Group insurance policy claim settlement high-risk time sequence early warning and analysis method based on SQL

Abstract

The invention relates to the technical field of insurance risk assessment, and discloses a group insurance policy claim settlement high-risk time sequence early warning and analysis method based on SQL, and the method comprises the following steps: a data collection and preprocessing module which collects basic data related to group insurance policy claim settlement from a plurality of business databases at regular intervals, comprising but not limited to insurance policy information, claim settlement case details and group member information, the collected data is cleaned and standardized to ensure the quality and consistency of the data, and a reliable data basis is provided for subsequent analysis. And the time sequence feature extraction module is used for extracting key time sequence features related to the group insurance policy claim risk based on the preprocessed data by using an SQL query statement. Through constructing the group insurance policy claim settlement high-risk time sequence early warning and analysis method based on the SQL, analysis of the group insurance policy claim settlement risk is realized, and at the same time, the powerful data processing and query functions of the SQL language are utilized to show a significant efficiency advantage in the aspects of group insurance policy claim settlement risk management and analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of insurance risk assessment, in particular to a SQL-based group insurance policy claim high-risk timing early warning and analysis method. BACKGROUND

[0002] Under the background of rapid expansion of insurance business today, group insurance policy claim risk control is becoming increasingly important. Although the existing technology can realize the basic claim process, it has obvious shortcomings. On the one hand, data collection and integration are difficult, relying on manual collection of data from multiple business systems, which is low in efficiency and prone to errors, and the data cleaning and conversion link is also prone to data loss or format inconsistency due to complex rules, affecting data accuracy and usability. On the other hand, the risk assessment is not accurate enough, and the traditional method mainly analyzes from a single dimension, ignoring key factors such as claim frequency, claim periodicity and timing trend, making it difficult to fully reflect the real risk situation. Moreover, the early warning mechanism is lagging, and the existing system is mostly based on fixed threshold early warning, which cannot adapt to the dynamic changes of claim risk and market fluctuations, making it difficult to capture potential high-risk claims in a timely manner and miss the opportunity to prevent risks. In addition, the data processing efficiency is low, and the traditional method is difficult to meet the rapid processing and analysis needs of massive claim data, with a long cycle from data collection to risk assessment to decision support, affecting the timely response and effective control of insurance companies to group insurance policy claim risks, and difficult to meet the needs of stable development and market competition of insurance business.

[0003] Therefore, the SQL-based group insurance policy claim high-risk timing early warning and analysis method is proposed by the person skilled in the art to solve the above problems. SUMMARY

[0004] In view of the shortcomings of the prior art, the SQL-based group insurance policy claim high-risk timing early warning and analysis method is provided to solve the problems raised in the background art.

[0005] To achieve the above purpose, the following technical scheme is adopted: the SQL-based group insurance policy claim high-risk timing early warning and analysis method comprises the following steps: Data acquisition and preprocessing module: periodically acquire basic data related to group insurance policy claims from multiple business databases, including but not limited to policy information, claim case details, group member information, and perform cleaning, conversion and standardization processing on the acquired data to ensure data quality and consistency, providing a reliable data foundation for subsequent analysis; The time-series feature extraction module uses SQL query statements to extract key time-series features related to group policy claims risk based on preprocessed data. These features cover multiple dimensions such as claim frequency, claim amount change trend over time, claim periodicity of different groups or group members, and claim case processing time. Through in-depth mining of these features, a comprehensive and detailed time-series feature set is constructed for risk assessment. Risk assessment model construction module: Constructs a time-series risk assessment model based on SQL calculation. This model takes the extracted time-series features as input parameters and combines them with preset risk assessment algorithms, such as statistical methods or machine learning algorithms based on time-series data, such as moving average, exponential smoothing, etc., to quantitatively assess the real-time risk level of group policy claims and generate assessment results containing key information such as risk score and risk level, so as to reflect the dynamic changes in the risk of group policy claims in a timely manner. Warning threshold setting and triggering module: Based on historical claims data of group insurance policies, industry experience and risk preferences, reasonable risk warning thresholds are set using SQL statements. These thresholds are dynamically adjustable. When the output of the risk assessment model exceeds the preset warning threshold, the warning mechanism is immediately triggered, and warning information is sent to relevant managers via instant messaging tools, email or SMS. The warning information includes detailed information on abnormal risk indicators, identification of affected group insurance policies, and possible risk evolution trends, so that managers can take timely countermeasures. Analysis report generation module: After the warning is triggered, the SQL query and summary functions are automatically invoked to generate a detailed analysis report that includes the current status of group policy claims risk, preliminary analysis of risk causes, assessment of the scope of risk impact, and comparison with historical data. The report is presented in a way that combines intuitive charts and clear text descriptions, providing managers with comprehensive and accurate decision support information for formulating risk response strategies, helping them to effectively reduce group policy claims risk and ensure the sound operation of the insurance business.

[0006] Preferably, the data acquisition and preprocessing module employs distributed database connection technology, establishing stable connections with multiple heterogeneous databases simultaneously. By writing efficient SQL query statements, it achieves parallel acquisition of massive group policy claims data. The acquisition frequency can be flexibly configured according to business needs, adapting to both real-time acquisition and periodic batch acquisition. In the data preprocessing stage, SQL's data type conversion, missing value filling, and duplicate value removal functions are used to ensure the integrity and accuracy of the data, enabling the data to meet the quality standards required for subsequent analysis.

[0007] Preferably, in the time-series feature extraction module, SQL window functions are used to flexibly arrange and analyze group policy claims data in a time-series manner, calculating key time-series indicators such as the rate of change in claim frequency, the year-on-year growth rate of claim amount, and the continuous claim cycle for each group in different time periods. At the same time, combined with the age, occupation, health status, and other attribute information of group members, a multi-dimensional combination of time-series features is constructed to deeply explore the potential patterns and related factors of group policy claims risks, providing rich feature evidence for accurate risk assessment.

[0008] Preferably, in the risk assessment model construction module, a machine learning algorithm based on time-series data is selected to train and optimize the extracted time-series features. SQL statements are used to efficiently manage and preprocess the training data. Through iterative training of the model, it can accurately capture the time-series change patterns and nonlinear relationships of group policy claims risks, thereby improving the accuracy and foresight of risk assessment. The model's assessment results are displayed intuitively in the form of risk heat maps, risk trend curves, etc., which makes it convenient for managers to quickly understand the spatiotemporal distribution of group policy claims risks.

[0009] Preferably, the warning threshold setting and triggering module introduces an adaptive adjustment mechanism. Based on the actual changes in the group policy claim risk and the dynamic factors of the business environment, the warning threshold is automatically corrected and optimized periodically or in real time using SQL statements. This ensures that the warning threshold remains reasonable and effective, which can not only capture potential high-risk claim situations in a timely manner, but also avoid frequent false alarms or missed alarms caused by improper threshold settings, thereby improving the reliability and practicality of the warning system.

[0010] Preferably, the analysis report generation module integrates a visualization chart generation tool, which seamlessly connects SQL query results with chart drawing functions to transform complex group policy claim risk data into a variety of intuitive and easy-to-understand visual charts such as bar charts, line charts, and pie charts.

[0011] Preferably, it also includes a feedback and optimization module, which regularly collects feedback from management personnel on the early warning and analysis results, and uses an SQL database to associate, store and analyze the feedback information with the original data, model parameters and other data, thereby continuously optimizing and improving the entire early warning and analysis method.

[0012] Preferably, the method fully considers data security and confidentiality during execution. All data related to group policy claims are protected by encryption technology during collection, transmission, storage, and processing. Sensitive data such as group members' personal information and claim amounts are encrypted during storage and transmission through SQL Server's built-in encryption function or third-party encryption plugins. At the same time, strict database access permissions and operation logs are set to ensure that only authorized personnel can access and operate the relevant data, effectively preventing the risk of data leakage and illegal tampering, and ensuring the security and integrity of group policy claim data.

[0013] Preferably, the method can also be integrated with other business systems of the insurance company, such as policy sales systems, customer service systems, reinsurance systems, etc. By establishing data sharing interfaces and business process linkage mechanisms, the risk warning and analysis results of group policy claims can be fed back to relevant business links in real time, thereby realizing risk collaborative management of the entire insurance business process.

[0014] This invention provides a SQL-based method for early warning and analysis of high-risk time-series claims in group insurance policies. It offers the following advantages: 1. This invention constructs a SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims, achieving real-time and accurate monitoring and analysis of group insurance policy claims risks. Its data acquisition and preprocessing module efficiently integrates claims-related data from multiple business databases while ensuring data quality; the time-series feature extraction module deeply mines key time-series features in claims data, reflecting changes in claims risk from multiple dimensions; the risk assessment model combines statistical and machine learning algorithms with time-series data to accurately quantify risk levels; the early warning threshold is dynamically adjustable to ensure timely and accurate warnings; and the analysis report generation module provides intuitive and detailed analysis results, offering strong support for managers to formulate risk response strategies, thereby effectively reducing group insurance policy claims risks, ensuring the sound operation of insurance business, and enhancing the competitiveness of insurance companies in risk management.

[0015] 2. This invention fully leverages the powerful data processing and query capabilities of the SQL language, demonstrating significant efficiency advantages in group policy claims risk management and analysis. In the data collection and preprocessing stages, it can rapidly collect massive amounts of data in parallel and complete cleaning and transformation, saving data preparation time. Time-series feature extraction utilizes efficient processing tools such as SQL window functions to quickly calculate various key time-series indicators and uncover risk patterns. The risk assessment model utilizes SQL to manage and preprocess training data, improving model training and optimization efficiency, thereby accelerating risk assessment. Early warning information is triggered and sent quickly and promptly, enabling managers to receive risk alerts immediately. The analysis report generation process integrates SQL queries and visualization tools to quickly generate intuitive and easy-to-understand reports, significantly shortening the cycle from data analysis to decision support. This effectively improves the response speed and decision-making efficiency of insurance companies in group policy claims risk prevention and control, helping them maintain their advantage in the complex and ever-changing insurance market. Attached Figure Description

[0016] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see the appendix Figure 1 This invention provides a SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims, including the following steps: Data Acquisition and Preprocessing Module: Regularly collects basic data related to group policy claims from multiple business databases, including but not limited to policy information, claim details, and group member information. The collected data is cleaned, transformed, and standardized to ensure data quality and consistency, providing a reliable data foundation for subsequent analysis. Specifically, basic data related to group policy claims, such as policy information, claim details, and group member information, is collected periodically from multiple business databases to provide data support for the entire risk warning and analysis process. The collected data often contains incomplete, inconsistent, or non-standardized data, thus requiring cleaning, transformation, and standardization. Data cleaning removes duplicate or erroneous data items and corrects obvious errors and outliers; transformation unifies the format and coding rules of data from different sources and in various formats to ensure compatibility; standardization further standardizes the data, unifying its dimensions, units, and representation. Through this series of processes, the quality and consistency of the data are improved, providing an accurate and reliable data foundation for subsequent time-series feature extraction, risk assessment, and analysis, ensuring the effective operation of the entire warning and analysis system.

[0019] In the data acquisition and preprocessing module, distributed database connection technology is adopted, and stable connections are established with multiple heterogeneous databases simultaneously. By writing efficient SQL query statements, parallel acquisition of massive group policy claims data is achieved. The acquisition frequency can be flexibly configured according to business needs, adapting to both real-time acquisition and periodic batch acquisition. In the data preprocessing stage, SQL's data type conversion, missing value filling, and duplicate value removal functions are used to ensure the integrity and accuracy of the data, so that the data meets the quality standards required for subsequent analysis.

[0020] Specifically, during the data acquisition phase, leveraging distributed database connectivity technology, this module can simultaneously establish stable connections with multiple heterogeneous databases, ensuring the breadth and diversity of data sources. By writing efficient SQL queries, it achieves parallel acquisition of massive amounts of group insurance policy claims data. This means that when faced with huge datasets, data acquisition tasks can be completed efficiently, and the acquisition frequency can be flexibly configured according to business needs, easily adapting to both real-time and periodic batch acquisition, thus ensuring the timeliness and relevance of the data. Secondly, in the data preprocessing stage, various SQL functions, such as data type conversion, missing value imputation, and duplicate value removal, are used to deeply clean and process the acquired data. This step ensures the integrity and accuracy of the data, effectively avoiding the impact of data quality issues on subsequent analysis. After processing by this module, the data quality is significantly improved, meeting the stringent standards required for subsequent analysis, providing a solid and reliable data foundation for the entire group insurance policy claims high-risk time-series early warning and analysis method.

[0021] The time-series feature extraction module uses SQL query statements to extract key time-series features related to group policy claims risk based on preprocessed data. These features cover multiple dimensions such as claim frequency, claim amount change trend over time, claim periodicity of different groups or group members, and claim case processing time. Through in-depth mining of these features, a comprehensive and detailed time-series feature set is constructed for risk assessment. In the time-series feature extraction module, SQL window functions are used to flexibly arrange and analyze group policy claims data over time. Key time-series indicators such as the rate of change in claim frequency, the year-on-year growth rate of claim amount, and the continuous claim cycle for each group are calculated in different time periods. At the same time, combined with the age, occupation, health status and other attribute information of group members, multi-dimensional time-series feature combinations are constructed to deeply explore the potential patterns and correlation factors of group policy claims risks, providing rich feature evidence for accurate risk assessment.

[0022] Specifically, the module employs SQL queries to deeply mine preprocessed group policy claims data, extracting key time-series features related to claims risk. First, it uses SQL window functions for time-series arrangement and analysis, calculating key indicators such as the rate of change in claims frequency and the year-on-year growth rate of claims amount. These indicators reflect the dynamic changes in claims behavior over time. Simultaneously, by combining attributes such as age, occupation, and health status of group members, a multi-dimensional combination of time-series features is constructed to further uncover potential patterns and correlations in claims risk. This multi-dimensional feature extraction method not only comprehensively reflects the changing trends of group policy claims risk but also provides rich feature data for subsequent risk assessment models, thereby achieving accurate assessment of claims risk.

[0023] Risk assessment model construction module: Constructs a time-series risk assessment model based on SQL calculation. This model takes the extracted time-series features as input parameters and combines them with preset risk assessment algorithms, such as statistical methods or machine learning algorithms based on time-series data, such as moving average, exponential smoothing, etc., to quantitatively assess the real-time risk level of group policy claims and generate assessment results containing key information such as risk score and risk level, so as to reflect the dynamic changes in the risk of group policy claims in a timely manner. In the risk assessment model construction module, a machine learning algorithm based on time series data is selected to train and optimize the extracted time series features. SQL statements are used to efficiently manage and preprocess the training data. Through iterative training of the model, it can accurately capture the time series change patterns and nonlinear relationships of group policy claims risks, thereby improving the accuracy and foresight of risk assessment. The model's assessment results are displayed intuitively in the form of risk heat maps, risk trend curves, etc., which makes it convenient for managers to quickly understand the spatiotemporal distribution of group policy claims risks.

[0024] Specifically, a time-series risk assessment model based on SQL computation is constructed. This model uses extracted time-series features as input parameters and combines them with pre-defined risk assessment algorithms, such as moving averages, exponential smoothing, and other statistical methods or machine learning algorithms, to quantitatively assess the immediate risk level of group policy claims. The module employs machine learning algorithms based on time-series data and utilizes SQL statements for efficient management and preprocessing of training data. Through iterative training, the model can accurately capture the time-series change patterns and non-linear relationships of group policy claim risks. This training method not only improves the accuracy of risk assessment but also enhances the model's foresight regarding risk changes. Finally, the model generates assessment results containing key information such as risk scores and risk levels, which are visually displayed in the form of risk heatmaps and risk trend curves. This allows managers to quickly understand the spatiotemporal distribution of group policy claim risks, thus providing a reliable basis for formulating effective risk response strategies.

[0025] Warning threshold setting and triggering module: Based on historical claims data of group insurance policies, industry experience and risk preferences, reasonable risk warning thresholds are set using SQL statements. These thresholds are dynamically adjustable. When the output of the risk assessment model exceeds the preset warning threshold, the warning mechanism is immediately triggered, and warning information is sent to relevant managers via instant messaging tools, email or SMS. The warning information includes detailed information on abnormal risk indicators, identification of affected group insurance policies, and possible risk evolution trends, so that managers can take timely countermeasures. In the warning threshold setting and triggering module, an adaptive adjustment mechanism is introduced. Based on the actual changes in the group policy claim risk and the dynamic factors of the business environment, the warning threshold is automatically corrected and optimized periodically or in real time using SQL statements. This ensures that the warning threshold remains reasonable and effective at all times, which can not only capture potential high-risk claim situations in a timely manner, but also avoid frequent false alarms or missed alarms caused by improper threshold settings, thereby improving the reliability and practicality of the warning system.

[0026] Specifically, initial risk warning thresholds are set via SQL statements. These thresholds are not fixed but dynamically adjustable. When the output of the risk assessment model exceeds the preset warning threshold, the warning mechanism is immediately activated. This module uses instant messaging tools, email, or SMS to promptly send warning information to relevant management personnel. The warning information is detailed, covering the specific circumstances of the abnormal risk indicators, the identification of group policies affected by the risk, and the possible future evolution trend of the risk, thus providing management personnel with sufficient information to take rapid and effective countermeasures. Furthermore, the module introduces an adaptive adjustment mechanism, periodically or in real-time using SQL statements to automatically correct and optimize the warning thresholds based on the actual dynamic changes in group policy claim risks and various dynamic factors in the business environment. This design ensures the rationality and effectiveness of the warning thresholds, both keenly capturing potential high-risk claim situations and effectively avoiding frequent false alarms or missed alarms caused by unreasonable threshold settings, thereby significantly improving the reliability and practicality of the warning system.

[0027] Analysis report generation module: After the warning is triggered, the SQL query and summary functions are automatically invoked to generate a detailed analysis report that includes the current status of group policy claims risk, preliminary analysis of risk causes, assessment of the scope of risk impact, and comparison with historical data. The report is presented in a way that combines intuitive charts and clear text descriptions, providing managers with comprehensive and accurate decision support information for formulating risk response strategies, helping them to effectively reduce group policy claims risk and ensure the sound operation of the insurance business.

[0028] The analysis report generation module integrates a visualization chart generation tool. Through seamless integration of SQL query results and chart drawing functions, it transforms complex group policy claims risk data into a variety of intuitive and easy-to-understand visual charts, such as bar charts, line charts, and pie charts.

[0029] Specifically, upon triggering an alert, the system automatically invokes SQL query and aggregation functions to generate a detailed analysis report. The report is comprehensive, including not only the current status of group policy claims risks but also a preliminary analysis of the risk's causes, an assessment of its impact, and comparisons with historical data from the same period. This comprehensive analysis provides managers with a holistic perspective on risk, offering accurate decision support for developing risk response strategies. Furthermore, the module integrates a visualization chart generation tool. Through seamless integration with SQL query results, it transforms complex claims risk data into intuitive and easy-to-understand charts, such as bar charts, line charts, and pie charts. This visualization significantly improves managers' understanding and analysis efficiency, enabling them to quickly grasp key risk points and trends, allowing for timely and effective measures to mitigate risks and ensure the stable operation of the insurance business.

[0030] It also includes a feedback and optimization module, which regularly collects feedback from managers on the early warning and analysis results. The feedback information is linked with the original data, model parameters, etc., through an SQL database for storage and analysis, thereby continuously optimizing and improving the entire early warning and analysis method.

[0031] Specifically, by regularly collecting feedback from management on the early warning and analysis results, this feedback information is linked, stored, and analyzed with raw data and model parameters using an SQL database. This process not only identifies shortcomings in the model's practical application but also uncovers new risk characteristics and patterns. Through this continuous feedback mechanism, the module can specifically optimize and improve the entire early warning and analysis method. This optimization may include adjusting early warning thresholds, improving the extraction method of time-series features, and updating the parameters of the risk assessment model, thereby improving the model's accuracy and adaptability. Furthermore, the feedback and optimization module forms a closed-loop continuous improvement process, ensuring that the system can continuously evolve according to actual business changes and management needs, providing more accurate risk early warning and analysis results, and enhancing the effectiveness and competitiveness of insurance companies in group policy claims risk management.

[0032] During implementation, the method fully considers data security and confidentiality. All data related to group policy claims is protected by encryption technology during collection, transmission, storage, and processing. Sensitive data such as group member personal information and claim amounts are encrypted during storage and transmission through SQL Server's built-in encryption function or third-party encryption plugins. At the same time, strict database access permissions and operation logs are set to ensure that only authorized personnel can access and operate the relevant data, effectively preventing the risk of data leakage and illegal tampering, and ensuring the security and integrity of group policy claim data.

[0033] The method can also be integrated with other business systems of insurance companies, such as policy sales systems, customer service systems, and reinsurance systems. By establishing data sharing interfaces and business process linkage mechanisms, the risk warning and analysis results of group policy claims can be fed back to relevant business links in real time, thereby achieving risk collaborative management throughout the entire insurance business process.

[0034] Specifically, by regularly collecting feedback from management on the early warning and analysis results, this feedback information is linked, stored, and analyzed with raw data and model parameters using an SQL database. This process not only identifies shortcomings in the model's practical application but also uncovers new risk characteristics and patterns. Through this continuous feedback mechanism, the module can optimize and improve the entire early warning and analysis method in a targeted manner, such as adjusting early warning thresholds, improving the extraction method of time-series features, and updating the parameters of the risk assessment model. This optimization and improvement enhances the model's accuracy and adaptability, enabling it to better cope with changes in group policy claims risks. Furthermore, the feedback and optimization module forms a closed-loop continuous improvement process, ensuring that the system can continuously evolve according to actual business changes and management needs, providing more accurate risk early warning and analysis results. This continuous optimization enhances the effectiveness and competitiveness of insurance companies in managing group policy claims risks, enabling them to maintain an advantage in a complex and ever-changing market environment.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A SQL-based method for early warning and analysis of high-risk time-series group insurance policy claims, characterized in that... Includes the following steps: Data Acquisition and Preprocessing Module: Regularly collects basic data related to group policy claims from multiple business databases, including but not limited to policy information, claim details, and group member information. The collected data is cleaned, transformed, and standardized to ensure data quality and consistency, providing a reliable data foundation for subsequent analysis. The time-series feature extraction module uses SQL query statements to extract key time-series features related to group policy claims risk based on preprocessed data. These features cover multiple dimensions such as claim frequency, claim amount change trend over time, claim periodicity of different groups or group members, and claim case processing time. Through in-depth mining of these features, a comprehensive and detailed time-series feature set is constructed for risk assessment. Risk assessment model construction module: Constructs a time-series risk assessment model based on SQL calculation. This model takes the extracted time-series features as input parameters and combines them with preset risk assessment algorithms, such as statistical methods or machine learning algorithms based on time-series data, such as moving average, exponential smoothing, etc., to quantitatively assess the real-time risk level of group policy claims and generate assessment results containing key information such as risk score and risk level, so as to reflect the dynamic changes in the risk of group policy claims in a timely manner. Warning threshold setting and triggering module: Based on historical claims data of group insurance policies, industry experience and risk preferences, reasonable risk warning thresholds are set using SQL statements. These thresholds are dynamically adjustable. When the output of the risk assessment model exceeds the preset warning threshold, the warning mechanism is immediately triggered, and warning information is sent to relevant managers via instant messaging tools, email or SMS. The warning information includes detailed information on abnormal risk indicators, identification of affected group insurance policies, and possible risk evolution trends, so that managers can take timely countermeasures. Analysis report generation module: After the warning is triggered, the SQL query and summary functions are automatically invoked to generate a detailed analysis report that includes the current status of group policy claims risk, preliminary analysis of risk causes, assessment of the scope of risk impact, and comparison with historical data. The report is presented in a way that combines intuitive charts and clear text descriptions, providing managers with comprehensive and accurate decision support information for formulating risk response strategies, helping them to effectively reduce group policy claims risk and ensure the sound operation of the insurance business.

2. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, The data acquisition and preprocessing module employs distributed database connection technology, establishing stable connections with multiple heterogeneous databases simultaneously. By writing efficient SQL query statements, it achieves parallel acquisition of massive group policy claims data. The acquisition frequency can be flexibly configured according to business needs, adapting to both real-time acquisition and periodic batch acquisition. In the data preprocessing stage, SQL's data type conversion, missing value filling, and duplicate value removal functions are used to ensure the integrity and accuracy of the data, enabling it to meet the quality standards required for subsequent analysis.

3. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, The time-series feature extraction module utilizes SQL window functions to flexibly arrange and analyze group policy claims data over time. It calculates key time-series indicators for each group, such as the rate of change in claims frequency, the year-on-year growth rate of claims amount, and the continuous claims cycle. Simultaneously, by combining the age, occupation, and health status of group members, it constructs multi-dimensional time-series feature combinations to deeply explore the potential patterns and correlation factors of group policy claims risks, providing rich feature evidence for accurate risk assessment.

4. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, In the risk assessment model construction module, a machine learning algorithm based on time-series data is selected to train and optimize the extracted time-series features. SQL statements are used to efficiently manage and preprocess the training data. Through iterative training of the model, it can accurately capture the time-series change patterns and nonlinear relationships of group policy claims risks, thereby improving the accuracy and foresight of risk assessment. The model's assessment results are displayed intuitively in the form of risk heat maps, risk trend curves, etc., which makes it convenient for managers to quickly understand the spatiotemporal distribution of group policy claims risks.

5. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, The warning threshold setting and triggering module introduces an adaptive adjustment mechanism. Based on the actual changes in group policy claim risks and dynamic factors in the business environment, the warning threshold is automatically corrected and optimized periodically or in real time using SQL statements. This ensures that the warning threshold remains reasonable and effective, enabling timely detection of potential high-risk claim situations while avoiding frequent false alarms or missed alarms due to improper threshold settings, thus improving the reliability and practicality of the warning system.

6. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, The analysis report generation module integrates a visualization chart generation tool. Through seamless integration of SQL query results and chart drawing functions, it transforms complex group policy claims risk data into a variety of intuitive and easy-to-understand visual charts, such as bar charts, line charts, and pie charts.

7. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, It also includes a feedback and optimization module, which regularly collects feedback from managers on the early warning and analysis results. The feedback information is linked with the original data, model parameters, etc., through an SQL database for storage and analysis, thereby continuously optimizing and improving the entire early warning and analysis method.

8. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, During the implementation of the method, data security and confidentiality are fully considered. All data related to group policy claims are protected by encryption technology in the collection, transmission, storage and processing stages. Sensitive data such as group member personal information and claim amounts are encrypted and stored and transmitted through SQL Server's built-in encryption function or third-party encryption plugins. At the same time, strict database access permissions and operation logs are set to ensure that only authorized personnel can access and operate the relevant data, effectively preventing the risk of data leakage and illegal tampering, and ensuring the security and integrity of group policy claim data.

9. The SQL-based method for high-risk time-series early warning and analysis of group insurance policy claims as described in claim 1, characterized in that, The method can also be integrated with other business systems of insurance companies, such as policy sales systems, customer service systems, and reinsurance systems. By establishing data sharing interfaces and business process linkage mechanisms, the risk warning and analysis results of group policy claims can be fed back to relevant business links in real time, thereby realizing risk collaborative management throughout the entire insurance business process.

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