Knowledge graph construction and application method based on insurance industry

Abstract

The invention discloses a knowledge graph construction and application method based on the insurance industry, and belongs to the technical field of industry knowledge graphs. Comprising the following steps: S1, data collection and arrangement; collecting multi-source data of the insurance industry, and sorting the data; s2, knowledge modeling: defining an entity, determining a relationship, and performing attribute setting; s3, constructing a map: performing entity recognition and disambiguation, performing relation extraction, and storing a map database; s4, map optimization: adding semantic information, optimizing a relationship and creating an index; and S5, applying the atlas. According to the invention, multi-source data of the insurance industry is collected, and more comprehensive and accurate information support is provided for insurance business; according to the technical scheme, multiple application methods of the knowledge graph are provided, insurance business links such as risk assessment and management, customer portrait and precision marketing, intelligent customer service and knowledge questions and answers, product design and optimization, claim settlement management and the like are covered, the practical value of the knowledge graph is realized, and the efficiency and effect of each business link can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of industry knowledge graph technology, and in particular to a knowledge graph construction and application method based on the insurance industry. Background Art

[0002] The insurance industry is a field that is highly dependent on information and data analysis. With the development of information technology, insurance companies have accumulated a large amount of unstructured or semi-structured information such as customer data, transaction records, and claims cases; traditional methods are difficult to effectively mine the value of this data. As a new form of information representation, knowledge graphs can help insurance companies understand their business environment more deeply, optimize decision-making processes, and improve customer service experience.

[0003] Therefore, the applicant gathered relevant personnel from various departments of the company and designed a knowledge graph construction and application method based on the insurance industry to better serve the insurance industry. Summary of the invention

[0004] The purpose of this invention is to propose a knowledge graph construction and application method based on the insurance industry to better serve the insurance industry.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A knowledge graph construction and application method based on the insurance industry, including.

[0007] The following steps are involved:

[0008] S1. Data collection and collation: Collect multi-source data of the insurance industry and collate the data;

[0009] S2. Knowledge modeling: define entities, determine relationships, and set attributes;

[0010] S3, graph construction: entity recognition and disambiguation, relationship extraction, graph database storage;

[0011] S4, graph optimization: add semantic information, optimize relationships, and create indexes;

[0012] S5. Graph applications: risk assessment and management, customer profiling and precision marketing, intelligent customer service and knowledge Q&A, product design and optimization, and claims management.

[0013] Preferably, before step S1, a preparation stage is also included;

[0014] ①. Demand analysis: Conduct in-depth communication with company departments to understand their needs and expectations for knowledge graphs, and clarify the application scenarios and goals of knowledge graphs;

[0015] ②. Team building: Establish a cross-departmental team and clarify the responsibilities and division of labor of each member;

[0016] ③. Technology selection: Select appropriate technology components based on project requirements and budget;

[0017] ④. Develop a data collection plan.

[0018] Preferably, in step S2, it includes:

[0019] S201. Define entities: Identify and define various entities based on the business characteristics and needs of the insurance industry;

[0020] S202. Determine the relationship: clarify the relationship type between entities;

[0021] S203, attribute setting: Add corresponding attributes to each entity and relationship to enrich knowledge representation.

[0022] Preferably, in step S3, it includes:

[0023] S301, Entity Recognition and Disambiguation: Use natural language processing technology to identify entities from text data, disambiguate synonymous entities through deep learning models or rule engines, and unify them into one node;

[0024] S302, relationship extraction: based on the identified entities, extract the relationship between entities through rules or deep learning algorithms;

[0025] S303, graph database storage: select a suitable graph database to store the extracted entities and relationships in it; use the query language of the graph database to create nodes and relationships, and assign values ​​to the attributes of the nodes and relationships.

[0026] The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that, in step S4, it includes:

[0027] S401, adding semantic information: further enriching the semantic information of the knowledge graph;

[0028] S402, optimize relationships: optimize the relationships in the graph, remove redundant relationships, and supplement missing relationships;

[0029] S403, create index: create index for commonly used query attributes in the graph database.

[0030] Preferably, step S4 further includes: quantitative evaluation and optimization;

[0031] Accuracy:

[0032]

[0033] Recall:

[0034]

[0035] F1 score:

[0036]

[0037] Spectrum redundancy assessment:

[0038]

[0039] Graph optimization strategy: adjust the algorithm parameters of entity recognition and relationship extraction according to accuracy and recall; use methods such as cluster analysis to identify and merge redundant nodes and relationships; continuously optimize graph structure and content through user feedback and expert evaluation.

[0040] Preferably, in step S5, the risk assessment and management includes:

[0041] a1. Risk prediction: Build a risk knowledge graph, integrate and analyze risk data, historical cases and market intelligence; segment industries, establish a relationship model between industries, and use machine learning algorithms to discover the correlation between industries;

[0042] a2. Risk public opinion monitoring: mining the associations of insurance companies to achieve semantically accurate public opinion warnings, helping to obtain first-hand information on risk public opinion so as to take timely response measures;

[0043] a3. Anti-fraud: Apply knowledge graph technology to integrate the basic information of policyholders and beneficiaries for in-depth analysis and prediction; improve the accuracy and efficiency of fraud detection by building a knowledge graph model for fraud detection and risk control, identify and respond to potential fraud in a timely manner, and protect business interests and customer interests.

[0044] Preferably, in step S5, the customer profiling and precision marketing include:

[0045] b1. Customer portrait construction: Build detailed customer portraits by analyzing customer letter data; associate customer entities with relevant attributes and behaviors in the knowledge graph;

[0046] b2. Accurate product recommendations: Based on customer profiles and insurance product knowledge graphs, we provide customers with personalized insurance product recommendations. We analyze customers’ potential needs through recommendation algorithms and recommend the most suitable insurance products to improve sales efficiency and customer satisfaction.

[0047] b3. Customer relationship management: Use knowledge graphs to analyze relationships between customers; use these relationships to tap into potential customer resources, achieve cross-selling and maintain customer relationships;

[0048] In the construction of b1 customer portrait, customer value assessment is also included;

[0049] Adopt RFM model: evaluate customer value based on the customer's most recent purchase time (recency), purchase frequency (frequency) and purchase amount (monetary);

[0050] CustomerValue=α×Recency+β×Frequency+γ×Monetary

[0051] Among them, α, β, and γ are weight coefficients;

[0052] In b2 precise product recommendation, it also includes recommendation algorithm evaluation;

[0053] Precision@K: The proportion of products actually purchased by users among the first K products in the recommended list.

[0054]

[0055] Recall @ K: The proportion of products recommended to the top K products among the products actually purchased by the user.

[0056]

[0057] Preferably, in step S5, the intelligent customer service and knowledge question and answer include:

[0058] c1. Intelligent question-answering system: Build an intelligent question-answering system based on knowledge graphs, and use natural language processing technology and semantic understanding technology to accurately understand and quickly answer customer questions;

[0059] c2. Knowledge push: Actively push useful information to customers based on their consultation records and relevant knowledge in the knowledge graph;

[0060] c3. Customer service training: Use knowledge graphs to provide training materials and knowledge support for customer service personnel;

[0061] Also included: Intelligent question and answer assessment;

[0062] Semantic matching: The accuracy of the question-answering system is evaluated by calculating the semantic similarity between the user's question and the answer in the knowledge graph; cosine similarity is used

[0063]

[0064] in, are the vector representations of user questions and answers, respectively.

[0065] Preferably, in step S5, the product design and optimization includes:

[0066] d1. Market analysis: Analyze market dynamics, competitor product information, and changes in customer demand through knowledge graphs to provide a basis for the design and optimization of insurance products; understand the characteristics of popular products in the market, the coverage and compensation conditions that customers are concerned about, so as to develop more competitive insurance products;

[0067] d2. Product pricing: Combine risk assessment knowledge graphs and market data to accurately price insurance products; consider risk factors, customer groups, and market competition factors to formulate reasonable premium standards and improve product profitability and market competitiveness;

[0068] d3. Product portfolio recommendation: Recommend insurance product portfolios based on customer needs and risk preferences; analyze the complementarity and correlation between different insurance products through knowledge graphs to provide customers with one-stop insurance solutions to meet their diverse protection needs.

[0069] Preferably, in step S5, the claims management includes:

[0070] e1. Claims process optimization: Use knowledge graphs to model and analyze the claims process, identify bottlenecks and problems in the process, and make optimization suggestions; visualize the claims process to help claims personnel better understand the process and improve work efficiency;

[0071] e2. Claims risk assessment: Combined with the risk knowledge graph, risk assessment of claims cases is conducted; the relationship between claims cases and risk events is analyzed, potential fraud risks and moral risks are identified, and the fairness and rationality of claims are ensured;

[0072] Among them, claims fraud detection uses machine learning algorithms to build fraud detection models and calculate fraud probability.

[0073] FraudProbability=f(ClaimData,KnowledgeGraph);

[0074] f is the fraud detection model, ClaimData is the claims data, and KnowledgeGraph is the knowledge graph.

[0075] Compared with the prior art, the present invention provides a knowledge graph construction and application method based on the insurance industry, which has the following beneficial effects.

[0076] 1. The present invention collects multi-source data of the insurance industry, and the subsequent knowledge graph construction can make full use of rich data resources to provide more comprehensive and accurate information support for insurance business; the insurance knowledge is presented in a structured form, so that the complex relationships and information in the insurance business can be clearly displayed, which is easy to understand and query, and helps to quickly grasp the overall picture of the business and improve decision-making efficiency.

[0077] 2. The present invention provides a variety of knowledge graph application methods, covering insurance business links such as risk assessment and management, customer profiling and precision marketing, intelligent customer service and knowledge question and answer, product design and optimization, and claims management, realizing the practical value of knowledge graphs; it can effectively improve the efficiency and effectiveness of each business link.

[0078] 3. The present invention can better meet customer needs through accurate customer portraits and personalized product recommendations; construct a fraud detection model to more effectively identify fraudulent behavior; the knowledge graph provides data support and inspiration for product innovation, encouraging insurance companies to continuously explore new insurance products and service models to adapt to market changes and diversified customer needs.

[0079] Other advantages, objectives and features of the present invention will be described in part in the following description; and in part, will be apparent to those skilled in the art based on an examination of the following; or, may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0082] Reference Figure 1 , a knowledge graph construction and application method based on the insurance industry, including the following steps: S1, data collection and organization → S2, knowledge modeling → S3, graph construction → S4, graph optimization.

[0083] In step S1 data collection and collation:

[0084] Collect multi-source data in the insurance industry and organize the data.

[0085] S101. Collect multi-source data in the insurance industry: including but not limited to insurance contracts, customer consultation records, claims reports, policies and regulations, industry news, market research reports, etc. These data come from within the company, such as customer relationship management systems (CRMs), claims management systems, etc.; or from external public data sources, such as industry data, market trends, changes in laws and regulations, and socio-economic indicators.

[0086] The multi-source data also includes: user reviews and discussions on relevant insurance products;

[0087] It can be captured through interactive information of its own platform, or obtained from public data and third-party cooperation platforms.

[0088] S102. Data collation: Clean and pre-process the collected data, remove irrelevant information, standardize the format, correct errors, fill in missing values, etc. For example, unify the date format and normalize the customer names to ensure the quality and consistency of the data.

[0089] It should be noted that:

[0090] The accuracy, completeness and consistency of data are crucial to the quality of the knowledge graph; if the data is erroneous, missing or inconsistent, it will affect the construction and application of the knowledge graph; in addition, insurance data involves customers' personal privacy and sensitive information, and the collection, storage and use of data must comply with relevant laws and regulations and privacy policy requirements; a strict data quality management process should be established to conduct multiple rounds of cleaning, verification and review of data to ensure data quality; technical means such as data encryption, access control, and anonymization processing should be used to protect the privacy and security of customer data; at the same time, strengthen cooperation with data providers to ensure the legal acquisition and use of data.

[0091] Before step S1 of constructing the knowledge graph, a preparation stage is also included.

[0092] ①. Demand analysis: Conduct in-depth communication with the company's business department, customer service department, claims department, etc. to understand the needs and expectations of each department for the knowledge graph, and clarify the application scenarios and goals of the knowledge graph;

[0093] ②. Team building: Establish a cross-departmental project team, including business experts, data scientists, NLP engineers, graph database experts, etc., clarify the responsibilities and division of labor of each member, and ensure the smooth progress of the project;

[0094] ③. Technology selection: According to project requirements and budget, select appropriate NLP tools, graph databases, machine learning frameworks and other technical components to build a technical architecture for knowledge graph construction and application;

[0095] ④. Develop a data collection plan to provide a basic plan for subsequent knowledge graph construction.

[0096] In step S2 knowledge modeling:

[0097] Define entities, determine relationships, and set attributes.

[0098] S201. Define entities: Identify and define various entities based on the business characteristics and needs of the insurance industry.

[0099] Common entities include: insurance products (such as life insurance, property insurance, health insurance, etc.), customers (insured, insured, beneficiary, etc.), insurance companies, claims cases, risk events, laws and regulations, etc.

[0100] S202. Determine the relationship: clarify the relationship type between entities.

[0101] For example, the "inclusion" relationship between insurance products and the coverage, the "purchase" relationship between customers and insurance products, the "association" relationship between claims cases and insurance products, the "trigger" relationship between risk events and insurance products, etc.

[0102] S203, attribute setting: Add corresponding attributes to each entity and relationship to enrich knowledge representation.

[0103] For example, an insurance product entity has the attributes of premium, coverage period, and compensation amount; a client entity has the attributes of age, gender, occupation, and health status; a claim case entity has the attributes of claim amount, claim time, and claim status.

[0104] Among them, natural language processing (NLP) technology is used;

[0105] Extract useful information such as entities, relations and attributes from large amounts of text data;

[0106] Named Entity Recognition (NER) technology is used to identify entities in the text, such as insurance product names, customer names, etc. Relationship extraction technology is used to identify the relationships between entities, such as "purchase" and "claims".

[0107] It should be noted that: define entities, relationships and attributes of the insurance industry based on business needs and data characteristics; business experts and data scientists should be organized to conduct multiple discussions and modifications to ensure the accuracy and completeness of the model;

[0108] In step S3, constructing the graph:

[0109] Entity recognition and disambiguation, relationship extraction, graph database storage.

[0110] S301, Entity Recognition and Disambiguation:

[0111] Using natural language processing (NLP) technology, entities are identified from text data, and synonymous entities are disambiguated through deep learning models or rule engines and unified into one node. For example, "critical illness insurance" and "major disease insurance" are identified as the same entity.

[0112] S302, Relationship Extraction:

[0113] Based on the identified entities, the relationships between entities are extracted through rules or deep learning algorithms.

[0114] It is also possible to combine the knowledge of domain experts and define some heuristic rules to assist in relationship extraction; for example, "Policyholder XXX purchased insurance product YYY" can extract the "purchase" relationship.

[0115] Among them, deep learning models, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), are used to extract relations from text data; the relationship patterns between entities in the text are learned by training the model to improve the accuracy and recall of relationship extraction.

[0116] It should be noted that in the process of extracting entities and relationships from data and performing disambiguation and integration, the model parameters and algorithms are continuously optimized to improve the accuracy and efficiency of extraction.

[0117] S303, graph database storage:

[0118] Select a suitable graph database, such as Neo4j, to store the extracted entities and relationships; use the query language of the graph database (such as Cypher) to create nodes and relationships, and assign values ​​to the properties of nodes and relationships. For example, use the Cypher language to create insurance product nodes and customer nodes, and establish a purchase relationship between them.

[0119] By using a graph database (such as Neo4j) and defining the properties of nodes and edges, insurance knowledge can be stored in a structured form in the database for easy query and display; using the query language of the graph database (such as Cypher), the knowledge graph can be quickly queried and analyzed. For example, all insurance products, claims cases and risk events related to a certain customer can be found, or the sales of a certain insurance product among different customer groups can be analyzed.

[0120] In step S4, map optimization:

[0121] Add semantic information, optimize relationships, and create indexes.

[0122] S401. Add semantic information:

[0123] Further enrich the semantic information of the knowledge graph; for example, add more detailed descriptions for entities and relationships, define the categories and hierarchies of entities, etc., to improve the comprehensibility and usability of the graph.

[0124] S402, Optimize relationship:

[0125] Optimize the relationships in the graph, remove redundant relationships, supplement missing relationships, and ensure the accuracy and completeness of the relationships; discover potential relationships by analyzing the statistical laws of the data or using machine learning algorithms.

[0126] S403. Create index:

[0127] Create indexes for commonly used query attributes in the graph database; for example, create indexes for the name attribute of the customer entity and the product name attribute of the insurance product entity to improve query efficiency.

[0128] It should be noted that: the extracted entities and relationships are stored in a graph database to build a preliminary knowledge graph; the knowledge graph is optimized by adding semantic information, optimizing relationships, creating indexes, and other operations to improve its quality and performance; in addition, business experts are invited to verify and evaluate the constructed knowledge graph to check whether the entities, relationships, and attributes in the graph are accurate and complete, and whether they can meet business needs; based on the evaluation results, the knowledge graph is adjusted and optimized.

[0129] Step S4 of the graph optimization includes: quantitative evaluation and optimization.

[0130] Graph quantitative evaluation indicators:

[0131] Precision: The ratio of the number of correctly extracted entities / relations to the total number of extractions

[0132]

[0133] Recall: The ratio of the number of correctly extracted entities / relations to the number of actually existing entities / relations.

[0134]

[0135] F1 Score: The harmonic mean of precision and recall, used to comprehensively evaluate model performance.

[0136]

[0137] Graph redundancy ratio evaluation: Redundancy is evaluated by calculating the ratio of unnecessary repeated relationships or nodes in the graph.

[0138]

[0139] Graph optimization strategy:

[0140] Adjust the algorithm parameters of entity recognition and relationship extraction based on precision and recall;

[0141] Identify and merge redundant nodes and relationships using methods such as cluster analysis;

[0142] Continuously optimize the graph structure and content through user feedback and expert evaluation.

[0143] It also includes: operations and maintenance;

[0144] Establish a data update mechanism, regularly collect the latest data from data sources, and update and maintain the knowledge graph to ensure that the information in the graph is always up-to-date and accurate; at the same time, promptly correct errors and inconsistent information in the graph.

[0145] It should be noted that:

[0146] The insurance industry is complex and involves numerous entities, relationships, and attributes. The construction of a knowledge graph requires a lot of expertise and experience. We need to form an interdisciplinary project team to give full play to the advantages of business experts and data scientists and jointly solve problems in the construction of the knowledge graph. We need to use a combination of multiple technologies and methods, such as combining rule engines with machine learning algorithms to improve the accuracy of entity recognition and relationship extraction. We need to regularly evaluate and optimize the knowledge graph, and adjust the structure and content of the knowledge graph in a timely manner according to business development and data changes.

[0147] In step S5, it includes: risk assessment and management, customer profiling and precision marketing, intelligent customer service and knowledge Q&A, product design and optimization, and claims management.

[0148] A. Risk assessment and management.

[0149] a1. Risk prediction: Build a risk knowledge graph, integrate and analyze risk data, historical cases and market intelligence; segment industries, establish relationship models between industries, and use machine learning algorithms to discover the correlation between industries; when risks or high-risk events occur in a certain industry, timely predict other industries with potential risks based on the correlation relationship, thereby helping insurance companies make predictions and avoid risks as early as possible.

[0150] a2. Risk public opinion monitoring: Mining the relationships among insurance companies to achieve semantically accurate public opinion warnings, helping to obtain first-hand information on risk public opinion so that timely response measures can be taken.

[0151] a3. Anti-fraud: Apply knowledge graph technology to integrate the basic information of policyholders and beneficiaries, such as consumption records, behavior records, relationship information, online log information, etc., for in-depth analysis and prediction; improve the accuracy and efficiency of fraud detection by building a knowledge graph model for fraud detection and risk control, identify and respond to potential fraud in a timely manner, and protect business interests and customer interests.

[0152] This application has at least the following advantages:

[0153] Improved accuracy of risk prediction: Through the integration and analysis of risk data through knowledge graphs, it is possible to more accurately predict industry risks, customer risks, etc., and take measures in advance to avoid risks and reduce risk losses. For example, for customers in high-risk industries, insurance terms can be adjusted or premiums can be increased in advance to reduce potential compensation risks.

[0154] Enhanced anti-fraud capabilities: The fraud detection model built using knowledge graphs can more effectively identify fraudulent behavior and improve the accuracy and timeliness of fraud detection; reduce compensation losses caused by fraud, protect the interests of insurance companies, and maintain the fairness and stability of the insurance market.

[0155] In the a1 risk profile:

[0156] The performance of the classification model is evaluated by the AUC-ROC curve; the closer the AUC value is to 1, the better the model performance;

[0157] The KS statistic is used to measure the difference in the distribution of positive and negative samples and to evaluate the model's ability to distinguish;

[0158] Calculate risk scores for each customer or industry based on historical data and risk knowledge graph;

[0159]

[0160] Among them, w i is the weight of the risk factor, RiskFactor i is the value of the ith risk factor.

[0161] B. Customer profiling and precision marketing.

[0162] b1. Customer portrait construction: Build a detailed customer portrait by analyzing customer information, purchase records, consultation records and other data; in the knowledge graph, associate customer entities with relevant attributes and behaviors, such as the customer's age, gender, occupation, income level, risk preference, purchase history, etc., to form a comprehensive understanding of the customer.

[0163] b2. Accurate product recommendations: Based on customer portraits and combined with insurance product knowledge graphs, we provide customers with personalized insurance product recommendations; we analyze customers’ potential needs through recommendation algorithms, recommend the most suitable insurance products, and improve sales efficiency and customer satisfaction.

[0164] b3. Customer relationship management: Use knowledge graphs to analyze relationships between customers, such as family relationships, friendships, colleague relationships, etc., as well as relationships between customers and insurance agents; through these relationships, tap into potential customer resources to achieve cross-selling and maintain customer relationships.

[0165] In the construction of b1 customer portrait, customer value assessment is also included.

[0166] Adopt RFM model: evaluate customer value based on the customer's most recent purchase time (recency), purchase frequency (frequency) and purchase amount (monetary);

[0167] CustomerValue=α×Recency+β×Frequency+γ×Monetary;

[0168] Among them, α, β, and γ are weight coefficients.

[0169] B2 precise product recommendations also include recommendation algorithm evaluation.

[0170] Precision@K: The proportion of products actually purchased by users among the first K products in the recommended list.

[0171]

[0172] Recall @ K: The proportion of products recommended to the top K products among the products actually purchased by the user.

[0173]

[0174] This application has at least the following advantages:

[0175] Improved customer satisfaction: Through accurate customer profiles and personalized product recommendations, we can better meet customer needs and increase customer purchase willingness and satisfaction; customers can find insurance products that suit them more quickly, reducing the time cost of consultation and purchase;

[0176] Improved marketing efficiency and effectiveness: Precision marketing can improve the utilization efficiency of marketing resources and reduce marketing costs. By pushing precise product information and marketing activities to target customers, the response rate and conversion rate of marketing activities can be improved, thereby increasing the sales volume and market share of insurance products.

[0177] C. Intelligent customer service and knowledge Q&A.

[0178] c1. Intelligent question-answering system: Build an intelligent question-answering system based on the knowledge graph, and use natural language processing technology and semantic understanding technology to achieve accurate understanding and rapid answers to customer questions; the system can generate online simulation data based on the information in the knowledge graph, and provide high-precision and high-recall answers based on semantic matching models and dialogue management technology.

[0179] c2. Knowledge push: Based on the customer's consultation records and relevant knowledge in the knowledge graph, proactively push useful information to the customer, such as insurance knowledge, claims process, policy interpretation, etc., to enhance the customer's service experience.

[0180] c3. Customer service training: Use knowledge graphs to provide customer service personnel with training materials and knowledge support to help them better understand insurance products and business processes, and improve their professionalism and service quality.

[0181] Also includes: Intelligent question and answer evaluation.

[0182] Semantic matching: The accuracy of the question-answering system is evaluated by calculating the semantic similarity between the user's question and the answer in the knowledge graph; cosine similarity is used

[0183]

[0184] in, are the vector representations of user questions and answers, respectively.

[0185] This application has at least the following advantages:

[0186] Improved customer service efficiency: The intelligent question-and-answer system can answer customers' questions quickly and accurately, reducing the workload of manual customer service and improving customer service response speed and service quality; customers can get satisfactory answers in a timely manner, improving customer experience;

[0187] Knowledge sharing and inheritance: The knowledge base in the knowledge graph provides rich knowledge support for customer service personnel, which helps to quickly train new employees and inherit knowledge; at the same time, it also provides customers with more insurance knowledge and information, enhancing their understanding and trust in insurance.

[0188] D. Product design and optimization.

[0189] d1. Market analysis: Analyze market dynamics, competitor product information, changes in customer demand, etc. through knowledge graphs to provide a basis for the design and optimization of insurance products; understand the characteristics of popular products in the market, the coverage and compensation conditions that customers are concerned about, etc., in order to develop more competitive insurance products.

[0190] d2. Product pricing: Combine risk assessment knowledge graphs and market data to accurately price insurance products; consider risk factors, customer groups, market competition and other factors to formulate reasonable premium standards and improve product profitability and market competitiveness.

[0191] d3. Product portfolio recommendation: Recommend insurance product portfolios based on customer needs and risk preferences; analyze the complementarity and correlation between different insurance products through knowledge graphs to provide customers with one-stop insurance solutions to meet their diverse protection needs.

[0192] This application has at least the following advantages:

[0193] Improve product competitiveness: Based on market analysis and customer demand insights, design insurance products that are more in line with market demand and more competitive; reasonable pricing strategies and product portfolio recommendations can attract more customers to purchase and increase the market share of insurance products;

[0194] Enhanced innovation capabilities: Knowledge graphs provide data support and inspiration for product innovation, encouraging insurance companies to continuously explore new insurance products and service models, adapt to market changes and diversified customer needs, and promote innovative development in the insurance industry.

[0195] E. Claims management.

[0196] e1. Claims process optimization: Use knowledge graphs to model and analyze the claims process, identify bottlenecks and problems in the process, and make optimization suggestions; visualize the claims process to help claims personnel better understand the process and improve work efficiency;

[0197] e2. Claims risk assessment: Combined with the risk knowledge graph, conduct risk assessment on claims cases; analyze the relationship between claims cases and risk events, identify potential fraud risks and moral risks, and ensure the fairness and rationality of claims.

[0198] Claims fraud detection uses machine learning algorithms (such as logistic regression and random forest) to build fraud detection models and calculate fraud probability.

[0199] FraudProbability=f(ClaimData,KnowledgeGraph)

[0200] f is the fraud detection model, ClaimData is the claims data, and KnowledgeGraph is the knowledge graph.

[0201] This application has at least the following advantages:

[0202] Improved claims efficiency: The optimized claims process and knowledge sharing mechanism can help claims personnel handle claims cases faster and improve claims efficiency; customers can receive compensation faster and reduce dissatisfaction caused by long waiting times for claims;

[0203] Reduction of claims risk: Through risk assessment and claims case analysis in the knowledge graph, it is possible to more accurately identify claims risks, prevent fraud and unreasonable payments, ensure the fairness and rationality of claims, and safeguard the interests of insurance companies.

[0204] Step S5 also includes: operation and maintenance.

[0205] Continuously optimize and upgrade the application system based on user feedback and business development needs; optimize the system's functions, performance and user experience, add new application function modules, and improve the competitiveness of the application and user satisfaction;

[0206] Regularly evaluate the application effect of the knowledge graph, analyze the performance of the application in risk assessment, customer profiling, intelligent customer service, etc., and evaluate its role in improving the business and its economic benefits; based on the evaluation results, adjust the knowledge graph construction and application strategy to form a closed loop of continuous improvement.

[0207] In the present invention, multi-source data of the insurance industry is collected, including internal system data and external public data. The subsequent knowledge graph construction can make full use of rich data resources to provide more comprehensive and accurate information support for insurance business; define entities, determine relationships, set attributes, and then use graph database storage to present insurance knowledge in a structured form, so that the complex relationships and information in the insurance business can be clearly displayed, easy to understand and query, help to quickly grasp the overall picture of the business, and improve decision-making efficiency; comprehensively use natural language processing (NLP), machine learning, graph database and other technologies to improve the accuracy and efficiency of information extraction.

[0208] In this invention, a variety of knowledge graph application methods are provided, covering insurance business links such as risk assessment and management, customer profiling and precision marketing, intelligent customer service and knowledge Q&A, product design and optimization, and claims management, realizing the practical value of knowledge graphs; it can effectively improve the efficiency and effectiveness of each business link, help insurance companies formulate more forward-looking and competitive business strategies, and promote the innovative development of the business. An operation and maintenance mechanism has been established to continuously optimize the knowledge graph and application system, ensuring the continuous improvement and competitiveness of the system.

[0209] In the present invention, accurate customer portraits and personalized product recommendations can better meet customer needs and improve customer purchasing intention and satisfaction; the fraud detection model constructed using the knowledge graph can more effectively identify fraudulent behavior, protect the interests of insurance companies, and also maintain the fairness and stability of the insurance market; the knowledge graph provides data support and inspiration for product innovation, encouraging insurance companies to continuously explore new insurance products and service models to adapt to market changes and diversified customer needs.

[0210] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A knowledge graph construction and application method based on the insurance industry, characterized in that: The following steps are involved: S1, data collection and compilation; Collect multi-source data of the insurance industry and organize the data; S2. Knowledge modeling: define entities, determine relationships, and set attributes; S3, graph construction: entity recognition and disambiguation, relationship extraction, graph database storage; S4, graph optimization: add semantic information, optimize relationships, and create indexes; S5. Graph applications: risk assessment and management, customer profiling and precision marketing, intelligent customer service and knowledge Q&A, product design and optimization, and claims management.

2. The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that: Before step S1, a preparation stage is also included; ①. Demand analysis: Conduct in-depth communication with company departments to understand their needs and expectations for knowledge graphs, and clarify the application scenarios and goals of knowledge graphs; ②. Team building: Establish a cross-departmental team and clarify the responsibilities and division of labor of each member; ③. Technology selection: Select appropriate technology components based on project requirements and budget; ④. Develop a data collection plan.

3. The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that: In step S2, it includes: S201. Define entities: Identify and define various entities based on the business characteristics and needs of the insurance industry; S202. Determine the relationship: clarify the relationship type between entities; S203, attribute setting: Add corresponding attributes to each entity and relationship to enrich knowledge representation.

4. The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that: In step S3, it includes: S301, Entity Recognition and Disambiguation: Use natural language processing technology to identify entities from text data, disambiguate synonymous entities through deep learning models or rule engines, and unify them into one node; S302, relationship extraction: based on the identified entities, extract the relationship between entities through rules or deep learning algorithms; S303, graph database storage: select a suitable graph database to store the extracted entities and relationships in it; use the query language of the graph database to create nodes and relationships, and assign values ​​to the attributes of the nodes and relationships.

5. The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that: In step S4, it includes: S401, adding semantic information: further enriching the semantic information of the knowledge graph; S402, optimize relationships: optimize the relationships in the graph, remove redundant relationships, and supplement missing relationships; S403, create index: create index for commonly used query attributes in the graph database.

6. The method for constructing and applying a knowledge graph based on the insurance industry according to claim 1 is characterized in that: Step S4 also includes: quantitative evaluation and optimization; Accuracy: Recall: F1 score: Spectrum redundancy assessment: Graph optimization strategy: adjust the algorithm parameters of entity recognition and relationship extraction according to accuracy and recall; use methods such as cluster analysis to identify and merge redundant nodes and relationships; continuously optimize graph structure and content through user feedback and expert evaluation.

7. The knowledge graph application method based on the insurance industry according to claim 1 is characterized in that: In step S5, the risk assessment and management includes: a1. Risk prediction: Build a risk knowledge graph, integrate and analyze risk data, historical cases and market intelligence; segment industries, establish a relationship model between industries, and use machine learning algorithms to discover the correlation between industries; a2. Risk public opinion monitoring: mining the associations of insurance companies to achieve semantically accurate public opinion warnings, helping to obtain first-hand information on risk public opinion so as to take timely response measures; a3. Anti-fraud: Apply knowledge graph technology to integrate the basic information of policyholders and beneficiaries for in-depth analysis and prediction; improve the accuracy and efficiency of fraud detection by building a knowledge graph model for fraud detection and risk control, identify and respond to potential fraud in a timely manner, and protect business interests and customer interests.

8. The knowledge graph application method based on the insurance industry according to claim 1 is characterized in that: In step S5, the customer profiling and precision marketing include: b1. Customer portrait construction: Build detailed customer portraits by analyzing customer letter data; associate customer entities with relevant attributes and behaviors in the knowledge graph; b2. Accurate product recommendations: Based on customer profiles and insurance product knowledge graphs, we provide customers with personalized insurance product recommendations. We analyze customers’ potential needs through recommendation algorithms and recommend the most suitable insurance products to improve sales efficiency and customer satisfaction. b3. Customer relationship management: Use knowledge graphs to analyze relationships between customers; use these relationships to tap into potential customer resources, achieve cross-selling and maintain customer relationships; In the construction of b1 customer portrait, customer value assessment is also included; Adopt RFM model: evaluate customer value based on the customer's most recent purchase time (recency), purchase frequency (frequency) and purchase amount (monetary); CustomerValue=α×Recency+β×Frequency+γ×Monetary Among them, α, β, and γ are weight coefficients; In b2 precise product recommendation, it also includes recommendation algorithm evaluation; Precision@K: The proportion of products actually purchased by users among the first K products in the recommended list. Recall @ K: The proportion of products recommended to the top K products among the products actually purchased by the user.

9. The knowledge graph application method based on the insurance industry according to claim 1 is characterized in that: In step S5, the product design and optimization includes: d1. Market analysis: Analyze market dynamics, competitor product information, and changes in customer demand through knowledge graphs to provide a basis for the design and optimization of insurance products; understand the characteristics of popular products in the market, the coverage and compensation conditions that customers are concerned about, so as to develop more competitive insurance products; d2. Product pricing: Combine risk assessment knowledge graphs and market data to accurately price insurance products; consider risk factors, customer groups, and market competition factors to formulate reasonable premium standards and improve product profitability and market competitiveness; d3. Product portfolio recommendation: Recommend insurance product portfolios based on customer needs and risk preferences; analyze the complementarity and correlation between different insurance products through knowledge graphs to provide customers with one-stop insurance solutions to meet their diverse protection needs.

10. The knowledge graph application method based on the insurance industry according to claim 1 is characterized in that: In step S5, the claims management includes: e1. Claims process optimization: Use knowledge graphs to model and analyze the claims process, identify bottlenecks and problems in the process, and make optimization suggestions; visualize the claims process to help claims personnel better understand the process and improve work efficiency; e2. Claims risk assessment: Combined with the risk knowledge graph, risk assessment of claims cases is conducted; the relationship between claims cases and risk events is analyzed, potential fraud risks and moral risks are identified, and the fairness and rationality of claims are ensured; Among them, claims fraud detection uses machine learning algorithms to build fraud detection models and calculate fraud probability. FraudProbability=f(ClaimData,KnowledgeGraph); f is the fraud detection model, ClaimData is the claims data, and KnowledgeGraph is the knowledge graph.

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