A management method for data interaction and sharing of security insurance based on regulatory cooperation

Abstract

The present application relates to the technical field of data interaction sharing management of liability insurance.The present application relates to a kind of data interaction sharing management method of liability insurance based on supervision cooperation.It includes the following steps: S1, establishing data sharing platform, and collecting safety production data to enterprise, collecting liability insurance policy data to insurance company, collecting public inspection data to supervisory agency through data sharing platform;S2, obtaining enterprise basic information, performing safety data type complete analysis according to enterprise basic information, and simultaneously performing data complete scoring by combining safety data type with safety production data;In the data collection stage, the present application differentiates setting collection frequency according to enterprise industry risk level, ensures data timeliness and pertinence, in the risk assessment stage, combines data complete scoring, enterprise credit score and reference time inspection result, calculates update score, and dynamically assesses policy risk.

Description

Technical Field

[0001] This invention relates to the field of data interaction and sharing management technology for safety liability insurance, and more specifically, to a data interaction and sharing management method for safety liability insurance based on regulatory collaboration. Background Technology

[0002] Work safety liability insurance (hereinafter referred to as work safety liability insurance) refers to commercial insurance that provides compensation from insurance institutions for personal injury and related economic losses caused by work safety accidents that occur in insured production and operation units, and provides accident prevention services to insured production and operation units;

[0003] Existing technologies provide basic information support for the underwriting, pricing, claims review, and safety supervision of work-related liability insurance through methods such as enterprise self-reported safety data, insurance company internal policy management systems, and independent enforcement records of regulatory agencies. However, enterprises mainly record safety production data through internal systems and only provide limited information to insurance companies when applying for insurance or when an accident occurs. Insurance companies rely on enterprise self-reported data and their own business systems for underwriting and claims processing, making it difficult to fully verify the authenticity of the data, resulting in data asymmetry. Because insurance companies cannot obtain authoritative inspection data from regulatory agencies, they are prone to misjudging underwriting risks due to enterprises concealing risk information, leading to unreasonable premium pricing or claims disputes. Furthermore, the data quality is low, and the lack of a constraint mechanism for enterprise self-reported data results in omissions and false reporting, affecting the accuracy of risk assessment. Therefore, this paper proposes a data interaction and sharing management method for work-related liability insurance based on regulatory collaboration. Summary of the Invention

[0004] The purpose of this invention is to provide a data interaction and sharing management method for safety liability insurance based on regulatory collaboration, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, a data exchange and sharing management method for safety liability insurance based on regulatory collaboration is provided, including the following steps:

[0006] S1. Establish a data sharing platform to collect safety production data from enterprises, safety liability insurance policy data from insurance companies, and public inspection data from regulatory agencies.

[0007] S2. Obtain basic enterprise information, conduct a complete analysis of safety data types based on the basic enterprise information, and combine the safety data types with safety production data to score data integrity.

[0008] S3. Extract the inspection results and recording time from the public inspection data. At the same time, bind the safety production data with the inspection results according to the recording time. Then, accurately analyze the safety production data with two adjacent inspection results and score the enterprise credit based on the accurate analysis results.

[0009] S4. Extract the inaccurate safety production data from the S3 analysis results. At the same time, set the deviation range based on the inspection results corresponding to the extracted data. Then, match the inaccurate safety production data with similar historical safety production data based on the deviation range.

[0010] S5. Based on the safety liability insurance policy data and inspection results, conduct a reference time analysis, and set an initial score using the safety production data and inspection results within the reference time. Then, combine the initial score with the data integrity score and the enterprise credit score to conduct risk management analysis on the safety liability insurance policy data.

[0011] As a further improvement to this technical solution, S1 constructs a data sharing platform with regulatory collaboration as its core, and establishes a three-way data channel between the data sharing platform and insurance companies, enterprises, and regulatory agencies, thereby collecting data from insurance companies, enterprises, and regulatory agencies.

[0012] As a further improvement to this technical solution, step S1 is as follows:

[0013] S1.1. Accident records and safety inspection records are collected from enterprises on a regular basis and stored as safety production data on the data sharing platform;

[0014] S1.2. Regularly collect policy data, claims history, and underwritten products from insurance companies and store them as safety liability insurance policy data on the data sharing platform;

[0015] S1.3. Collect public safety inspections, legal inspections, and administrative inspections from regulatory agencies on a regular basis, and combine them into inspection reports, which are then stored as public inspection data on a data sharing platform.

[0016] As a further improvement to this technical solution, step S2 is as follows:

[0017] S2.1 Obtain basic enterprise information, perform a complete analysis of safety data types based on the basic enterprise information, and obtain the safety data types that the enterprise needs to apply when conducting safety liability insurance data analysis;

[0018] S2.2. Based on the safety data type obtained in S2.1, extract and analyze the same type of safety production data in the data sharing platform to obtain safety production data with the same safety data type in the data sharing platform;

[0019] S2.3. Combine the safety production data obtained in S2.2 with the safety data types to perform missing data analysis, obtain the safety data types that the enterprise has not uploaded, and then score the enterprise's data completeness based on the missing safety data types.

[0020] As a further improvement to this technical solution, step S2.3 is as follows:

[0021] S2.3.1. Assign a corresponding weight to each type of security data, and the total weight of all security data types is 100.

[0022] S2.3.2 Calculate the data integrity score by combining the missing security data types with their corresponding weights, obtain the enterprise's data integrity score based on the calculation results, and send the missing security data types to the regulatory agency.

[0023] As a further improvement to this technical solution, step S3 is as follows:

[0024] S3.1 Extract the inspection results and recording time from the public inspection data, divide the safety production data according to the recording time, and take the recording time of each inspection result as a node to divide the safety production data into multiple segments. Then, select the inspection results corresponding to the end time of each segment of safety production data and bind them so that each segment of safety production data has an inspection result.

[0025] S3.2 Extract the two inspection results corresponding to the start and end times of the safety production data, and then combine the two inspection results with the safety production data for accurate analysis;

[0026] If the safety production data does not match the results of the two inspections, the safety production data is deemed inaccurate.

[0027] If the safety production data meets the results of both inspections, the safety production data is deemed accurate.

[0028] S3.3. Combine the total number of safety production data segments with inaccurate safety production data to conduct enterprise credit scoring, thereby obtaining the enterprise's credit score;

[0029] The more inaccurate safety production data there are in the total number of segments, the lower the reputation score will be.

[0030] As a further improvement to this technical solution, step S4 is as follows:

[0031] S4.1 Extract the inaccurate safety production data from the analysis results of S3.2, and set the deviation range based on the inspection results linked to this safety production data;

[0032] The more non-compliant items there are in the inspection results, the larger the range of deviation.

[0033] S4.2. Combine inaccurate safety production data with deviation ranges to match similar historical safety production data and obtain the most similar historical safety production data.

[0034] As a further improvement to this technical solution, in step S4.2, when matching similar historical safety production data, the historical safety production data is the safety production data that is determined to be accurate.

[0035] The deviation range is downward, which means that historical safety production data with lower scores will be obtained. Similarity matching will not obtain data that performs better than inaccurate safety production data.

[0036] If historical safety production data that meets the requirements cannot be obtained, only the inspection results are retained in S5.

[0037] As a further improvement to this technical solution, step S5 is as follows:

[0038] S5.1. Analyze the reference time based on the safety liability insurance policy data and the inspection results, and set the reference time according to the analysis results;

[0039] The more claims a liability insurance policy has, the longer the reference period should be.

[0040] The more unqualified results there are in the inspection, the longer the reference period should be.

[0041] S5.2. Set an initial score based on safety production data and inspection results extracted from the reference time. Then, combine the initial score with the data integrity score and the enterprise credit score to update the score analysis. At the same time, conduct risk management analysis on the safety liability insurance policy data based on the updated score.

[0042] The more unqualified results there are in the inspection, the lower the initial score will be;

[0043] The worse the safety production data performance, the lower the initial score;

[0044] The lower the update score, the higher the risk of the current work-related liability insurance policy data, and the more necessary it is to adjust the pricing and underwriting conditions of the insurance products.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This data exchange and sharing management method for work safety liability insurance based on regulatory collaboration ensures data timeliness and relevance by setting different collection frequencies according to the risk levels of enterprises in different industries during the data collection phase. During the risk assessment phase, the method calculates and updates scores by combining data integrity scores, enterprise credit scores, and inspection results within a reference period to dynamically assess policy risks. In terms of historical data application, the method avoids misjudgments of risks due to data distortion by matching similar historical data. This enables work safety liability insurance to shift from passive underwriting to proactive risk management based on data, improving the efficiency and accuracy of the entire process of insurance pricing, underwriting, and claims settlement. At the same time, it provides regulatory agencies with scientific enforcement basis and promotes the overall improvement of risk management level in the industry.

[0047] 2. This data sharing and management method for safety liability insurance based on regulatory collaboration constructs a data sharing platform centered on regulatory collaboration, connecting dedicated data channels among enterprises, insurance companies, and regulatory agencies. This enables centralized collection and sharing of safety production data, safety liability insurance policy data, and public inspection data. For insurance companies, this provides access to authoritative inspection reports from regulatory agencies and accurate safety data from enterprises, avoiding misjudgments of underwriting risks caused by relying solely on self-reported data from enterprises, and allowing for precise formulation of premiums, coverage amounts, and underwriting conditions. For regulatory agencies, it integrates enterprise safety data with insurance company claims data, enabling dynamic monitoring of enterprise safety risks and insurance company underwriting compliance. For enterprises, the platform allows them to understand regulatory requirements and industry safety benchmarks, clarifying their own data gaps and deficiencies, thereby breaking down data silos among the three parties and eliminating the inefficiency in risk management caused by information asymmetry.

[0048] 3. In this data interaction and sharing management method for safety liability insurance based on regulatory collaboration, the data integrity score quantifies data integrity by comparing the matching degree between the types of safety data that enterprises should submit and the actual data submitted, thereby forcing enterprises to improve the submission of safety data and avoiding the impact of data omissions on risk assessment. The enterprise reputation score judges the accuracy of the data by comparing the enterprise's safety production data with the inspection results of regulatory agencies, and quantifies the reputation level based on the proportion of inaccurate data, thereby prompting enterprises to truthfully report safety information and reducing data falsification or concealment. Attached Figure Description

[0049] Figure 1 This is an overall flowchart of the present invention;

[0050] Figure 2 This is a flowchart illustrating the process of the present invention of periodically collecting accident records and safety inspection records from enterprises;

[0051] Figure 3 This is a flowchart illustrating the process of obtaining basic enterprise information according to the present invention.

[0052] Figure 4 This is a flowchart illustrating the process of extracting inspection results and recording time from public inspection data according to the present invention.

[0053] Figure 5 The flowchart of the present invention sets the deviation range based on the inspection results bound to the safety production data;

[0054] Figure 6 This is a flowchart illustrating the process of risk management analysis of safety liability insurance policy data based on updated scores, as described in this invention. Detailed Implementation

[0055] The technical solutions of 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.

[0056] Please see Figure 1 - Figure 6 As shown, the purpose of this embodiment is to provide a data interaction and sharing management method for safety liability insurance based on regulatory collaboration, including the following steps:

[0057] S1. Establish a data sharing platform to collect safety production data from enterprises, safety liability insurance policy data from insurance companies, and public inspection data from regulatory agencies.

[0058] Establish infrastructure for data exchange, connect data channels between enterprises, insurance companies, and regulatory agencies, and achieve centralized collection and storage of multi-dimensional data to provide data sources for subsequent data analysis (integrity, reputation, risk).

[0059] S1 constructs a data sharing platform centered on regulatory collaboration (the platform's core functions are data collection, storage, sharing, and security management, while establishing rules and permission systems for tripartite data interaction to ensure that enterprises, insurance companies, and regulatory agencies can legally obtain the required data while protecting data privacy). At the same time, the data sharing platform establishes tripartite data channels with insurance companies, enterprises, and regulatory agencies to collect data from them.

[0060] The steps for S1 are as follows:

[0061] S1.1. Accident records and safety inspection records are collected from enterprises on a regular basis and stored as safety production data on the data sharing platform;

[0062] The data sharing platform connects with the enterprise's information system and opens a dedicated data channel. Through this channel, the enterprise's safety production data is collected regularly, including but not limited to accident records (accident type, time, and handling results) and internal safety inspection records (inspection frequency, identified hazards, and rectification status). The collection frequency can be set according to the enterprise's industry risk level (e.g., once a month for high-risk industries and once a quarter for low-risk industries).

[0063] S1.2. Regularly collect policy data, claims history, and underwritten products from insurance companies and store them as safety liability insurance policy data on the data sharing platform;

[0064] The platform establishes data connections with insurance companies' business systems, opens a dedicated data channel for work-related liability insurance, and regularly collects work-related liability insurance policy data (policyholder information, coverage, sum insured, premium), claims history data (claim events, amounts, reasons), and underwritten product information (product type, applicable industries) from insurance companies to ensure the timeliness and completeness of the data.

[0065] S1.3. Collect public safety inspections, legal inspections, and administrative inspections from regulatory agencies on a regular basis, and combine them into inspection reports, which are then stored as public inspection data on a data sharing platform.

[0066] The platform connects with the enforcement systems of regulatory agencies, opens a public inspection data channel, and collects the results of public safety inspections, legal compliance inspection records, and administrative regulatory measures (such as penalties and rectification notices) of enterprises by regulatory agencies. This data is then integrated into standardized inspection reports, which are used as public inspection data storage.

[0067] S2. Obtain basic enterprise information, conduct a complete analysis of safety data types based on the basic enterprise information, and combine the safety data types with safety production data to score data integrity.

[0068] By analyzing the matching degree between the security data that enterprises should submit and the data they actually submit, the integrity of the data is quantified, which forces enterprises to improve their data submission and ensures the comprehensiveness of the data, thus avoiding the distortion of subsequent analysis due to missing data.

[0069] Through regulatory feedback, a constraint mechanism for data submission is established;

[0070] The steps for S2 are as follows:

[0071] S2.1 Obtain basic enterprise information, perform a complete analysis of safety data types based on the basic enterprise information, and obtain the safety data types that the enterprise needs to apply when conducting safety liability insurance data analysis;

[0072] By acquiring basic information about enterprises through the data sharing platform, including but not limited to the industry to which the enterprise belongs (such as chemical, construction, manufacturing, etc.), production scale, main business, production process characteristics, historical safety accident records, number of employees, etc., this information will serve as the basis for analyzing safety data types.

[0073] Based on the company's basic information, combined with industry safety supervision standards, safety liability insurance risk assessment models, and relevant regulatory requirements, this paper analyzes the types of safety data that the company needs to cover in its safety liability insurance data analysis.

[0074] For example, chemical companies may need to include records of hazardous chemical storage, explosion-proof equipment inspections, and employee safety training; construction companies may need to include records of scaffolding erection inspections, high-altitude work protection, and construction machinery maintenance. This ultimately forms a unique list of safety data types that each company needs to collect.

[0075] S2.2. Based on the safety data type obtained in S2.1, extract and analyze the same type of safety production data in the data sharing platform to obtain safety production data with the same safety data type in the data sharing platform;

[0076] Based on the list of safety data types that need to be collected, retrieve and extract safety production data that matches the list types from the data sharing platform;

[0077] If the list includes monthly security inspection records, all records of this type will be filtered out from the enterprise data stored on the platform. Simultaneously, the extracted data will undergo preliminary verification to confirm whether the data source, time range, and format meet the analysis requirements, ensuring that the extracted data accurately matches the required security data type.

[0078] Then, the extracted safety production data is matched one by one with the list of safety data types to be collected, and the presence and coverage of each type of data are recorded to provide a basis for subsequent data integrity assessment.

[0079] S2.3. Combine the safety production data obtained in S2.2 with the safety data types to perform missing data analysis, obtain the safety data types that the enterprise has not uploaded, and then score the enterprise's data completeness based on the missing safety data types.

[0080] The steps in S2.3 are as follows:

[0081] S2.3.1. Assign a corresponding weight to each type of security data, and the total weight of all security data types is 100.

[0082] Based on the importance of the missing data type (such as whether it is directly related to major safety risks or whether it is a mandatory requirement of regulations), a corresponding weight is assigned to each missing type. The higher the importance, the greater the weight value (e.g., the weight of major accident hazard rectification records is higher than that of routine hygiene inspection records), and the total weight of all types is 100.

[0083] S2.3.2 Calculate the data integrity score by combining the missing security data types with their corresponding weights. Obtain the enterprise's data integrity score based on the calculation results, and send the missing security data types to the regulatory agency. The formula is as follows:

[0084] ;

[0085] Where S is the enterprise data integrity score, n is the total number of missing security data types of the enterprise, and W i The weight is the weight corresponding to the i-th missing security data type.

[0086] S3. Extract the inspection results and recording time from the public inspection data. At the same time, bind the safety production data with the inspection results according to the recording time. Then, accurately analyze the safety production data with two adjacent inspection results and score the enterprise credit based on the accurate analysis results.

[0087] By comparing the consistency between the results of regulatory inspections and the data reported by enterprises, the credibility of enterprise data is assessed and the level of credit is quantified.

[0088] Verifying the authenticity of enterprise data through external authoritative inspection results solves the problem of potentially distorted self-reported data by enterprises, and provides a reliable data foundation for subsequent risk analysis;

[0089] The steps for S3 are as follows:

[0090] S3.1 Extract the inspection results and recording time from the public inspection data, divide the safety production data according to the recording time, and take the recording time of each inspection result as a node to divide the safety production data into multiple segments. Then, select the inspection results corresponding to the end time of each segment of safety production data and bind them so that each segment of safety production data has an inspection result.

[0091] The extracted inspection records are sorted chronologically as dividing points. Using these points as boundaries, the company's safety production data (recorded in chronological order) is divided into multiple segments: the first segment is the safety production data before the earliest inspection time, the middle segments are the safety production data between two adjacent inspection times, and the last segment is the safety production data after the latest inspection time.

[0092] For each segment of safety production data, match the corresponding inspection results according to its end time:

[0093] If the end time of a certain data segment is the same as the time of a certain inspection record, then the inspection result corresponding to that time will be directly bound;

[0094] If the end time of a data segment falls between two inspection times, the result corresponding to the next inspection record time closest to its end time is bound to it, ensuring that each segment of safety production data has a unique corresponding inspection result.

[0095] S3.2 Extract the two inspection results corresponding to the start and end times of the safety production data, and then combine the two inspection results with the safety production data for accurate analysis;

[0096] If the safety production data does not match the results of the two inspections, the safety production data is deemed inaccurate.

[0097] If the data content contradicts any inspection result (e.g., the data record shows that rectification has been completed, but the final inspection result is that rectification has not been completed), then the data segment is deemed inaccurate.

[0098] If the safety production data meets the results of both inspections, the safety production data is deemed accurate.

[0099] If the information recorded in the data, such as the safety status and the rectification of hidden dangers, can be corroborated by the two inspection results (such as rectification required at the beginning and rectification completed at the end), then the data segment is considered accurate.

[0100] S3.3. Combine the total number of safety production data segments with inaccurate safety production data to conduct enterprise credit scoring, thereby obtaining the enterprise's credit score;

[0101] The number of inaccurate safety production data segments is counted, and their proportion to the total number of segments is calculated. A corporate reputation score is determined based on this proportion: a higher proportion indicates lower credibility of the reported data and a correspondingly lower reputation score; conversely, a lower proportion results in a higher score. This ultimately forms a quantitative corporate reputation score.

[0102] The more inaccurate safety production data there are in the total number of segments, the lower the reputation score will be, as shown in the formula below:

[0103] ;

[0104] Where R is the enterprise credit score, U is the number of safety production data segments judged to be inaccurate, and N is the total number of safety production data segments.

[0105] S4. Extract the inaccurate safety production data from the S3 analysis results. At the same time, set the deviation range based on the inspection results corresponding to the extracted data. Then, match the inaccurate safety production data with similar historical safety production data based on the deviation range.

[0106] For inaccurate data identified in S3, historical case matching is used to provide a reference for risk assessment;

[0107] Provide historical benchmarks for inaccurate data, avoid misjudgment of risks due to data distortion, and enhance the robustness of analysis;

[0108] The steps for S4 are as follows:

[0109] S4.1 Extract the inaccurate safety production data from the analysis results of S3.2, and set the deviation range based on the inspection results linked to this safety production data;

[0110] The more non-compliant items there are in the inspection results, the larger the range of deviation.

[0111] Analyze the inspection results linked to inaccurate data, and count the number and severity of nonconformities: the more nonconformities and the higher the severity, the larger the set deviation range (minor nonconformities correspond to ±5% deviation, multiple serious nonconformities correspond to ±20% deviation).

[0112] The deviation range is used to limit the fluctuation range for subsequent historical data matching;

[0113] S4.2. Combine inaccurate safety production data with the deviation range and match it with similar historical safety production data to obtain the most similar historical safety production data. The formula is as follows:

[0114] ;

[0115] Where Z is the similarity value, θ is the angle between the vectors of inaccurate data and historical data, and the smaller the angle, the higher the similarity. j For the inaccurate safety production data, y represents the value of the i-th indicator. j is the value of the i-th indicator in the historical safety production data, and m is the total number of indicators participating in the similarity calculation (such as the total number of key indicators such as the number of hidden dangers and the rectification rate).

[0116] S4.2 When matching similar historical safety production data, the historical safety production data shall be the accurate safety production data.

[0117] The deviation range is downward, which means that historical safety production data with lower scores will be obtained. Similarity matching will not obtain data that performs better than inaccurate safety production data.

[0118] From the historical database of the data sharing platform, safety production data that is deemed accurate is selected, and these data must be within the set deviation range (i.e., the data indicators are not better than inaccurate data, and are only allowed to be lower than or close to the data performance within the deviation range).

[0119] If historical safety production data that meets the requirements cannot be obtained, only the inspection results are retained in S5.

[0120] S5. Based on the safety liability insurance policy data and inspection results, conduct a reference time analysis, and set an initial score using the safety production data and inspection results within the reference time. Then, combine the initial score with the data integrity score and the enterprise credit score to conduct risk management analysis on the safety liability insurance policy data.

[0121] By combining data from various sources and scoring results, the risk level of safety liability insurance policies is assessed to guide insurance pricing and underwriting adjustments, thereby improving the accuracy of risk management.

[0122] The steps for S5 are as follows:

[0123] S5.1. Analyze the reference time based on the safety liability insurance policy data and the inspection results, and set the reference time according to the analysis results;

[0124] The more claims a liability insurance policy has, the longer the reference period should be.

[0125] The more unqualified results there are in the inspection, the longer the reference period should be.

[0126] Collect the number of accidents (such as the number of historical claims) from the safety liability insurance policy data and the number of non-compliance results (such as the total number of inspection conclusions that require rectification) from the public inspection data.

[0127] The reference time is set based on the values ​​of both: if there are many claims or many unsatisfactory inspection results, it indicates that the company's risk is highly volatile, and a longer reference time is needed for a comprehensive assessment; conversely, the reference time can be appropriately shortened. Finally, a time range for analysis is determined (such as 6 months, 1 year, etc.).

[0128] S5.2. Set the initial score based on safety production data and inspection results extracted from the reference time.

[0129] Extract safety production data (such as hazard records, rectification status, etc.) and inspection results within a reference time range, and conduct a comprehensive evaluation of the two to set an initial score.

[0130] The assessment rules are as follows: the more non-compliant items in the inspection results, the lower the initial score; the worse the safety status reflected in the safety production data (such as unrectified hidden dangers and frequent violations of regulations), the lower the initial score.

[0131] Then, the initial score is combined with the data integrity score and the enterprise credit score to update the score analysis, and at the same time, risk management analysis is carried out on the safety liability insurance policy data based on the updated score.

[0132] The more unqualified results there are in the inspection, the lower the initial score will be;

[0133] The worse the safety production data performance, the lower the initial score;

[0134] The lower the update score, the higher the risk of the current work-related injury insurance policy data, and the pricing and underwriting conditions of the insurance products need to be adjusted.

[0135] The lower the update score, the lower the company's current security management level and the worse the data credibility, and the higher the corresponding policy risk;

[0136] In response to high-risk situations, insurance product strategies need to be adjusted, such as increasing premiums, narrowing the scope of coverage, and increasing safety rectification requirements.

[0137] A higher score can maintain or optimize underwriting conditions and reduce risk exposure, as shown in the formula below:

[0138] ;

[0139] Where Q0 is the initial score, d is the influence coefficient of non-conforming results on the initial score, D is the number of non-conforming results in the inspection results within the reference time, f is the influence coefficient of safety production data performance on the initial score, and F is the negative evaluation of safety production data performance within the reference time.

[0140] ;

[0141] Where Q is the update score, and α1, α2, and α3 correspond to different weights.

[0142] This has created a complete chain of data-assessment-application. Through the intervention of authoritative data from regulatory agencies, the information asymmetry between enterprises and insurance companies has been resolved, enabling the upgrade of work liability insurance from passive underwriting to proactive risk management based on data.

[0143] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A supervision and coordination-based insurance data interaction sharing management method, characterized in that: Comprise the following steps: S1, establish a data sharing platform, collect safety production data through the data sharing platform, collect insurance company data, collect public inspection data from the regulatory agency; S2, obtain enterprise basic information, analyze the type of safety data according to the enterprise basic information, and score the data according to the type of safety data and safety production data; S3, the inspection result and record time of the public inspection data are extracted, the safety production data are combined with the inspection result according to the record time, then the safety production data are combined with the adjacent two inspection results for accurate analysis, and the enterprise credit score is calculated according to the accurate analysis result; The steps of S3 are as follows: S3.1, the inspection result and record time of the public inspection data are extracted, the safety production data are divided according to the record time, the record time of each inspection result is taken as a node, so that the safety production data are divided into several segments, then the inspection result corresponding to the end time of each segment of safety production data is bound, so that each segment of safety production data corresponds to an inspection result; S3.2, extract the two inspection results corresponding to the start time and end time of the safety production data, and then accurately analyze the two inspection results combined with the safety production data; When the safety production data do not conform to the two inspection results, it is determined that the safety production data are not accurate; When the safety production data conform to the two inspection results, it is determined that the safety production data are accurate; S3.3, the total number of segments of safety production data is combined with the inaccurate safety production data to score the enterprise credit, so as to obtain the credit score of the enterprise; The more the total number of segments of inaccurate safety production data, the lower the credit score; S4, extract the safety production data which is not accurate in S3 analysis result, and set the deviation range according to the corresponding inspection result of the extracted data, then match the inaccurate safety production data with similar historical safety production data according to the deviation range; The steps of S4 are as follows: S4.1, extract the safety production data which is not accurate in S3.2 analysis result, and set the deviation range according to the inspection result bound to the safety production data; The more unqualified places in the inspection result, the larger the deviation range; S4.2, match the inaccurate safety production data with similar historical safety production data according to the deviation range, and obtain the most similar historical safety production data; S5, analyze the reference time according to the insurance data combined with the inspection result, set the initial score according to the safety production data and inspection result in the reference time, then combine the initial score with the data integrity score and enterprise credit score to analyze the risk management of the insurance data.

2. The method of claim 1, wherein the method is characterized by: The S1 builds a data sharing platform with supervision and cooperation as the core, and establishes a three-way data channel between the data sharing platform, the insurance company, the enterprise and the regulatory agency, so as to collect data from the insurance company, the enterprise and the regulatory agency.

3. The method of claim 1, wherein the method is characterized by: The steps of S1 are as follows: S1.1, collect accident records and safety inspection records from enterprises regularly as safety production data and save them in the data sharing platform; S1.2, by regularly collecting insurance company policy data, claim history, underwriting products, as an insurance policy data saved in the data sharing platform; S1.3, by regularly collecting public safety inspection, legal inspection, administrative inspection from the regulatory agency, and merging into inspection report, as public inspection data saved in the data sharing platform.

4. The method of claim 1, wherein the method is characterized by: The steps of S2 are as follows: S2.1, obtaining enterprise basic information, and performing safety data type complete analysis according to the enterprise basic information, to obtain the safety data type required by the enterprise in the safety production data analysis; S2.2, according to the safety data type obtained in S2.1, the same type of safety production data is extracted and analyzed in the data sharing platform, so as to obtain the same safety production data in the data sharing platform; S2.3, combining the safety production data obtained in S2.2 with the safety data type to perform missing analysis, to obtain the safety data type lacking uploading of the enterprise, and then performing data complete score on the enterprise according to the safety data type lacking uploading.

5. The method of claim 4, wherein the method is characterized by: The steps of S2.3 are as follows: S2.3.1, setting a corresponding weight for each safety data type, and the total weight of all safety data types is 100; S2.3.2, combining the safety data type lacking uploading with the corresponding weight to calculate the data complete score, obtaining the data complete score of the enterprise according to the calculation result, and sending the missing safety data type to the regulatory agency.

6. The method of claim 1, wherein the method is characterized by: When performing similar historical safety production data matching, the historical safety production data is accurate safety production data; The deviation range is downward deviation, and the historical safety production data with lower score is obtained, and the similarity matching will not obtain data with better performance than the inaccurate safety production data; When there is no historical safety production data meeting the requirements, only the inspection result is retained in S5.

7. The method of claim 1, wherein the method is characterized by: The steps of S5 are as follows: S5.1, performing reference time analysis according to the insurance policy data combined with the inspection result, and setting the reference time according to the analysis result; The more the insurance policy data, the longer the reference time; The more the unqualified results in the inspection result, the longer the reference time; S5.2, according to the reference time, the safety production data and the inspection result are cut off to set the initial score, and then the initial score is combined with the data complete score and the enterprise credit score to update the score analysis, and the insurance policy data is analyzed according to the updated score; The more the unqualified results in the inspection result, the lower the initial score; The worse the safety production data, the lower the initial score; The lower the updated score, the higher the risk of the current insurance policy data, and the pricing and underwriting conditions of the insurance product need to be adjusted.

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