Insurance insurance preservation list standardization processing system based on AI large model

Abstract

The invention discloses an insurance buying preservation list standardization processing system based on an AI large model, and the system comprises a data collection module, a data preprocessing module, an AI large model processing module, an auditing and verification module, a result output module, and a system monitoring and optimization module. And the data acquisition module is configured with a data source to ensure that data can be stably and accurately acquired. A user submits insurance buying and preservation list data through a system interface or an API (Application Program Interface), a data acquisition module acquires data from a specified source and transmits the data to a data preprocessing module, and the data preprocessing module formats and standardizes the original data and transmits the original data to a server; and the AI large model processing module performs intelligent analysis and processing on the preprocessed data, so that intelligent and automatic processing of insurance buying and preservation lists can be realized, the processing efficiency and accuracy are improved, and the manual intervention cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of standardized processing systems for insurance application and preservation lists of personal insurance, and more specifically to a standardized processing system for insurance application and preservation lists based on an AI large model. Background Art

[0002] Insurance is an important part of the social and economic security system. With the continuous development of the social economy and the continuous improvement of residents' security awareness, the scope covered by insurance business is getting wider and wider. Effectively realizing risk identification by insurance companies is the most critical technical issue in the process of undertaking insurance business.

[0003] The insurance application and preservation list is an important bridge of communication between the applicant and the insurance company, which ensures that both parties have a clear and accurate understanding of the status of the insurance contract. At the same time, this list is also an important basis for the applicant to safeguard their own rights and interests and carry out subsequent operations such as claim applications.

[0004] The traditional processing process of insurance application and preservation lists often relies on manual operations and cumbersome review processes, resulting in low processing efficiency and inability to meet the rapid response requirements of modern insurance business. In the manual processing process, problems such as data entry errors, omissions or understanding deviations are likely to occur, resulting in insufficient accuracy of the processing results. Therefore, a new technical solution is needed to solve this problem. Summary of the Invention

[0005] The purpose of the present invention is to provide a standardized processing system for insurance application and preservation lists based on an AI large model, which solves the problems that the traditional processing process of insurance application and preservation lists often relies on manual operations and cumbersome review processes, resulting in low processing efficiency and inability to meet the rapid response requirements of modern insurance business. In the manual processing process, problems such as data entry errors, omissions or understanding deviations are likely to occur, resulting in insufficient accuracy of the processing results.

[0006] To achieve the above object, the present invention provides the following technical solutions: In the insurance application and preservation list standardization processing system based on the AI large model, there are a data acquisition module, a data preprocessing module, an AI large model processing module, an audit and verification module, a result output module, and a system monitoring and optimization module. The data acquisition module configures the data source to ensure stable and accurate data acquisition, and performs preliminary cleaning on the acquired data to remove invalid or redundant information. The data preprocessing module defines data formats and standards, such as date formats and numerical formats, and uses AI algorithms to perform intelligent cleaning and conversion of the data, automatically filling in missing values and correcting incorrect data. The AI large model processing module inputs the preprocessed data into the model for intelligent analysis, such as risk assessment and compliance checking, obtains the model output results, and performs subsequent processing according to business requirements. The audit and verification module sets audit rules and verification standards, automatically or manually audits and verifies the data output by the AI model, and marks and processes the data that does not conform to the rules. The result output module defines the output format and style, outputs the audited data in the specified format, and provides a download function to facilitate users to obtain and use the standardized list. The system monitoring and optimization module sets monitoring indicators and alarm rules, and real-time monitors the system operation status, including data processing speed and model accuracy. According to the monitoring results, the system is optimized and improved, and the model parameters are adjusted and the data processing process is optimized.

[0007] As a preferred embodiment of the present invention, the data acquisition module includes a data source interface, a data scraping tool, a data caching system, and a data preliminary cleaning component.

[0008] As a preferred embodiment of the present invention, the data preprocessing module includes a data format definition module, a data conversion tool, a missing value processing component, and an incorrect data correction tool.

[0009] As a preferred embodiment of the present invention, the AI large model processing module includes a model loading component, a data input interface, a model inference engine, and a result output component.

[0010] As a preferred embodiment of the present invention, the audit and verification module includes a rule definition module, an automatic audit module, a manual audit module, and a non-conforming data processing module.

[0011] As a preferred embodiment of the present invention, the result output module includes an output format definition module, a data formatting tool, a file production component, and a download function module.

[0012] As a preferred embodiment of the present invention, the system monitoring and optimization module includes a monitoring indicator definition module, a real-time monitoring tool, an alarm system, and a system optimization component.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] In the present invention, users submit insurance application and endorsement list data through the system interface or API interface. The data acquisition module collects data from the specified source and transmits it to the data preprocessing module. The data preprocessing module formats and standardizes the original data. The AI large model processing module performs intelligent analysis and processing on the preprocessed data. The review and verification module reviews and verifies the data output by the AI model. The result output module formats and outputs the data that passes the review to generate a standardized insurance application and endorsement list. Users obtain the standardized list through the system interface or API interface. The system monitoring and optimization module monitors the running status of the entire system in real time and optimizes and improves the system based on the monitoring results. The insurance application and endorsement list standardization processing system based on the AI large model can realize the intelligent and automated processing of insurance application and endorsement lists, improve the processing efficiency and accuracy, and reduce the manual intervention cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the insurance application and endorsement list standardization processing system based on the AI large model of the present invention;

[0016] Figure 2 It is a schematic diagram of the process of the data acquisition module of the present invention;

[0017] Figure 3 It is a schematic diagram of the process of the data preprocessing module of the present invention;

[0018] Figure 4 It is a schematic diagram of the process of the AI large model processing module of the present invention;

[0019] Figure 5 It is a schematic diagram of the process of the review and verification module of the present invention;

[0020] Figure 6 It is a schematic diagram of the process of the result output module of the present invention;

[0021] Figure 7 It is a schematic diagram of the process of the system monitoring and optimization module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer toFigure 1-7 , the present invention provides a technical solution: In the insurance application and preservation list standardization processing system based on the AI large model, it includes a data collection module, a data preprocessing module, an AI large model processing module, an audit and verification module, a result output module, and a system monitoring and optimization module. The data collection module configures data sources to ensure stable and accurate data collection, and performs preliminary cleaning on the collected data to remove invalid or redundant information. The data preprocessing module defines data formats and standards, such as date formats and numerical formats, and uses AI algorithms to perform intelligent cleaning and conversion of the data, automatically filling in missing values and correcting incorrect data. The AI large model processing module inputs the preprocessed data into the model for intelligent analysis, such as risk assessment and compliance check, obtains the model output results, and performs subsequent processing according to business requirements. The audit and verification module sets audit rules and verification standards, automatically or manually audits and verifies the data output by the AI model, marks and processes the data that does not conform to the rules. The result output module defines the output format and style, outputs the audited data in the specified format, and provides a download function to facilitate users to obtain and use the standardized list. The system monitoring and optimization module sets monitoring indicators and alarm rules, and real-time monitors the system operation status, including data processing speed and model accuracy. According to the monitoring results, the system is optimized and improved, and the model parameters are adjusted and the data processing process is optimized.

[0024] Further improved, such as Figure 2 shown: The data collection module includes a data source interface, a data scraping tool, a data caching system, and a data preliminary cleaning component;

[0025] The data source interface is used to connect different data sources, such as databases, file servers, API interfaces, etc.

[0026] The data scraping tool automatically scrapes data from the data source according to the configuration, and supports scheduled scraping and real-time scraping.

[0027] The data caching system temporarily stores the scraped data to ensure the stability and reliability of the data during transmission and processing.

[0028] The data preliminary cleaning component performs preliminary inspection and cleaning on the scraped data to remove invalid, duplicate, or incorrectly formatted data.

[0029] Further improved, such as Figure 3 shown: The data preprocessing module includes a data format definition module, a data conversion tool, a missing value processing component, and an incorrect data correction tool;

[0030] The data format definition component defines the standard format of the data, including the formats of fields such as dates, numerical values, and texts.

[0031] The data conversion tool converts the original data into a standard format according to the defined data format.

[0032] The missing value processing component automatically identifies and processes the missing values in the data, such as filling default values, using algorithms for prediction, etc.

[0033] The error data correction tool detects and corrects the errors in the data, such as spelling mistakes, numerical range errors, etc.

[0034] Further improved, such as Figure 4 As shown: The AI large model processing module includes a model loading component, a data input interface, a model inference engine, and a result output component;

[0035] The model loading component is responsible for loading the trained AI large model.

[0036] The data input interface receives the preprocessed data and inputs it into the AI model.

[0037] The model inference engine executes the inference process of the AI model and performs intelligent analysis on the input data.

[0038] The result output component obtains the results of the model inference and performs necessary formatting processing.

[0039] Further improved, such as Figure 5 As shown: The review and verification module includes a rule definition module, an automatic review module, a manual review module, and a non-compliant data processing module;

[0040] The rule definition component is used to define the rules and standards for review and verification.

[0041] The automatic review tool automatically reviews the data output by the AI model according to the defined rules.

[0042] The manual review interface provides an interface for manual review, supporting the review and modification of the automatic review results.

[0043] The non-compliant data processing component marks and processes the data that does not conform to the rules, such as returning it for modification, recording logs.

[0044] Further improved, such as Figure 6 As shown: The result output module includes an output format definition module, a data formatting tool, a file production component, and a download function module;

[0045] The output format definition module defines the output format and style of the standardized list.

[0046] The data formatting tool formats the approved data according to the specified format.

[0047] The document generation component generates standardized insurance application and maintenance list documents, such as Excel, PDF, etc.

[0048] The download function module provides users with the functions of downloading and sharing standardized lists.

[0049] Further improved, such as Figure 7 As shown: The system monitoring and optimization module includes a monitoring index definition module, a real-time monitoring tool, an alarm system, and a system optimization component;

[0050] The monitoring index definition module defines system indexes that need to be monitored, such as data processing speed, model accuracy, etc.

[0051] The real-time monitoring tool monitors the running status and index data of the system in real time.

[0052] The alarm system triggers an alarm when the system index is abnormal and notifies relevant personnel for handling.

[0053] The system optimization component optimizes and improves the system according to the monitoring results, such as adjusting model parameters, optimizing the data processing process, etc.

[0054] Working principle: The data acquisition module is responsible for efficiently obtaining the original data of the insurance application and endorsement list from various data sources. This module establishes connections with databases, file servers, or API interfaces through data source interfaces, and uses data scraping tools to achieve scheduled or real-time data scraping. The data caching system ensures the stability and reliability of data during transmission and processing. At the same time, the preliminary data cleaning component will conduct preliminary inspections and cleaning on the scraped data to provide high-quality data input for subsequent modules; the data preprocessing module formats and cleans the collected original data to ensure its compliance with the requirements of AI large model processing. The data format definition component defines the standard format of the data, including the formats of fields such as dates, numerical values, and texts. The data conversion tool then converts the original data into the standard format according to these definitions. This module also includes a missing value processing component and an error data correction tool, which are used to automatically identify and process missing values and errors in the data to improve the accuracy and integrity of the data; the AI large model processing module uses advanced AI technologies to perform intelligent analysis on the preprocessed data. The model loading component is responsible for loading the trained AI large model. The data input interface inputs the preprocessed data into the model. The model inference engine then executes the inference process of the AI model, conducts in-depth learning and analysis on the input data to extract key information and make predictions. The result output component formats the results of the model inference to provide a standardized output for subsequent modules; the review and verification module strictly reviews and verifies the data output by the AI large model to ensure its accuracy and compliance. The rule definition component is used to define the rules and standards for review and verification. These rules cover all aspects of the insurance application and endorsement list. The automatic review tool preliminarily reviews the data output by the AI model according to these rules, while the manual review interface provides the functions of manual review and modification. The non-compliant data processing component marks and processes the data that does not conform to the rules to ensure the accuracy and integrity of the list. Through the combined actions of modules such as data acquisition, preprocessing, AI large model processing, review and verification, result output, and system monitoring and optimization, the insurance application and endorsement list standardization processing system realizes the efficient, accurate, and standardized processing of the insurance application and endorsement list.

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0056] The following points should be noted: First, in the description of this application, it should be noted that unless otherwise specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. It can be a mechanical connection or an electrical connection, or it can be the communication inside two components. It can be directly connected. "Upper", "lower", "left", "right", etc. are only used to represent the relative position relationship. When the absolute position of the object being described changes, the relative position relationship may change.

[0057] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An insurance application and preservation list standardization processing system based on an AI large model, characterized in that: The insurance application and preservation list standardization processing system based on the AI large model includes a data collection module, a data preprocessing module, an AI large model processing module, an audit and verification module, a result output module, and a system monitoring and optimization module. The data collection module configures data sources to ensure stable and accurate data collection, and performs preliminary cleaning on the collected data to remove invalid or redundant information. The data preprocessing module defines data formats and standards, such as date formats and numerical formats, and uses AI algorithms to perform intelligent cleaning and conversion of the data, automatically filling in missing values and correcting incorrect data. The AI large model processing module inputs the preprocessed data into the model for intelligent analysis, such as risk assessment and compliance checks, obtains the model output results, and performs subsequent processing according to business requirements. The audit and verification module sets audit rules and verification standards, automatically or manually audits and verifies the data output by the AI model, and marks and processes the data that does not conform to the rules. The result output module defines the output format and style, outputs the audited data in the specified format, and provides a download function to facilitate users to obtain and use the standardized list. The system monitoring and optimization module sets monitoring indicators and alarm rules, and monitors the running status of the system in real time, including data processing speed and model accuracy. According to the monitoring results, the system is optimized and improved, and the model parameters are adjusted and the data processing process is optimized.

2. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, characterized in that: The data collection module includes a data source interface, a data scraping tool, a data caching system, and a data preliminary cleaning component.

3. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, characterized in that: The data preprocessing module includes a data format definition module, a data conversion tool, a missing value processing component, and an incorrect data correction tool.

4. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, wherein: The AI large model processing module includes a model loading component, a data input interface, a model inference engine, and a result output component.

5. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, characterized in that: The audit and verification module includes a rule definition module, an automatic audit module, a manual audit module, and a non-compliant data processing module.

6. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, characterized in that: The result output module includes an output format definition module, a data formatting tool, a file production component, and a download function module.

7. The insurance application and preservation list standardization processing system based on the AI large model according to claim 1, characterized in that: The system monitoring and optimization module includes a monitoring indicator definition module, a real-time monitoring tool, an alarm system, and a system optimization component.

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