1.   AI in insurance

 

The application of big models in the insurance industry has comprehensively improved the level of insurance business. From the customer's perspective, it brings a better and more personalized service experience; from the insurance company's perspective, it improves operational efficiency, reduces costs, and enhances risk management capabilities.

 

The intelligent applications of insurance mainly include intelligent customer service, precision marketing, intelligent underwriting and claims, and intelligent assistants. Intelligent customer service provides personalized answers, improves the limitations of traditional customer service that relies on preset templates, and enhances customer experience. Precision marketing analyzes customer behavior, health, and financial status to accurately recommend insurance products and improve customer conversion rates. Intelligent underwriting and claims can quickly parse medical records, physical examination reports and other information to assist underwriters in risk assessment, and preliminarily review materials and determine the scope of compensation when settling claims, thereby reducing manual workload, shortening the review time for complex cases, and improving overall efficiency. Intelligent assistants are mainly agent assistants, which can help agents quickly obtain product information, generate communication documents, and optimize sales strategies, helping them transform from "sales-oriented" to "expert consultants", focusing on providing customers with high-value consulting services such as family wealth planning.

 

Insurance is a service with the strongest product understanding in the financial industry. Different from banking and securities businesses, in addition to financial attributes such as wealth management and investment, insurance also has protection attributes for users such as medical care, health, accidents, and retirement. Therefore, when sorting out the application of big models in the insurance industry, the positive impact of big models on the industry will be analyzed from the perspective of users and insurance companies (insurance agents, telemarketing, bank insurance, etc.).

 

From the user's perspective, what users need to know most is what kind of protection the insurance products provided by the insurance company can provide, the various attributes of its products, precautions and other key information. The interpretation of the big model will be more diversified, and can be personified, analogous, and professional. Focusing on the characteristics of insurance products that users are most concerned about, the big model will provide various types of detailed explanations based on the following tables to achieve the goal of users fully understanding insurance products.

 

From the perspective of insurance companies (insurance agents, telemarketing, bancassurance, etc.), compliance with regulatory standards, lowering understanding costs, and reducing process time are the three most critical aspects. Therefore, this paper analyzes the insurance companies' demand for big models and the points for continuous improvement in the three aspects of product development and sales, underwriting and claims, and regulatory compliance.

 

Insurance Agent Panoramic Application also elaborates on three aspects: product development and sales, underwriting and claims, and regulatory compliance, rather than on intelligent customer service and precision marketing analysis. It mainly includes the following two factors:

 

First, all the backend content must be made clear so that a better user experience can be provided to front-end users, account managers, and agents.

 

Second, only by transforming the company's information data into a large-scale model can we truly streamline the information flow and significantly improve the processing efficiency of the business flow, thereby enhancing the sales performance of the insurance company.

 

The following is a comprehensive introduction to the panoramic application of Insurance Agent in product development and sales, underwriting and claims, and regulatory compliance.

 

2.   Insurance Agent Application

 

2.1  Product development and sales

 

2.1.1  Insurance Product Interpretation

 

Agent scenario:

 

In the insurance industry, insurance product terms are complex, with numerous professional terms and difficult to understand. This not only brings great trouble to users, but also greatly increases the communication costs in the insurance sales process. It is also easy to cause potential sales misleading risks, which seriously restrict the healthy development of the insurance business.

 

Insurance product interpretation agents mainly serve insurance sales personnel, users and related stakeholders. Through in-depth analysis of insurance clauses, they can accurately extract key information, convert it into easy-to-understand language, and output interpretation content, effectively solving problems such as obscure insurance clauses, high communication costs, and sales misleading risks. This not only greatly improves users' understanding and satisfaction with insurance products, but also improves sales efficiency and reduces communication costs and sales misleading risks.  

 

Demand Analysis:

 

Currently, there are many problems in the interpretation of insurance products, which seriously affect the quality of insurance sales and services. The market urgently needs a tool that can accurately interpret insurance products, convert complex terms into easy-to-understand content, and help users needs to quickly understand products.

 

At the user level, insurance product clauses are lengthy and contain a lot of professional terms, which makes it difficult for users to quickly and accurately grasp key information such as coverage, compensation conditions, and cost details. This not only affects users' purchasing decisions but may also cause disputes in the subsequent claims process.

 

At the sales staff level, when introducing products to users, sales staff need to spend a lot of time and energy explaining the terms, and due to differences in personal understanding, it is difficult to unify the interpretation, which increases sales difficulty and compliance risks.

 

Business process:

 

Insurance product interpretation agents can be embedded in the user communication module of the sales system. After users select a product and request an interpretation, the agent will quickly analyze the terms and present key content such as product highlights, protection details, and claims process to users in a concise, clear, and graphic manner.   

 

Basic models include language model, visual model, document parsing model, vectorization model, etc. These models form the technical foundation of the entire solution and provide strong support for subsequent knowledge processing and application.

 

Clause extraction uses layout analysis technology to accurately identify the page layout and structural information of the document; through document parsing technology, text content and key data are extracted; with the help of OCR recognition technology, the text in the image is converted into editable text; using chart recognition technology, the data information in the chart is interpreted; finally, the various fields required to interpret the product are extracted from the document through a large model and stored in the database. 

 

Product interpretation: infer the user's demands based on the products selected and questions asked by the user, interpret them using a large model, and choose a suitable presentation format to display the final results. Extract insurance product element information from long insurance clauses and generate personalized product interpretations for users in the form of pictures, texts, tables, and mind maps.

 

Value Analysis:

 

Improve user experience by converting obscure insurance terms into easy-to-understand content through large models, helping users quickly understand insurance products, shortening the time for understanding, improving their understanding of insurance products, reducing potential complaints caused by misunderstanding of terms, enhancing users' willingness to buy, and reducing user churn caused by poor communication.

 

Improve sales efficiency, standardize knowledge processing and concise presentation methods, so that sales personnel can quickly acquire knowledge to answer customer questions, reduce communication costs and time costs in the sales process, and improve sales efficiency.

 

The cost has been greatly reduced. The average manual cost of extracting factor information has been reduced from  40  yuan  /  item to  2  yuan  /  item, a 20-fold reduction. The processing time for producing personalized descriptions has been reduced from  4  hours to less than  10 minutes, with a production cost of 4 man-days/product. After the improvement of the large model, it takes  1 minute/product to generate personalized product interpretations for individual users.

 

2.1.2  Insurance Product Search

 

Agent scenario:

 

In the insurance industry, traditional insurance product search pages provide a variety of query conditions for users to choose from, such as insurance type, insurance company, coverage, premium range, and policyholder age. Users need to select or enter specific query conditions based on their own needs. The backend will then combine these conditions into corresponding query statements, search the business database, and return a list of eligible insurance products. However, traditional search methods have a relatively mechanical understanding of user needs, lack flexibility, and are difficult to adapt to complex and changing user needs and natural language expressions. Users not only need to have certain insurance expertise, but also need to go through a tedious query process.

 

The insurance product search agent uses a large model and, with its powerful semantic analysis capabilities, has changed the interaction mode of traditional insurance product search. It can deeply understand user intentions and quickly return accurately matched insurance products even when faced with complex demands expressed in natural language, greatly improving search efficiency and user experience.

 

Demand Analysis:

 

Characteristics and limitations of traditional insurance product searches:

 

The user threshold is high. Users need to have certain insurance knowledge, understand the meaning of each query condition provided by the system, and know how to reasonably combine these conditions to obtain accurate search results. For example, an office worker wants to buy an insurance product that includes critical illness and accidental insurance. In the traditional search mode, he needs to understand and select "insurance type" as critical illness insurance and accidental insurance, and may also need to consider multiple conditions such as "coverage" and "premium". For users who are not familiar with insurance, these professional terms and complex query conditions may confuse them confused and difficult to operate.

 

Traditional search algorithms have great limitations. Since they cannot analyze and understand from a semantic level, matching and search results are poor. For example, if the system records that the description of an insurance product contains "cancer protection", if the user enters "malignant tumor insurance" to search, the traditional system may not be able to accurately match this insurance product. This situation highlights the limitations of traditional insurance product search in terms of semantic understanding.

 

Business process:

 

Search intent reasoning: This module is responsible for deeply analyzing the user's original search query and subdividing its intent into two categories: structured needs and personalized needs. Structured demand processing: For structured needs, the module uses a preset filtering tag system to efficiently retrieve matching products from the product library. Personalized demand processing: For personalized needs, firstly, the query is accurately converted into industry terms through a large language model ( LLM ), and the recall template pre-prepared by industry experts is combined to further improve the relevance of the recall. Finally, a multi-way recall strategy of BM25 (similarity algorithm based on word frequency) + Vector (vector similarity matching) is adopted to comprehensively generate a list of product candidates.   

 

Product library module, which stores basic information of all products and supports structured and unstructured searches of user products. Filter tag field: Rich filter tags are set up to facilitate users to quickly filter products according to specific conditions. Product feature library: specially stores semantic vector information of products, which is extracted through advanced natural language processing technology to support higher-level semantic matching and recommendations.

 

The hybrid sorting module which aims to integrate and optimize the product lists obtained from conditional screening and multi-way recall. Candidate fusion: First, the product lists obtained through conditional screening and multi-way recall are intersected to ensure that the final candidate products meet both the structured and personalized needs of users. Final candidates: Then, a large language model is used to deeply understand and sort the candidate fusion products, and the final, highly personalized product candidate list is generated based on factors such as product relevance, user preferences, and historical behavior.

 

Value Analysis:

 

Significantly improve user experience. By adopting a large model, this agent can deeply understand the user's natural language query, without requiring the user to have complex insurance knowledge or to make tedious condition selections. This interactive method is closer to the user's daily thinking habits, greatly simplifies the search process, and thus improves user satisfaction and loyalty.

 

Enhanced search flexibility and accuracy: Traditional insurance product search systems are often limited by fixed query conditions and are difficult to adapt to complex and changing user needs. However, this agent can accurately capture the personalized needs of users through semantic analysis technology, and can return accurate matching results even when faced with ambiguous or diverse expressions described in natural language. This greatly enhances the flexibility and accuracy of the search.

 

2.1.3  Insurance product recommendations

 

Agent scenario:

 

In online insurance sales, traditional product recommendations rely on established models and expert experience, and are modeled through collaborative signals between users and product features. Although this approach can capture user preferences and has low resource overhead, it has obvious limitations. It mainly recommends based on historical data and user behavior, lacks in-depth analysis of semantics and intent, and is difficult to meet complex and changing user needs. For example, when young families choose life insurance, they will comprehensively consider multiple factors such as the age, occupational risk, economic status, and health status of family members, and these factors may not be fully reflected in traditional recommendation models.

 

The insurance product recommendation agent introduces a recommendation system based on a large language model, which can introduce external knowledge, extract feature information more comprehensively, enrich semantic signals, and have logical reasoning capabilities. It can deeply understand user motivations and preferences and provide accurate and comprehensive product and background information. This system not only improves the accuracy of recommendations but is also particularly suitable for cold start scenarios, effectively optimizing the performance of the recommendation system and user experience.

Demand Analysis:

 

Insurance product purchases are infrequent and complex, and user needs and preferences vary widely, making insurance product recommendation systems more challenging than traditional e-commerce. Traditional recommendation systems have many pain points:

 

Poor explainability. Traditional recommendation systems are based on complex algorithm models, and it is difficult to explain the basis of recommendation results to users. It is difficult for users to understand the recommendation logic, which makes users have low trust and acceptance of the recommendation results.

 

The cold start and long tail problems are prominent. When facing new users or new products, traditional recommendation systems are overly dependent on historical data and find it difficult to make effective recommendations when data is lacking. This limits the expansion of new users and the promotion of long tail products. 

 

Insufficient personalization. Due to the lack of strong natural language understanding and generation capabilities, traditional recommendation systems are unable to deeply understand user context and needs, and it is difficult to accurately capture user contextual information. The recommendation results provided are not diverse enough and cannot meet user personalized needs, resulting in low user engagement and satisfaction, and the product recommendation conversion rate is also affected.

 

Business process:

 

Original data, product information: covers detailed terms and conditions of the product, instructions, and information about historical purchasers. This information is an important basis for understanding product features and building product portraits. User data: includes the user's historical insurance policies, browsing behavior, basic information (such as age, gender, etc.), and health information. This data constitutes the key basis for user portraits.

 

Feature extraction, product understanding and characterization: Through in-depth analysis of historical purchasing user portraits, product terms and instructions and other materials, key information such as the core features and coverage of the product can be extracted. Using model reasoning, we can further infer the population to which the product may be applicable. These features directly map the product's functions, advantages and target user groups. User understanding and characterization: By analyzing the user's historical insurance policies, browsing behavior, basic information and health data, we can dig out the user's potential needs, purchasing motivations and product preferences. This feature information is converted into specific labels and potential demands of the user, thereby constructing a user characterization library.

 

Product recommendation, recommendation request initiation: When a user visits an insurance app or web page, a recommendation system request is triggered. Multi-channel recall: Based on user representation, product information that meets user needs is accurately recalled from the product library through multiple means such as conditional screening, semantic matching, and operational recommendations. Fusion sorting: Sort the recalled products, screen out the Top N  candidate products, and use the large model for multi-dimensional reasoning and selection to finally determine the recommendation results.  

 

Display fusion: On the product interface, a list of products selected by the model is displayed for users to refer to and choose from. These product lists contain basic information about the products and can also be accompanied by clear reasons and explanations for the recommendations, helping users better understand the recommendations and make wise choices.

 

Value Analysis:

 

Economic benefits are improved. Accurate recommendations increase the conversion rate of product recommendations, and more users choose suitable insurance products, which directly increases sales. At the same time, reducing invalid recommendations saves operating costs, allowing resources to be more efficiently invested in other key business links, improving overall operating efficiency, and bringing sustained profit growth to the company in the long run.

 

The user experience is optimized and the explainability is enhanced, so that users can clearly understand the basis of the recommendation and enhance their trust. Personalized recommendations meet the diverse needs of users, and the solution of cold start and long tail problems ensures that new users and niche products can also receive attention, comprehensively improving the user's satisfaction and convenience in the process of selecting insurance products, which helps to cultivate user loyalty and form a good word-of-mouth communication.

 

2.1.4  Insurance Product Q&A

 

Agent scenario:

 

Insurance products are uniquely complex and are different from relatively structured financial products such as stocks and funds. Insurance products often have many types of insurance, and their detailed information is contained in various insurance clause materials. These clauses are not only highly professional and personalized, but also obscure and difficult for ordinary users to understand. Even insurance sales personnel must conduct in-depth research on the characteristics of each product in advance if they want to fully and accurately answer customers' diverse questions and give precise and easy-to-understand answers. This places extremely high demands on the professional level and the adaptability of sales personnel. At the same time, sales personnel need to serve a large number of customers around the clock, and service efficiency and quality are difficult to guarantee. 

 

Based on the natural language processing, semantic understanding and generation capabilities of the big model, the insurance product question and answer assistant can transform obscure insurance clauses into clear and easy-to-understand product knowledge, thereby building a comprehensive, accurate and easy-to-use insurance product knowledge base.

 

Demand Analysis:

 

The current Q&A about insurance products has the following pain points:

 

The documents in the insurance industry are of various types and in various forms, including but not limited to product terms, instructions, operating rules, regulatory requirements, insurance rules, channel business rules, company brand information and rate tables. These materials need to be orderly classified and associated according to business logic, such as classifying product terms, instructions and insurance rules by insurance product number, and dividing them by channel. Manually organizing these materials and establishing effective connections, as well as extracting and integrating knowledge from documents of different formats to build a knowledge base, require a lot of manpower and time.

 

Insurance salespeople need to master a large amount of product information, but the knowledge system of each product is independent and complex, and the learning cost is extremely high. Therefore, salespeople are in urgent need of an efficient and convenient way to acquire knowledge in order to improve sales service levels and better meet customer needs.

 

Low service efficiency and limited throughput. In the insurance business, there are a large number of customers and the time distribution of consultation questions is wide. It is difficult for insurance sales staff to respond to a large number of customers' questions around the clock at the same time. Each salesperson needs to serve many customers, resulting in high work pressure and difficulty in ensuring service efficiency.

 

Business process:

 

Basic models include language models, visual models, document parsing models, and vectorization models. These models form the technical foundation of the entire solution and provide strong support for subsequent knowledge processing and application.

 

Knowledge question answering uses query understanding optimization technology to deeply analyze the semantics, intent and key information of user questions; uses multi-way retrieval recall strategy to quickly obtain relevant knowledge fragments from the knowledge base; adopts re-ranking algorithm to sort the recalled knowledge according to importance; integrates the filtered knowledge into prompts to provide richer contextual information for the big model; finally, with the help of the powerful generation ability of the big model, generates accurate, clear and easy-to-understand answers to user questions.

 

Knowledge Processing

 

This phase mainly manages, analyzes, segments and extracts knowledge from business documents, and finally builds a full-text index and a vector index to meet the needs of knowledge question and answer search. The specific steps are as follows:

 

1 ) Document management

 

Using big model technology and combining professional knowledge in the insurance business field, we carefully categorize and deeply integrate the original submitted product terms, instructions, rate tables, operating rules and other documents. Based on the inherent logical relationship between different types of knowledge, we build an original knowledge database with distinct insurance industry characteristics.

 

2 ) Document parsing and segmentation

 

Use layout analysis technology to accurately identify the page layout and structural information of the document; use document parsing technology to extract text content and key data; use OCR recognition technology to convert text in images into editable text; use chart recognition technology to interpret data information in charts; and finally use document segmentation technology to split long documents into reasonable segments.  

 

3 ) Knowledge extraction

 

Using large model knowledge enhancement technology, we introduce external knowledge bases and domain expert knowledge to supplement and improve the original knowledge. Using vectorization technology, we convert text into vector form and build a knowledge base with multiple capabilities, such as efficient vector retrieval.

 

Value Analysis:

 

Significantly improve efficiency through automated document management and knowledge processing, greatly shorten the conversion time from raw data to a usable knowledge base. Sales staff do not need to manually organize complicated data, and can quickly obtain the required information from the constructed knowledge base to answer customer questions, greatly improving work efficiency and significantly speeding up the response speed of insurance business consultation.

 

Reduce costs, reduce the investment of manpower in data collation and knowledge construction, and reduce labor costs and time costs. At the same time, efficient knowledge acquisition methods reduce the learning costs of sales staff, shorten the training cycle, and enable new employees to get started faster.

 

By enhancing customer experience, customers can get faster, more accurate and easier-to-understand answers, solve the difficulties in insurance product consultation, improve customer satisfaction and trust in insurance services, help improve customer conversion rate and loyalty, and promote the growth of the insurance business.

 

2.1.5  Comparison of insurance products

 

Agent scenario:

 

In insurance sales and user service scenarios, users often face the difficulty of choosing between many insurance products. They find it difficult to clearly understand key information such as the coverage, compensation conditions, and premium differences among different products, which leads to decision-making difficulties and affects the purchasing experience and sales efficiency.

 

The insurance product comparison agent aims to solve this pain point. Based on advanced large model technology, it can quickly and accurately conduct multi-dimensional comparative analysis of various insurance products. The agent can generate clear and easy-to-understand comparison results in real time based on the product name, demand preferences and other information entered by the user, and present them intuitively in the form of tables, charts, etc., to help users quickly grasp the differences between products, make more informed decisions, and promote sales conversion of insurance business and improve user satisfaction.   

 

Demand Analysis:

 

At the front end of insurance sales, users often spend a lot of time comparing products during the consultation phase. Due to the lack of professional knowledge and convenient tools, it is difficult for users to sort out the complex terms and detailed differences of various products on their own, which can easily lead to confusion and anxiety during the selection process, which may lead to a decrease in purchasing intention.

 

For insurance sales personnel, manual product comparison is not only inefficient, but also difficult to ensure the accuracy and completeness of information, and it is difficult to quickly provide accurate product recommendations and comparison plans based on users' personalized needs.

 

In addition, in the increasingly competitive environment of the insurance market, insurance companies need to more efficiently demonstrate the advantages and features of their products to users in order to attract more users and increase market share. However, traditional product introduction methods can no longer meet this demand. An intelligent and automated tool is urgently needed to optimize the product comparison process and improve user experience and sales competitiveness.

 

Business process:

 

In the past, when users inquired about product comparisons in the sales team of insurance companies, sales staff needed to consult a large amount of paper materials or switch queries between multiple systems, and then manually organize the comparison information. The entire process could take tens of minutes or even longer. Users lacked patience and often interrupted the consultation process, resulting in the loss of sales opportunities.

 

After the introduction of the insurance product comparison agent, when a user raises a comparison request, such as "I want to know the difference between critical illness insurance A and critical illness insurance B ", the agent immediately parses the user's needs through the natural language understanding module, and accurately extracts detailed information about the two products from the insurance product database, including key elements such as the types of covered diseases, compensation ratios, waiting periods, and premium calculation methods. Using the data comparison and visualization modules, it quickly generates a clear comparison table and displays it on the sales terminal device in a graphic and textual format. The whole process takes only a few seconds.        

 

The big model plays a key role in information extraction and semantic understanding, ensuring the accuracy and comprehensiveness of comparative information. Its main functional modules include natural language understanding, data extraction and processing, comparative analysis and visualization, etc. The front end interacts with users through the sales system, and the back end connects the insurance product database and the big model service interface to form a complete call link.

 

After the implementation of this solution, user consultation satisfaction has been greatly improved, sales conversion rate has increased, and the work efficiency of sales staff has been significantly improved, allowing them to focus more time on user communication and demand exploration. The technical innovation lies in the intelligent and automated processing of insurance product comparison, breaking through the limitations of traditional manual comparison.

 

Value Analysis:

 

Improve user experience, provide users with fast, accurate and intuitive insurance product comparison services, reduce user decision-making time, enhance user satisfaction and confidence in the insurance purchasing process, and help improve user loyalty and word-of-mouth communication.

 

Improve sales efficiency, help insurance sales personnel quickly respond to user needs, provide professional product comparison suggestions, shorten sales cycles, increase sales conversion rates, reduce sales costs, and enable sales personnel to devote more energy to user relationship maintenance and business expansion.

 

By enhancing market competitiveness, insurance companies can more effectively demonstrate the advantages and differentiated features of their products to users, highlight product competitiveness, stand out in the fierce market competition, and attract more users to choose the company's insurance products.

 

2.1.6  Clause Analysis Assistant

 

Agent scenario:

 

In the diversified and competitive insurance industry, product design and management capabilities are the core competitiveness of insurance companies. In order to improve product management capabilities, the new generation of core systems needs to achieve refined configuration of insurance terms and products, covering a variety of insurance types and hundreds of fields. This requires more standardized system configuration and data granularity to be refined to the responsibility level, so as to achieve full-link automation of upstream and downstream system data. This refined data processing is of great significance to product analysis, actuarial pricing, claims analysis and risk control, and is the basis for evaluating and improving product profitability. However, when actually implementing a refined interpretation of terms, there are problems such as long time consumption, high professional requirements, and large manpower investment.

 

With the powerful parsing ability of the big model, the clause parsing agent can efficiently parse insurance clauses and output them according to the product data structure of the core system. This will precipitate the experience of business experts into a fixed parsing solution, significantly improve the efficiency of clause interpretation, standardize product data, and effectively solve the difficulties of the insurance industry in clause interpretation and data processing.

 

Demand Analysis:

 

The current clause analysis has the following problems:

 

The time cost is high. When actually implementing the detailed interpretation of terms, the entire process takes a long time, which may lead to slow business advancement, missed market opportunities, and increased operating costs.

 

The professional requirements are high. Insurance clauses involve the setting of supplementary insurance types and a large amount of professional knowledge, which requires an extremely high professional quality of the interpreters. Ordinary employees are not competent, and professional talents are relatively scarce, which restricts the development of the business to a certain extent.

 

The manpower investment is large. In order to complete the detailed interpretation of terms and data processing, a large amount of manpower is needed, which not only increases the labor cost, but also may face the difficulty of personnel deployment and management.

 

There is a lack of effective accumulation of experience, and the original interpretation standards are scattered in the minds of various experts. A unified and effective accumulation mechanism has not been formed, which is not conducive to knowledge inheritance and team collaboration, and it is impossible to give full play to collective wisdom to improve the efficiency and quality of clause interpretation.

 

Business process:

 

The insurance terms intelligent parsing assistant can realize the intelligent parsing of textual terms and rate regulations, and convert them into structured data that meets the core system product data structure requirements, and then automatically fill in the system configuration page, replacing the original manual interpretation and manual configuration operation mode.

 

Insurance type parsing template. This module configures the field extraction definitions of different insurance types, generally including the name of the field to be extracted, its definition, extraction prompts, verification rules, etc.

 

The document preprocessing module mainly performs text analysis on the original documents (such as PDF or Word documents) to convert them into a format that can be processed by the big model, and then classifies the insurance types of the documents through the big model.    

 

The extraction and verification module loads the corresponding field extraction definition from the insurance type parsing template according to the insurance type classification results of the document preprocessing module, and calls the large model for extraction and verification, respectively.

 

Generate an export result file, which can be used for further quality inspection and use by humans.

 

Value Analysis:

 

Efficiency has been greatly improved. The original analysis time of each clause document took several days, but the large model solution can reduce it to several minutes, which greatly speeds up the business advancement and enables insurance companies to respond to market changes in a timely manner and seize market opportunities.

 

The cost is significantly reduced, as the large model can automatically complete the analysis of terms and data processing, reducing the need for a large number of people. There is no need to invest a large number of professionals for manual interpretation, and it also avoids the complex issues of personnel deployment and management.

 

Knowledge accumulation and inheritance, the experience of business experts is accumulated into a fixed analysis solution, forming a unified clause interpretation standard, which is conducive to knowledge inheritance and team collaboration.

 

2.1.7  Insurance Marketing Creation

 

Agent scenario:

 

In the sales process of the insurance industry, the diversity and complexity of insurance products make it particularly difficult to accurately convey the value of products. With the continuous segmentation of consumer demand and the strengthening of the trend of personalization, sales personnel not only need to be proficient in insurance expertise but also need to have keen market insight and delicate psychological analysis capabilities in order to accurately capture the unique needs of each customer and create appropriate marketing copy accordingly. How to perform this task efficiently and accurately has undoubtedly become a very challenging task.

 

Insurance Marketing Creation Agent is an intelligent solution designed for the marketing process of the insurance industry. Through a large language model, it can accurately understand the characteristics of insurance products and user needs, and automatically generate personalized marketing copy, promotional posters and other marketing materials. The Agent has greatly improved the efficiency of marketing content creation, reduced labor costs, and significantly improved the accuracy and success rate of marketing, creating greater market value for insurance companies.    

 

Demand Analysis:

 

In the insurance marketing scenario, business pain points are mainly concentrated in the following aspects:

 

Insurance product terms are complex, and it is difficult for marketers to quickly extract core selling points for promotion.

 

The user group is huge and their needs vary, and traditional marketing methods are difficult to meet personalized needs.

 

Marketing content creation relies on professionals, with a long creation cycle and high costs.

 

For example, when promoting a new health insurance product, marketers need to spend a lot of time studying product terms, understanding details such as coverage and compensation conditions, and then creating targeted marketing copy based on factors such as the health status and economic strength of different user groups. The whole process is cumbersome and inefficient.

 

Business process:

 

Raw data mining provides the data that creation relies on

 

Mining creative clues: The big model captures a wide range of insurance industry news, social media discussions, industry reports and other massive data, and analyzes the data to extract creative clues such as hot topics, emerging trends and user focus. At the same time, for high-quality content, the content creation strategy framework is analyzed to discover and learn differentiated creative angles, providing directional guidance for insurance marketing content creation and quality content output. 

 

Product feature mining: The big model conducts in-depth disassembly and extraction of insurance knowledge base documents, identifies key information in product documents, and uses knowledge graphs to structure the information, clearly present product features, core advantages and labels, and ensure that product value can be accurately conveyed in marketing content creation.

 

User portrait mining: Integrate multi-source data to build user portraits, including but not limited to basic information, historical insurance records, protection gaps, operational behaviors and other data to group users, predict user needs and purchasing tendencies, and customize personalized insurance marketing content for each user portrait to improve the accuracy and effectiveness of marketing.

 

The creation engine is built to meet the needs of creation

 

Creation goal setting: define the core role of content (such as sales conversion, brand communication or user education); simultaneously lock in the target audience and determine the content direction based on user portraits and demand characteristics. Match the content form and format specifications according to the characteristics of the publishing channel (WeChat Moments  /  Xiaohongshu  /  short video platforms, etc.) to ensure that it is adapted to the display rules of different terminals. Finally, set the style tone, choose professional and rational or emotionally resonant expressions based on product attributes, and define visual elements (color matching, layout, image-text ratio) to enhance the efficiency of information transmission.

 

Scenario template selection: Call the preset framework based on the target type: product promotion usually adopts the structure of "pain point trigger - solution - action incentive"; activity focuses on "time limit pressure - interest point - participation path"; brand relies on "data/case endorsement - value proposition"; user education follows "cognitive misunderstanding - knowledge analysis - behavior guidance". The template provides a structured content skeleton to ensure that the core information does not deviate from the business demands.  

 

Content generation: Integrate the four elements of goals, audiences, channels, and templates, generate multiple sets of creative directions (such as emotional narratives, data comparisons, and Q&A interactions) by analyzing historical data and user behavior preferences, and automatically embed compliance evidence (such as clause references and risk warnings). The output content must contain a complete logical chain (attract attention - stimulate demand - prove value - promote action), and adjust details according to channel rules (such as word limit, jump link location).      

 

Compliance quality inspection before delivery: filter illegal statements (such as absolute promises, misleading benefits) and automatically replace them with compliant words. In response to the needs of multi-terminal publishing, the main content is disassembled into derivative versions adapted to different platforms (such as long pictures and texts are simplified into bullet screen words, and oral scripts are disassembled into storyboards). Use A/B testing to compare key variables (titles, visual elements), and select the optimal solution based on real-time data feedback. 

 

Value Analysis:

 

Improve marketing efficiency, quickly understand the characteristics of insurance products and the needs of target audiences through large-scale deep learning and natural language processing, continuously output a large number of differentiated copywriting, automatically adapt to multiple channels such as social media platforms, emails, and text messages, and shorten traditional manual work of several hours to minutes.

 

Improve content quality. Through the built-in knowledge base of the big model, you can quickly master professional knowledge and different copywriting styles, output copywriting content with a unified style and professional standards, reduce the risk of human errors, and ensure the professionalism of language expression through big model training.

 

Improve marketing effectiveness, utilize large-model user portraits and scene recognition capabilities to generate personalized copy for different user groups, improve content relevance and user interactivity, and promote the achievement of corresponding marketing goals.

 

2.2  Underwriting and Claims Settlement

 

2.2.1  Intelligent pre-underwriting

 

Agent scenario:

 

Insurance products are diverse, cover a wide range of professions, and have different customer needs. It is difficult for insurance agents to fully grasp all product forms and underwriting policies. In actual business, agents often need to consult back-end underwriters on policy interpretation, plan matching, and preliminary underwriting judgments through telephone, WeChat, etc. However, the number of underwriters is limited, and their response time and concurrent processing capabilities are difficult to meet the peak business needs; and different underwriters have differences in experience and professional capabilities, resulting in a lack of unified standards for answering front-line consultations, which undoubtedly brings many challenges to customer experience and service efficiency.

 

Pre-underwriting is a key link in the insurance sales process. It can utilize the capabilities of large language models in natural semantic dialogue, information collection, risk assessment, and decision support, combined with the existing underwriting engine capabilities at the back end, to assess the customer's risk status and insurance eligibility in advance, thereby improving the efficiency of pre-underwriting and providing customers with a smoother and more efficient insurance experience.

 

Demand Analysis:

 

There are many pain points in the pre-sale and pre-underwriting stage of insurance business that need to be solved urgently, as shown below:

 

There is a high reliance on manual experience. There are many types of products in the insurance market, and there are significant differences between different insurance plans and pricing policies. The current pre-underwriting process relies heavily on the manual experience of underwriters for judgment. However, due to the uneven professional quality and experience of underwriters, the risk assessment results of the same insurance subject are often biased, making it difficult to ensure the accuracy and consistency of the assessment.

 

The opportunity cost loss is obvious. The traditional pre-underwriting process requires the insured to fill out a large number of forms and questionnaires, and the process is extremely cumbersome and complicated. This not only seriously affects the sales efficiency of insurance products, but also slows down the speed of customer acquisition. The lengthy process can easily make the insured feel bored and dissatisfied. Some potential customers will even give up insurance due to long waiting time or cumbersome process, which undoubtedly causes the insurance company to suffer opportunity cost loss that cannot be ignored.

 

Labor costs remain high, and insurance agents need to frequently consult with professional underwriters in the background during their daily business. However, manual underwriting assessment is inefficient, and the processing capacity is obviously insufficient when facing large-scale business, making it difficult to meet the market's demand for rapid response, which in turn leads to delayed feedback, seriously affecting customer experience and business turnover speed.

 

Business process:

 

Dialogue question generation: This module abandons the traditional fixed questionnaire filling mode and adopts a dialogue mode driven by a large model. The system will automatically generate necessary follow-up questions based on the user's current answers and the underwriting rules and requirements of different insurance products to achieve personalized questioning. This method greatly reduces the number of unnecessary questions and is no longer limited to boilerplate answers. It greatly lowers the understanding threshold of the insured, significantly enhances the flexibility of the entire interactive process, and improves the quality and efficiency of information collection.

 

Information collection and organization: This module uses a big model to classify materials from customer conversations, medical reports, identity documents, and other information, accurately parse and extract key information, and then summarize and organize it and submit it to the underwriting engine for underwriting decision-making.

 

The underwriting engine makes decisions based on the submitted materials, combined with the underwriting rules, the customer's risk assessment results, the characteristics of the insurance product and the insurance company's underwriting policy, and provides the underwriter with possible underwriting conclusions, including standard underwriting, conditional underwriting or rejection of insurance.

 

Generate pre-underwriting suggestions based on the underwriting conclusions of the underwriting engine, and use big models to provide targeted suggestions to agents and customers, helping them to prepare mentally and take countermeasures in advance. For example, it may recommend that customers undergo further physical examinations or propose premium underwriting plans based on certain risk factors.

 

Value Analysis:

 

Improve underwriting efficiency. With its powerful natural semantic dialogue and information collection capabilities, the big model can quickly understand customer information and assist agents in quickly determining the basic insurance qualifications of customers. It can screen out customers who are obviously not eligible for insurance or have high risks in advance to prevent these customers from entering the formal underwriting process, thereby reducing the waste of underwriting resources and improving overall underwriting efficiency.

 

Optimize customer experience, adopt a more humane dialogue mode to replace the traditional cumbersome questionnaire, intelligently generate follow-up questions based on customer answers, lower the threshold for customer understanding, and improve the efficiency and quality of information collection. When interacting with the system, customers can express their own situation more easily and understand the possibility of insurance in advance. In addition, the big model can also provide customers with personalized suggestions based on the underwriting conclusions, such as guiding customers to supplement information and choose suitable insurance products, so that customers can feel more professional and considerate services during the insurance process, greatly improving the customer insurance experience.

 

Reduce adverse selection risk. By integrating multi-dimensional customer information and combining insurance product characteristics and underwriting policies, the big model can accurately identify potential high-risk customers. Based on the evaluation results of the big model, agents can effectively guide or reject customers with significantly higher risks and who do not meet the underwriting conditions, avoiding excessive concentration of high-risk customers in insurance, thereby reducing the adverse selection risk of insurance companies, maintaining the fairness and stability of the insurance market, and ensuring the sustainable and healthy development of the insurance business.

 

2.2.2  Claims Verification Assistance

 

Agent scenario:

 

The notable features of insurance products are the complexity of their clauses and the increasingly refined definition of responsibilities. For example, hospital grades, hospital lists, disease lists, drug types/lists, or designated pharmacies are usually agreed upon. These detailed definitions of responsibilities directly lead to the complexity of the materials required for the claims process. This complexity not only increases the risk of fraud and leakage, but also places higher demands on audit work: auditors need to remember a large number of audit rules and operating procedures, and also need to have a deep medical background to deal with complex medical situations. This undoubtedly increases the difficulty for auditors to understand and uniformly implement audit standards, making it difficult to efficiently accumulate and pass on valuable experience in historical audits.

 

The big model is used to learn insurance products, policies and claims information, and combined with the medical knowledge graph, to achieve intelligent review of claims cases. This process can accurately evaluate the applicability of clauses, determine compensation items and predict risks, while clearly displaying the reasoning logic and basis. This innovation greatly reduces the work of claims auditors, so that they no longer need to tediously check insurance policies, compare clauses and mark risks, but can directly jump to the efficient decision-making stage.

 

Demand Analysis:

 

There are many problems in the operation of the insurance industry, which are mainly reflected in the following aspects:

 

The terms are complex and the learning cost is high. There are many insurance products, and comprehensive insurance companies often have more than 10,000 products. The terms change rapidly due to supervision, competition and product iteration. Learning the terms requires professional knowledge and is costly.

 

There is a shortage of expert resources, and there are few medical professionals among claims adjusters. However, the proportion of medical, disability, and death claims in health insurance and accident insurance is high, and the review of key claims items lacks professional medical knowledge support.

 

It is difficult to disassemble the structure. The insurance clauses are complicated and involve many aspects, such as underwriting, liability, and exemption. Insurance terminology is different from industry standards. The knowledge level and cognition of each business department are different, and the front-end and back-end departments also have deviations in interpreting the clauses.

 

Business process:

 

Create an intelligent entity to assist claims auditors, which can realize the big model to assist in liability determination, risk identification, and claims determination. Functions include trial clauses  -  liability determination, exemption  /  deductible determination, compensation scope determination, risk identification and discovery, etc., and can output determination conclusions and related basis to assist claims auditors in case review. Combined with the existing claims system, the big model is introduced to supplement and improve capabilities in the following three core links:

 

The document-structured processing uses advanced large language models and visual large model technologies to deeply analyze the specific content of insurance clauses and medical event-related information and convert them into structured data. In this process, the use of standardized terms is unified and standardized data structures are formed, thus providing a solid foundation for subsequent processing and analysis.

 

The insurance clause knowledge base is constructed through system integration and analysis to comprehensively sort out various information of insurance products, such as insurance conditions, coverage, premium structure and exemption clauses, to form a unified and authoritative interpretation system. This knowledge base will become an important reference for claims work, ensuring an accurate understanding of insurance clauses.

 

Assisted audit decision support, giving full play to the powerful reasoning ability of the big model, conducts in-depth analysis of the claim application based on the claim materials submitted by the user and the claim requirements information stipulated in the insurance terms. It effectively makes up for the shortcomings of traditional rule engines in semantic auditing, and gives accurate semantic audit judgments in the form of natural language, providing strong support for audit decisions.

 

Value Analysis:

 

Improve customer claims experience. The big model can quickly understand and analyze insurance terms, policies and claim information, assist claims auditors to make accurate judgments quickly, advance risk control, shorten the claims cycle, and improve customer satisfaction.

 

Optimize resource utilization and reduce costs. Through intelligent review, large models can reduce the burden of manual review, reasonably allocate expert resources, reduce learning and maintenance costs, and improve the review capabilities of the overall team.

 

2.2.3  Intelligent Image Processing

 

Agent scenario:

 

Key business processes in the insurance industry, such as underwriting and claims settlement, are highly dependent on the processing of image data. In the extraction and classification of image information, traditional methods have obvious drawbacks. For example, when processing card images, it is necessary to design a separate layout for each image to train the recognition model, which requires the annotation of massive data sets, which consumes a lot of manpower, material resources and time, and is costly. Moreover, traditional methods have poor adaptability. Once a new image layout appears, it is difficult to adapt, and the complex training process has to be restarted, which seriously slows down the processing progress of subsequent business.

 

The introduction of visual big data model technology has brought major business and technical breakthroughs to the insurance industry. The big model has good generalization capabilities and can accurately understand and efficiently infer image content in the case of zero samples. There is no need to train for different formats separately, which greatly simplifies the operation. Through this technology, image processing in the insurance industry can be highly automated and intelligent, with greatly improved operating efficiency and recognition accuracy, and the mining value of image data has also been further enhanced, effectively ensuring the compliance of business operations and providing a strong boost to the development of the insurance industry.

 

Demand Analysis:

 

The image processing efficiency is low, and the user experience is poor. When users submit materials during underwriting and claims, they need to manually classify and upload various materials, such as material diagnosis certificates, and fill in a large amount of information. The operation process is cumbersome and complicated. Traditional image processing technology requires separate layout training recognition models for different types of image materials, such as cards, and annotates large data sets. The process is long and costly, and it is even more difficult to adapt to new layouts and extraction fields, which seriously affects processing efficiency.

 

The value of historical image archives has not been fully explored. A large amount of historical image data has been accumulated in the process of insurance business processing, covering various types of documents, photos and videos. In the traditional way, due to the lack of effective mining technology for different images, it is difficult to extract key content such as customer personal information, purchase behavior, and claims records from these dormant historical image data. As a result, insurance companies are unable to conduct in-depth customer behavior analysis based on this data to provide personalized services.

 

Insufficient risk identification and assessment capabilities lead to frequent false insurance and fraud in the insurance business. Current technical means make it difficult to accurately identify the authenticity and logical rationality of image files. For example, in the auto insurance claims scenario, it is difficult to accurately assess the degree of vehicle damage and repair costs; in the health insurance claims review, the ability to identify abnormalities in medical images is insufficient, and the accuracy of determining liability is low. These problems seriously affect the risk prevention and control of the insurance business.

 

Business process:

 

Document image classification, big model intelligent imaging technology provides a direct and effective solution to the pain points of image data processing. The big model can automatically identify the document type and accurately classify the information in the document. This solution significantly improves the efficiency and accuracy of image processing, reduces human errors, ensures the accuracy of image classification, and thus enhances the ability to control risks.

 

Image information extraction, with the help of large-model image recognition technology, structured information extraction and meaning extraction of various standard or non-standard images and documents, such as identity information, property certificates, outpatient invoices, insurance applications, co-insurance agreements, bank credit authorization letters, medical records, medical test reports, public photos, etc.

 

Image semantic understanding, with the help of big model semantic understanding. The big visual model can quickly identify and analyze key information in image data, such as vehicle damage, medical image abnormalities, etc., thereby accelerating the claims process and improving claims efficiency.

 

Value Analysis:

 

Efficiency has been significantly improved. After the introduction of the visual big model, material classification and key information extraction are automated, and users are only required to supplement when information is missing. This change has greatly optimized the user experience in the underwriting and claims process, while significantly improving manual operation efficiency and greatly reducing the time and energy costs required for manual processing.

 

Significant cost savings: large models have excellent generalization capabilities and can cope with new materials, thus replacing the traditional, cumbersome model training process. There is no need to annotate massive data sets, which greatly reduces the manpower, material resources and time costs required to train recognition models and annotate data sets separately.

 

Deeply mining the value of data, with the help of large-scale image recognition and semantic understanding technology, can accurately extract key information from massive historical image data, providing rich data support for customer behavior analysis. Based on this data, insurance companies can have a deeper understanding of customer needs, and then provide personalized services, effectively enhance customer stickiness, and improve market competitiveness.

 

2.3  Regulatory Compliance

 

2.3.1  Smart Verification Assistant

 

Agent scenario:

 

In the development of insurance products, the formulation of insurance clauses is extremely critical. It is strictly supervised by external regulatory agencies and must comply with the company's internal regulations. According to regulations, insurance products must complete the submission process through an intelligent verification system. The China Banking and Insurance Regulatory Commission strictly verifies from multiple dimensions based on more than  600  rules in more than  80  documents. However, the current product clause verification rules are complicated, and the review is time-consuming and laborious. Auditors are prone to misjudgment and omissions, and potential risks are often discovered only after the fact, which seriously affects the product launch cycle.

 

Relying on big model technology, the clause intelligent verification assistant can accurately parse and extract rules, and compare them with product clauses to find problems. Manual review can quickly locate and correct defects in clauses based on the prompts of the intelligent body, and realize pre-automatic supervision. This innovative model effectively improves the efficiency and accuracy of insurance product development, enhances timeliness, reduces potential risks, and improves the quality of reporting.

 

Demand Analysis:

 

Traditional audit methods can no longer meet the current business development needs, mainly reflected in the following aspects:

 

With the increasing difficulty of supervision and the limitations of manual review, as regulatory requirements continue to increase, the complexity of insurance terms filing has increased significantly. The traditional manual review model is unable to efficiently deal with a large number of term documents, resulting in an extension of the review cycle, which seriously affects the speed of new products to market.

 

Professional knowledge barriers and unstable audit quality. Insurance clause audits involve many professional terms and complex legal provisions, which require extremely high professional knowledge of auditors. However, non-professionals are not competent for this job, and the training cycle for professional talents is long and costly. In addition, manual audits are prone to errors. Even experienced people may miss or misjudge due to the large number of clauses, affecting the audit quality.

 

The delayed response to regulatory updates and inconsistent audit standards, frequent changes in regulatory policies, and the time lag in manually tracking and adjusting clauses may result in new products not being able to meet the latest requirements in a timely manner. At the same time, different auditors may have different understandings of the same issue, resulting in inconsistent audit results, increasing the repetitiveness and uncertainty of audits.

 

Business process:

 

1. Build a comprehensive clause rule base

 

Regulatory rules library: Comprehensively sort out the regulatory rules for industry products, use the natural language understanding and extraction capabilities of the large language model to intelligently parse and match regulatory rules, and establish a detailed rule library. This rule library will cover all internal and external regulatory requirements to ensure that these rules can be quickly and accurately applied to all of the company's businesses.

 

Custom rules: To cope with future regulatory changes, a rule editing function is added to support operations such as adding, modifying, and deleting to ensure that the rule base is synchronized with the latest regulatory requirements in real time.

 

2. Implement an intelligent verification function

 

Based on the constructed rule base, the large model intelligent verification function is used to realize the automatic verification of regulatory rules. Specifically including:

 

Automated rule verification: After the business uploads the terms document, the big model quickly parses the text and compares and analyzes each item based on the rule library to ensure compliance with the terms.

 

Intelligent modification suggestions: For clauses that do not meet regulatory requirements, the big model will provide specific and detailed modification suggestions. These suggestions will cover multiple aspects such as wording standardization and logical structure optimization to help the business team quickly and accurately adjust the clause content.

 

3. Output verification results. After the review is completed, the system automatically generates conclusions and gives scientific and reasonable rectification suggestions. These suggestions are based on large-scale intelligent analysis, combined with industry practices and regulatory requirements, to provide business teams with clear and feasible rectification paths.

 

Value Analysis:

 

Improved efficiency and accuracy, automated verification and intelligent suggestions significantly shorten terms processing time, reduce errors, and accelerate product launch.

 

Reduce operating costs, reduce manpower requirements and training costs, optimize resource allocation, and reduce overall operating costs.

 

Strengthen compliance management, quickly adapt to regulatory changes, unify audit standards, ensure regulatory compliance, and effectively reduce legal risks.

 

2.3.2  Articles Smart Transfer Assistant

 

Agent scenario:

 

In the insurance industry, the preparation and formulation of insurance clauses is not only complex and crucial but also subject to strict supervision by regulators. In 2024, the General Office of the State Financial Supervision and Administration Bureau successively issued the "Notice on the Activation of the Intelligent Verification System for Property Insurance Products" (Jinban Bianhan [ 2024 ] No. 1117 ) and the "Notice on the Activation of the Intelligent Verification System for Agricultural Insurance Products" (Jinban Bianhan [ 2024 ] No. 1521 ). According to the requirements, in addition to the original text of the clauses, rate regulations and actuarial reports, they must also be converted into an element table that meets regulatory requirements and uploaded to the system for review.   

 

The Smart Clause Transfer Assistant helps insurance company personnel intelligently extract clauses, rate regulations, and actuarial report contents, and automatically generate an element table in Excel format in accordance with the regulatory requirements. At the same time, the generated element table is verified according to the element rules to determine whether it meets the requirements and give reasonable suggestions. The assistant significantly improves the efficiency and accuracy of clause regulatory reporting, effectively reduces human errors, and speeds up product launch.  

 

Demand Analysis:

 

If an insurance company wants to sell an insurance policy, it needs to go through a series of manual operations such as manual conversion, manual verification, and multiple approvals. This makes the entire process inefficient, time-consuming, and error-prone, seriously affecting the speed of product launch. The specific business pain points are as follows:

 

 Manual conversion is inefficient. Clauses, rate regulations and actuarial reports need to be manually converted into element table formats that meet regulatory requirements. That means at least 6  documents need to be processed for each clause that is put on the shelves, which consumes a lot of time and manpower.

 

Manual verification is cumbersome, and the content of each element table needs to be manually edited and processed by business personnel. They also need to check the rules one by one. For example, if there are requirements on the number of words and the Chinese and English versions of symbols, business personnel need to check them one by one, which is time-consuming and laborious.

 

The approval process is time-consuming, and the terms and conditions report must go through multiple rounds of internal approval and verification. Everyone has to check all the contents from 0-1, which is a cumbersome and error-prone process. This further prolongs the time to market and reduces overall efficiency.

 

High error rate risk: Manual operations increase the possibility of errors, which may result in clauses not meeting regulatory requirements, delaying the review process or being returned for re-review.

 

Targeted business goal construction: Automatic generation of factor tables, automatically filling in the factor tables for regulatory review, generating verification reports, and reducing the pressure of manual review. Factor table information check, re-verify whether the factor content is in compliance when generating the factor table.

 

Business process:

 

Based on the big model technology, the terms, rate regulations, and actuarial reports are extracted as required, and the correctness of each element of information is verified, and finally the Json format output is generated.

 

Material preprocessing mainly involves text analysis of original documents (such as PDF or Word documents) to convert them into a format that can be processed by the big model, and then classifying the insurance types of the documents through the big model.    

 

The information extraction function is responsible for extracting specific element information from documents, such as the type of insurance product, coverage, premium calculation method, claim conditions, etc. It will identify and extract this information based on predefined patterns or rules.

 

Structured output, converting validated data into a structured format, such as  JSON . This module is responsible for converting unstructured text information into a machine-readable structured data format for subsequent processing or integration into other systems.

 

Value Analysis:

 

Efficiency and cost optimization: With the help of automatic generation of feature tables, the time and labor costs of manual conversion and editing are reduced. Intelligent verification can quickly identify and correct non-compliance with rules, greatly improving work efficiency.

 

Accuracy and compliance assurance: Use big models to accurately extract terms, rate regulations and actuarial report contents, ensure that the generated element tables are standardized in format and accurate in content, strictly comply with regulatory requirements, and reduce the risk of being returned for reexamination.

 

The process and experience are upgraded. By verifying the results through large model rules, repetitive internal approval work can be reduced, the clause reporting process can be optimized, and the time to market for products can be shortened. At the same time, the workload of business personnel can be reduced, helping them focus on high-value tasks. 

 

2.3.3  External Disclosure Review

 

Agent scenario:

 

Insurance companies' daily operations are highly dependent on Internet platforms for information disclosure, covering product details, brand promotion, marketing, advertising, user education and other aspects. This information must not only comply with basic language standards, be accurate, clear and unambiguous, but also strictly comply with the laws and regulations of the financial industry. At present, insurance companies generally adopt a combination of manual and system audit methods to strive to conduct a detailed review of disclosed materials. However, many problems are still exposed in actual operations, such as the lack of consistency in audit standards, low efficiency of manual audits, and complex regulations that are difficult to fully grasp.

 

The External Disclosure Audit Assistant is based on the multimodal understanding and audit capabilities of large language and visual multimodal models, and is designed to provide comprehensive and efficient external information disclosure audit capabilities for the marketing, brand management, and compliance departments of insurance companies.

 

Demand Analysis:

 

The current information disclosure review has the following pain points:

 

Audit standards are not unified. Different insurance companies or audit teams may use different audit standards, which may affect the accuracy and consistency of information disclosure. Such inconsistency may cause confusion and misunderstanding among consumers, and further damage the reputation of insurance companies.

 

Manual review is inefficient. Faced with a large number of information disclosure requirements, manual review is often time-consuming and error-prone. This not only increases operating costs but may also lead to delays in information disclosure, affecting the insurance company's market response speed and customer experience.

 

Regulations are complex. Faced with the complex and ever-changing regulatory requirements of the financial industry, auditors need to not only have comprehensive professional knowledge but also keep abreast of the latest regulations. However, this requirement is often difficult to fully meet.

 

Business process:

 

The rule base is classified and constructed according to the material usage scenarios. The audit rules under each category contain multiple checkpoints. Each checkpoint is configured with detailed information such as audit rules, verification rules, audit tools, and prompt words.

 

 

The big model integrates the language big model, the visual big model and the document parsing model, which can realize natural language understanding, text and video review reasoning, and generate accurate review conclusions.

 

 

Document parsing: receiving materials submitted externally, parsing and converting them into a document format recognizable by the big model, and classifying materials based on content, laying the foundation for subsequent review.

 

 

Audit execution: load the corresponding rules from the rule library according to the material category, and carry out the audit work according to the audit point configuration definition with the help of the rule engine combined with the big model.

 

 

Generate conclusions. The big model automatically generates an audit report and provides targeted rectification suggestions, which are then handed over to humans for final rectification and improvement, thus achieving an efficient working mode of human-machine collaboration.

 

Value Analysis:

 

Improve audit efficiency. With the powerful computing and analysis capabilities of the big model, it can process a large amount of information disclosure materials in a short time, greatly shortening the audit cycle. In the past, it might have taken several hours or even longer to manually review a complex product promotional material. After the introduction of the audit agent, the review time for the same content can be shortened to more than ten minutes or even shorter, greatly improving the timeliness of information disclosure.  

 

Reduce compliance risks. The rule base fully integrates various regulatory requirements of the financial industry. The big model can be accurately compared during the review process to ensure that every piece of information disclosed to the outside is in strict compliance with regulatory standards. This effectively avoids risks such as regulatory penalties and legal proceedings caused by illegal disclosure, and reduces potential economic losses and reputation damage costs.