amount, the risk and the economic basis, and a high ratio indicates that the family still has stronger economic bearing capacity under the high risk, namely the reflected family insurance coverage rate is larger, and conversely, if the ratio of the part is smaller, the ratio indicates that the family has smaller risk bearing capacity, namely the family insurance coverage rate is smaller.

Illustratively, assume that the total guard C _i for household A is 1000000, the sum of risk factors ΣR _i,j is 10, the number of family members M _i is 4, the total annual household income A _i is 50000, the total household asset value B _i is 200000, and the guard period T is 5. Then the part representing the post-risk-adjustment guard350000, A portion (A _i×T+B_i) representing the basis of home economy of 450000The value of the fraction was 0.75. The ratio of family A is 0.75, which indicates the ratio of the insurance after risk adjustment to the economic basis of the family, and the larger the ratio is, the larger the coverage of the family insurance is reflected, and the smaller the ratio is, the smaller the coverage of the family insurance is reflected.

It should be noted that the weight coefficients ω ₁、ω₂ and ω ₃ in the formula are used to balance the influence of different factors on the total coverage, each coefficient reflects the importance of the factor in the overall calculation, and the sum of the three weight coefficients is 1. The weight coefficient is set by collecting a great amount of past insurance policy data to perform existing regression analysis and other methods, and the change degree of the household risk coverage rate caused by the total annual household income amount and the total household asset value of each household unit, the personal risk coefficient corresponding to a plurality of household members in each household unit, the total package amount of each household unit, the average risk type number of the existing insurance policy coverage and the average age stage number of the existing insurance policy coverage is analyzed and determined. For example, in an insurance company, according to market research and data analysis, the weight of omega ₁ is 0.4, the weight of risk type coverage is higher, because it directly affects the probability of claim settlement, the weight of omega ₂ coverage in the 0.3 age stage is moderate, and in consideration of the demand difference of different ages, omega ₃ is the weight of 0.3 economic factors, and is used for evaluating the payment capability and financial stability of families. In different families, young families may pay more attention to risk type coverage, so the weight of omega ₁ can be increased, while in high-income families, economic factors can be paid more attention to, the weight of omega ₃ is properly increased, in multi-generation homohousing families, family members cover a plurality of age stages, and the protection aiming at different ages is needed, the weight of omega ₂ can be increased, the coverage degree of a policy in different age stages can be improved, and the requirements of specific family structures are met.

And 104, identifying repeated guarantee items and guarantee gap items of each customer group according to the family policy relation map, and matching the repeated guarantee items and the corresponding insurance products of the guarantee gap items of each customer group in a pre-established insurance product library.

The repeated guarantee project refers to the situation that multiple insurance policies in a family repeatedly guarantee the same type of risk and age group, and in the embodiment of the application, the situation that multiple insurance policy nodes correspond to the same type of life cycle risk and age group in a family insurance policy relationship map can be understood.

The security gap item refers to the situation that security is insufficient corresponding to certain life cycle stages or risk types in a household, and in the embodiment of the application, the situation that no policy node corresponds to the age group or risk type in the household policy relation map is understood to be used for indicating the problem that blank and missing exist in household security.

The pre-established insurance product library refers to a database for recording various insurance product information, and in the embodiment of the application, the database can be understood as a structured data set containing detailed information of a plurality of insurance products, and the structured data set is used for quickly searching matched insurance products as a substitute or supplement after repeated guarantee items and guarantee gaps of a home policy are identified.

Insurance products refer to a guarantee solution designed by an insurance company for a certain type of risk or age group, and in the embodiment of the application, commercial insurance recorded in a product library and used for guaranteeing a specific risk or age group can be pre-established, so that more optimized guarantee selection is provided when repeated guarantee items or guarantee gaps in a home policy are identified.

Specifically, after the insurance coverage of each family is calculated, repeated guarantee items and guarantee gap items of each family need to be identified based on the constructed family policy relation map, and corresponding supplementary insurance products are found in the insurance product library. The method is used for indicating the problems of each family under the current guarantee configuration through the map analysis, and can directly match insurance products required by optimization, so that product support is provided for the subsequent generation of an optimization scheme. The distribution condition of the policy nodes is analyzed, and the stage and risk of the guarantee blank are found. The product library is then queried for insurance products that supplement these items and risks. And finally, repeated guarantee and guarantee gaps and corresponding supplementary products are marked under each home node, a product foundation is provided for generating personalized policy combination, and the guarantee problem of each home can be pointed out efficiently and accurately and a solution is provided based on analysis of a policy relation graph and product matching, so that the precise policy optimization of each client group is realized.

On the basis of the embodiment, as an optional embodiment, in step 104, the step of identifying the repeated guarantee items and the guarantee gap items of each client group according to the family policy relation map may further include the step of traversing a third-level node and a second-level node in the family policy relation map to determine a plurality of guarantee items and guarantee amounts of the guarantee items in each client group.

The guarantee item refers to specific content of a policy for guaranteeing a certain type of risk or age group, and in the embodiment of the application, the specific guarantee item in a certain insurance product corresponding to a third-level node in a family policy relationship map can be understood.

The guarantee amount refers to the money amount paid by the guarantee list to a certain guarantee item when an insurance accident occurs, and in the embodiment of the application, the money amount can be understood as the payment amount specified in the insurance product corresponding to the third-level node in the family guarantee relation map, and is used for representing the guarantee strength and the guarantee level of the guarantee item.

Specifically, after the insurance products required for optimization are matched, each third-level policy node and each second-level member node need to be traversed in the family policy relation map to obtain the current guarantee item and the corresponding guarantee amount. The method comprises the steps of obtaining the information of the security item, the security period, the security limit and the like of each security policy by reading the attribute of each security policy node, obtaining the information of the age, occupation, risk condition and the like of each member by reading the attribute of each member node, accurately obtaining the detailed security information of the family by traversing, including purchased products and corresponding security contents, providing information support for the subsequent generation of new security schemes, and laying a foundation for the establishment of an optimal solution based on individual information.

Step 502, traversing each guarantee item for each client group, taking the overlapped guarantee item as a repeated guarantee item of the client group if the overlapped guarantee item exists, and taking the guarantee item as a guarantee gap item if the guarantee amount is smaller than a guarantee amount threshold value.

Specifically, after the existing guarantee items and the guarantee amount of each household are obtained, each guarantee item needs to be traversed, and whether repeated guarantee and guarantee gaps exist or not is judged. This is done to identify configuration problems for each household at the specific security content level and provide guidance for subsequent policy optimization. The method comprises the steps of traversing the guarantee items of each family, judging the same risk and age group as repeated guarantee items if two guarantee lists provide guarantees for the same risk and age group, and determining the same as a guarantee gap item if the guarantee amount of a certain item is lower than a preset minimum guarantee amount threshold value. And finally, the repeated guarantee items and the guarantee gap items of each family can be defined in the item level, the subsequent regulation and optimization of the insurance policy are guided, and the policy configuration status is judged from the guarantee content level in this step, so that the optimization scheme can more accurately solve the specific guarantee problem.

Step 105, calculating the coverage rate improvement amplitude of each insurance product on the family insurance, and for each insurance product, if the coverage rate improvement amplitude of the insurance product is greater than the amplitude threshold, sending the insurance product to the corresponding customer group.

The improvement range refers to the increase proportion of the coverage rate of the family insurance relative to the original coverage rate after a certain insurance product is added, and in the embodiment of the application, the difference between the coverage rate of a certain candidate product added into the existing insurance policy of the family and the combined coverage rate of the original insurance policy accounts for the percentage of the original coverage rate, so that the effect of the candidate product on the improvement of the family guarantee is evaluated.

Specifically, after the alternative insurance products are matched, the lifting range of the family insurance coverage rate of each product needs to be calculated, and the products with larger lifting range are selected to be recommended to the corresponding families. The method is to select the product with the largest current guarantee lifting effect from the selectable products to recommend, so that the policy optimization is more accurate and effective. Each candidate product can be simulated to be added into the existing insurance policy combination of the family, the coverage rate is recalculated based on a formula, and the coverage rate is compared with the original coverage rate to obtain an improvement value. If the preset lifting amplitude threshold is met, recommending the product to the family. Finally, each household only can obtain the product recommendation which is suitable for the household and has the largest effect of guaranteeing the lifting, and personalized policy optimization is realized.

Based on the above embodiment, as an optional embodiment, in step 105, calculating the lifting amplitude of the coverage rate of each insurance product on the family insurance may further include the following steps:

step 601, obtaining the expected family insurance coverage rate of each customer group after the corresponding insurance product is adjusted.

The predicted family insurance coverage refers to a new insurance coverage level predicted to be possessed by a family after policy optimization adjustment is performed, and in the embodiment of the application, the predicted family insurance coverage is understood to be a new insurance coverage value predicted by using a calculation model after repeated guarantee products are replaced and supplementary products are added, and the new insurance coverage value is used for evaluating the effect of an optimization scheme and displaying the coverage level which can be achieved after adjustment.

Specifically, after a personalized insurance product is recommended for each home, the projected coverage of each home needs to be obtained based on the adjusted policy combination. This is done to evaluate the guarantee effect of the recommended solution, checking the specific improvement achieved by optimization. The adjusted estimated coverage rate can be obtained by removing the repeated guarantee products, adding the supplementary products, simulating the new policy combination after adjustment, and substituting the new policy combination into the formula for calculation. Compared with the original scheme, the novel policy combination enables the coverage rate of the home security to be obviously improved, the optimization effect is achieved, the optimization effect is calculated, the effectiveness of the scheme is ensured, and visual planning expectation is provided for clients.

Step 602, calculating coverage difference between the expected home insurance coverage and the home insurance coverage corresponding to each client group, and dividing the coverage difference corresponding to each client group by the home insurance coverage to obtain the promotion amplitude of each insurance product to the home insurance coverage.

Specifically, after calculating the estimated coverage of each household, it is necessary to further obtain the improvement amplitude compared with the original coverage. The method comprises the steps of intuitively evaluating the effect of the optimization scheme through quantitative increment, specifically, obtaining the absolute increment of the coverage rate of the family by differentiating the estimated coverage rate value after adjustment and the original coverage rate value, and then dividing the increment by the original coverage rate value to calculate the percentage improvement amplitude of the coverage rate brought by the adjustment scheme. Therefore, the improvement ratio of coverage rate can be guaranteed after the policy optimization of each family can be quantitatively displayed, and the assessment effect is facilitated.

Referring to fig. 2, the policy escrow system provided by the embodiment of the application comprises a data acquisition module, a relationship map determining module, a coverage rate determining module and an insurance product pushing module, wherein:

the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring policy data of a plurality of clients;

The insurance product pushing module is used for identifying repeated guarantee items and guarantee gap items of each customer group according to the family policy relation map, matching the repeated guarantee items of each customer group and insurance products corresponding to the guarantee gap items in the pre-established insurance product library, calculating the lifting range of each insurance product on family insurance coverage, and for each insurance product, if the lifting range of the coverage of the insurance product is larger than a range threshold, sending the insurance product to the corresponding customer group.

On the basis of the embodiment, the data acquisition module is further used for extracting the applicant information and the insured person information in the policy data, constructing a client relationship network based on the applicant information and the insured person information, identifying a plurality of client nodes with relatives in the client relationship network, clustering the client nodes with the same relatives to obtain a plurality of client groups, and merging the policy data of the clients in the client groups to obtain family policy data of the client groups.

On the basis of the embodiment, the data acquisition module is further used for taking the insulant corresponding to each insulant information and the insured corresponding to each insured information as one node in a client relationship network, and establishing a connection relationship between the nodes based on a preset rule to obtain the client relationship network, wherein the preset rule is that if the insured of a certain insulant is not an insulant, the connection relationship is established between the insulant and the node corresponding to the insured, and if a plurality of insurance policy data correspond to the same insulant or the insured, the same insulant or the plurality of nodes corresponding to the insured are combined.

On the basis of the embodiment, the relation map determining module is further used for carrying out text recognition on the household policy data, extracting household information, member information and policy information in the household policy data, creating first level nodes corresponding to the household information, creating second level nodes corresponding to the member information, creating third level nodes corresponding to the policy information, associating the first level nodes with the corresponding second level nodes, associating the second level nodes with the corresponding third level nodes, and generating a household policy relation map.

On the basis of the embodiment, the coverage rate determining module is further configured to traverse each family information, determine a total annual household income amount and a total household asset value of family units corresponding to the plurality of client groups, traverse each member information, determine ages and professions of the plurality of family members in each family unit, traverse each policy information, determine a total package amount of each family unit, an average risk type number covered by an existing policy, and an average age stage number covered by an existing policy, determine personal risk coefficients corresponding to ages and professions of the plurality of family members in each family unit according to a preset risk coefficient table, and substitute the total annual household income amount and the total household asset value of each family unit, the personal risk coefficients corresponding to the plurality of family members in each family unit, and the total package amount of each family unit, the average risk type number covered by the existing policy, and the average age stage number covered by the existing policy into a preset formula to obtain the current family coverage rate of each client group, wherein the preset formula is as follows:

On the basis of the embodiment, the insurance product pushing module is further used for traversing a third-level node and a second-level node in the family policy relation map to determine a plurality of security items in each client group and security amounts of the security items, traversing each security item for each client group, taking the overlapped security items as repeated security items of the client group if the overlapped security items exist, and taking the security items as security gap items if the security amounts are smaller than security amount threshold values.

On the basis of the embodiment, the insurance product pushing module is further used for obtaining the expected family insurance coverage of each customer group after the corresponding insurance products are adjusted, calculating the coverage difference between the expected family insurance coverage and the family insurance coverage corresponding to each customer group, and dividing the coverage difference corresponding to each customer group by the family insurance coverage to obtain the promotion amplitude of each insurance product to the family insurance coverage.

It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be implemented by different functional modules, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.

The application also discloses electronic equipment. Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. 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.

Wherein the communication bus 302 is used to enable connected communication between these components.

The user interface 303 may include a Display screen (Display) interface and a Camera (Camera) interface, and the optional user interface 303 may further include a standard wired interface and a standard wireless interface.

The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may 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), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface diagram, an application program and the like, the GPU is used for rendering and drawing contents required to be displayed by the display screen, and the modem is used for processing wireless communication. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.

The Memory 305 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area that may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the respective method embodiments described above, etc., and a stored data area that may store data, etc., involved in the respective method embodiments described above. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. Referring to fig. 3, an operating system, a network communication module, a user interface module, and an application program of a policy escrow method may be included in the memory 305 as a computer storage medium.

In the electronic device 300 shown in fig. 3, the user interface 303 is primarily used to provide an input interface for a user to obtain data entered by the user, while the processor 301 may be used to invoke an application program in the memory 305 that stores a policy hosting method, which when executed by the one or more processors 301, causes the electronic device 300 to perform the method as in one or more of the embodiments described above. It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. The memory includes various media capable of storing program codes, such as a USB flash disk, a mobile hard disk, a magnetic disk or an optical disk.

The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.

This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. The specification and examples are to be regarded in an illustrative manner only.

Claims

1. A policy escrow method, comprising:

Acquiring policy data of a plurality of clients;

2. The policy escrow method according to claim 1, wherein the identifying each policy data to obtain a plurality of client groups having the same family relationship, and extracting family policy data of each client group, includes:

extracting applicant information and insured person information in the policy data, and constructing a customer relationship network based on the applicant information and the insured person information;

identifying, in the customer relationship network, a plurality of customer nodes having a relationship;

Clustering the client nodes with the same relative relationship to obtain a plurality of client groups;

and merging the policy data of a plurality of clients in each client group to obtain the family policy data of each client group.

3. The policy escrow method of claim 2, wherein constructing a customer relationship network based on each of the applicant information and each of the insured information includes:

Taking the insurant corresponding to each insurant information and the insured corresponding to each insured information as a node in the client relationship network;

establishing a connection relation between the nodes based on a preset rule to obtain a client relation network;

Wherein, the preset rule is:

if the insured person of a certain insuring person is not the insuring person, the connection relation is established between the insuring person and the node corresponding to the insured person;

If a plurality of insurance policy data correspond to the same applicant or insured person, merging a plurality of nodes corresponding to the same applicant or insured person.

4. The policy escrow method according to claim 1, wherein the constructing a family policy relation map based on each of the family policy data includes:

text recognition is carried out on each piece of home policy data, and home information, member information and policy information in each piece of home policy data are extracted;

Creating first hierarchical nodes corresponding to the family information, wherein each first hierarchical node corresponds to a family unit, creating second hierarchical nodes corresponding to the member information, wherein each second hierarchical node corresponds to a family member, and creating third hierarchical nodes corresponding to the policy information, wherein each third hierarchical node corresponds to a policy;

and associating each first level node with a corresponding second level node, and associating the second level node with a corresponding third level node to generate a family policy relation map.

5. The policy hosting method according to claim 1, wherein the calculating the current family insurance coverage of each client 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 includes:

Traversing each piece of family information, determining the total annual household income and the total household asset value of family units corresponding to a plurality of customer groups, traversing each piece of member information, determining the ages and occupations of a plurality of family members in each family unit, traversing each piece of policy information, and determining the total package amount of each family unit, the average risk type number covered by the existing policy and the average age stage number covered by the existing policy;

determining personal risk coefficients corresponding to ages and occupations of a plurality of family members in each family unit according to a preset risk coefficient table;

Substituting the total annual income amount and the total asset value of each family unit, the personal risk coefficients corresponding to a plurality of family members in each family unit, the total package amount of each family unit, the average risk type number of the existing policy coverage and the average age stage number of the existing policy coverage into a preset formula to obtain the current family insurance coverage of each client group, wherein the preset formula is as follows:

6. The policy escrow method according to claim 1, wherein the identifying duplicate security items and security breach items for each of the client groups according to the family policy relationship map includes:

Traversing a third level node and a second level node in the family policy relation graph to determine a plurality of guarantee items in each client group and the guarantee amount of each guarantee item;

Traversing each guarantee item for each client group, and taking the overlapped guarantee item as a repeated guarantee item of the client group if the overlapped guarantee item exists;

And if the guarantee items with the guarantee amount smaller than the threshold value of the guarantee amount exist, the guarantee items are used as guarantee gap items.

7. The policy escrow method of claim 1, wherein calculating the magnitude of the increase in coverage of home insurance by each of the insurance products includes:

acquiring the estimated family insurance coverage rate of each customer group after the corresponding insurance product is adjusted;

Calculating coverage rate difference between the expected home insurance coverage rate and the home insurance coverage rate corresponding to each client group;

And dividing the coverage rate difference value corresponding to each client group by the family insurance coverage rate to obtain the promotion amplitude of each insurance product to the family insurance coverage rate.

8. A policy escrow system, the system comprising:

9. An electronic device comprising a processor, a memory, a user interface, and a network interface, the memory for storing instructions, the user interface and the network interface for communicating to other devices, the processor for executing the instructions stored in the memory to cause the electronic device to perform a policy escrow method as claimed in any of claims 1-7.

10. A computer readable storage medium storing instructions which, when executed, perform a policy escrow method as claimed in any one of claims 1-7.