A method for verifying port safety production liability insurance information based on safety risks

Abstract

This invention discloses a method for verifying port safety production liability insurance information based on safety risks, and is situated within the field of data verification technology. The method includes the following steps: comprehensively evaluating the authenticity assessment coefficient and the accuracy assessment coefficient of the port safety production liability insurance information to obtain a verification assessment coefficient; and matching the difference between the authenticity assessment coefficient and the authenticity assessment threshold of the port safety production liability insurance information to obtain preliminary optimization measures for the authenticity of the information. This invention, by deeply optimizing the verification of policy information based on threshold comparison results, achieves the effect of improving the regulatory efficiency of the insurance market and solves the problem of insufficient regulatory efficiency in the insurance market in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of data verification technology, and in particular to a method for verifying port safety production liability insurance information based on safety risks. Background Technology

[0002] With the rapid advancement of information technology, various types of insurance have emerged. Due to the hazardous working environment of ports, insurance products have been established to address specific port risks. Existing port safety production liability insurance information verification methods rely on advanced technologies such as risk assessment models, data mining and analysis, identity authentication, electronic signatures, and cloud services. Through intelligent means, the accuracy and efficiency of insurance information verification are improved, ensuring the authenticity and validity of port safety production liability insurance data.

[0003] For example, the invention patent announcement CN118037461B, concerning a method, apparatus, equipment, and storage medium for verifying property insurance information, includes: acquiring an insurance application request uploaded by a service terminal and extracting insurance information from the application request; determining the target insurance risk model and target insurance risk association features based on the insured property type and insurance risk association information in the insurance information; calling the insurance claims database, extracting historical insurance risk association features from claims policies using the target insurance risk model, adjusting the risk redundancy of the target insurance risk model, and obtaining the final insurance risk model; inputting the target insurance risk association features into the final insurance risk model, generating verification results, and sending them to the service terminal.

[0004] For example, the invention patent with publication number CN119107188A discloses a method and system for verifying insurance industry regulatory reporting data. This includes: establishing a graph database of insurance industry regulatory reporting data by using records from a customer subject table, an underwriting subject table, and a payment collection subject table in a relational database as nodes; using the underwriting relationship between the customer subject table and the underwriting subject table, and the payment collection record relationship between the payment collection subject table and the underwriting subject table in the relational database as the first association relationship between nodes; and verifying the graph database composed of the insurance industry regulatory reporting data according to preset verification rules and the insurance company's core business database, marking risk nodes. If a node is marked as a risk node, the insurance industry regulatory reporting data involved in that node fails the verification.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0006] In the current technology, when making claims under port safety production liability insurance, the lack of an insurance information verification system makes it difficult to accurately verify the information of the insurance beneficiary, the insured, and the policy, which may lead to insufficient regulatory efficiency in the insurance market. Summary of the Invention

[0007] This application provides a method for verifying port safety production liability insurance information based on safety risks, which solves the problem of insufficient regulatory efficiency in the insurance market in the prior art and achieves the effect of improving the regulatory efficiency of the insurance market.

[0008] This application provides a method for verifying port safety production liability insurance information based on safety risks, including the following steps: collecting data related to the authenticity and accuracy of port safety production liability insurance information; preprocessing the data to obtain preprocessed data on the authenticity and accuracy of the port safety production liability insurance information; analyzing the preprocessed data on the authenticity of the port safety production liability insurance information to obtain an authenticity evaluation coefficient; and analyzing the preprocessed data on the accuracy of the port safety production liability insurance information to obtain an accuracy evaluation coefficient. The authenticity assessment coefficient of production liability insurance information and the accuracy assessment coefficient of port safety production liability insurance information are comprehensively evaluated to obtain the verification assessment coefficient of port safety production liability insurance information. The authenticity assessment threshold and verification assessment threshold of port safety production liability insurance information are obtained from the database. The difference between the authenticity assessment coefficient and the authenticity assessment threshold is calculated. Based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold, preliminary optimization measures for the authenticity of port safety production liability insurance information are obtained. The verification assessment coefficient and the verification assessment threshold of port safety production liability insurance information are compared, and the verification of policy information is further optimized based on the threshold comparison results.

[0009] Furthermore, data related to the authenticity and accuracy of port safety production liability insurance information include: data related to the authenticity of port safety production liability insurance information includes: the average data matching rate of multiple basic policy information, document consistency ratio, and historical claims frequency; data related to the accuracy of port safety production liability insurance information includes: data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects.

[0010] Furthermore, the specific preprocessing process for the data related to the authenticity and accuracy of port safety production liability insurance information is as follows: Preprocessing includes data cleaning, data transformation, and feature extraction; Data cleaning includes filling missing values, deleting outliers and duplicate values; Data transformation: Normalizing the data related to the authenticity and accuracy of port safety production liability insurance information; Feature extraction: Using principal component analysis to perform feature processing on the data related to the authenticity and accuracy of port safety production liability insurance information.

[0011] Furthermore, the specific analysis process for analyzing the authenticity-related data of the preprocessed port safety production liability insurance information is as follows: a comprehensive analysis is conducted on the average data matching rate, document consistency ratio, and historical claim frequency of multiple basic information items of the policy. The average data matching rate, document consistency ratio, and historical claim frequency of multiple basic information items of the port safety production liability insurance information are multiplied by their corresponding weights and processed to obtain the authenticity evaluation coefficient of the port safety production liability insurance information.

[0012] Furthermore, the specific analysis process for analyzing the accuracy-related data of the preprocessed port safety production liability insurance information is as follows: A comprehensive analysis is conducted on the data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of the insured object. The data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of the insured object of the port safety production liability insurance information are multiplied by their corresponding weights and processed to obtain the accuracy evaluation coefficient of the port safety production liability insurance information.

[0013] Furthermore, the specific process of subtracting the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is as follows: obtain the authenticity assessment threshold of port safety production liability insurance information from the database, subtract the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information to obtain the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information.

[0014] Furthermore, based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information, the specific matching process for preliminary optimization measures to improve the authenticity of port safety production liability insurance information is as follows: The difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is compared with a preset tolerance range. If the difference is within the tolerance range, the port safety production liability insurance information is marked as having passed the authenticity check. If the difference is outside the tolerance range, preliminary optimization measures are implemented for the port safety production liability insurance information. These preliminary optimization measures include data verification, risk assessment level correction, and policy information correction.

[0015] Furthermore, the specific comparison process for comparing the verification evaluation coefficient of port safety production liability insurance information with the verification evaluation threshold of port safety production liability insurance information is as follows: The verification evaluation threshold of port safety production liability insurance information is obtained from the database; the verification evaluation coefficient of port safety production liability insurance information is compared with the verification evaluation threshold; if the verification evaluation coefficient of port safety production liability insurance information is greater than or equal to the verification evaluation threshold, the port safety production liability insurance information is marked as having passed verification; if the verification evaluation coefficient of port safety production liability insurance information is less than the verification evaluation threshold, the port safety production liability insurance information undergoes in-depth optimization; in-depth optimization includes establishing a predictive model and predicting insurance fraud behavior.

[0016] Furthermore, the specific process of establishing the prediction model is as follows: obtain the recurrent neural network model from the database, determine the number of layers, the number of neurons in each layer, and the activation function of the recurrent neural network, divide the data related to the authenticity of the preprocessed port safety production liability insurance information and the data related to the accuracy of the preprocessed port safety production liability insurance information into training set and test set according to a certain ratio, input the training set into the recurrent neural network for training to obtain the preliminary prediction model, use the test set to evaluate the performance of the preliminary prediction model, and obtain the prediction model by adjusting the hyperparameters and model structure.

[0017] Furthermore, the specific method for obtaining the verification and evaluation coefficient of port safety production liability insurance information is as follows: In the formula, This represents the verification and assessment coefficient for port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This represents the weighting factor for the authenticity assessment coefficient of port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This represents the weighting factor for the accuracy assessment coefficient of port safety production liability insurance information.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0019] 1. By comparing the verification and evaluation coefficient of port safety production liability insurance information with the verification and evaluation threshold of port safety production liability insurance information, the verification of policy information is deeply optimized based on the threshold comparison results, thereby improving the regulatory efficiency of the insurance market and effectively solving the problem of insufficient regulatory efficiency in the insurance market in the existing technology.

[0020] 2. By comprehensively evaluating the authenticity assessment coefficient and the accuracy assessment coefficient of port safety production liability insurance information, a verification assessment coefficient for port safety production liability insurance information is obtained. This achieves the effect of optimizing the allocation of insurance resources in the insurance market and effectively solves the problem of insufficient allocation of insurance resources in the existing technology.

[0021] 3. By subtracting the authenticity assessment coefficient of port safety production liability insurance information from the authenticity assessment threshold of port safety production liability insurance information, preliminary optimization measures for the authenticity of port safety production liability insurance information are obtained based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information. This achieves the effect of quantifying risk assessment and effectively solves the problem that existing technologies cannot quantify risk assessment in the insurance market. Attached Figure Description

[0022] Figure 1 A flowchart of a port safety production liability insurance information verification method based on safety risks provided in this application embodiment;

[0023] Figure 2 The image shows the authenticity evaluation coefficient of port safety production liability insurance information in a port safety production liability insurance information verification method based on safety risk provided in this application embodiment. Detailed Implementation

[0024] This application provides a method for verifying port safety production liability insurance information based on safety risks, which solves the problem of insufficient regulatory efficiency in the insurance market in the prior art. By comparing the verification evaluation coefficient of port safety production liability insurance information with the verification evaluation threshold of port safety production liability insurance information, the verification of policy information is deeply optimized based on the threshold comparison results, thereby improving the regulatory efficiency of the insurance market.

[0025] The technical solution in this application aims to address the aforementioned problem of insufficient regulatory efficiency in the insurance market. The overall approach is as follows:

[0026] By analyzing the authenticity-related data of pre-processed port safety production liability insurance information, an authenticity assessment coefficient for port safety production liability insurance information is obtained. Similarly, by analyzing the accuracy-related data of pre-processed port safety production liability insurance information, an accuracy assessment coefficient for port safety production liability insurance information is obtained. A comprehensive evaluation of both the authenticity and accuracy assessment coefficients yields a verification assessment coefficient for port safety production liability insurance information. This verification assessment coefficient is then compared with a verification assessment threshold for port safety production liability insurance information. Based on the threshold comparison results, the verification of policy information is further optimized, thereby improving the regulatory efficiency of the insurance market.

[0027] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1The diagram shows a flowchart of a port safety production liability insurance information verification method based on safety risk, provided in an embodiment of this application. The method includes the following steps: collecting data related to the authenticity and accuracy of port safety production liability insurance information; preprocessing the data to obtain preprocessed data on the authenticity and accuracy of the port safety production liability insurance information; analyzing the preprocessed data on the authenticity of the port safety production liability insurance information to obtain an authenticity evaluation coefficient; and analyzing the preprocessed data on the accuracy of the port safety production liability insurance information to obtain an accuracy evaluation coefficient. The authenticity assessment coefficient and accuracy assessment coefficient of port safety production liability insurance information are comprehensively evaluated to obtain the verification assessment coefficient of port safety production liability insurance information. The authenticity assessment threshold and verification assessment threshold of port safety production liability insurance information are obtained from the database. The difference between the authenticity assessment coefficient and the authenticity assessment threshold is calculated. Based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold, preliminary optimization measures for the authenticity of port safety production liability insurance information are obtained. The verification assessment coefficient and the verification assessment threshold of port safety production liability insurance information are compared, and the verification of policy information is further optimized based on the threshold comparison results.

[0029] Furthermore, data related to the authenticity and accuracy of port safety production liability insurance information include: data related to the authenticity of port safety production liability insurance information includes: the average data matching rate of multiple basic policy information, document consistency ratio, and historical claims frequency; data related to the accuracy of port safety production liability insurance information includes: data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects.

[0030] In this embodiment, the average data matching rate of multiple basic information of the policy refers to the average degree of matching between the various basic information of the policy (such as the insured's name, insurance period, insurance amount, etc.) and the actual records or official data sources. It can be obtained by comparing with official databases and enterprise registration information and calculating.

[0031] Document consistency ratio refers to the degree of consistency between policy documents (such as electronic policies and paper policies) or between policy documents and related supporting documents. It is calculated by comparing the content of different documents.

[0032] Historical claims frequency refers to the ratio of the number of insured incidents to the number of insurance policies within a certain period, which can be obtained from the insurance company's claims records.

[0033] The data validation pass rate refers to the proportion of data items that pass validation out of the total number of data items during the data validation process. It can be calculated through data validation.

[0034] The historical error rate of data entry refers to the proportion of data items that were entered incorrectly in the past out of the total number of data items, which can be obtained through historical record analysis.

[0035] The insured object verification rate refers to the degree to which the actual verification of the insured object (such as ships, cargo, etc.) matches the policy record, and can be obtained through on-site verification.

[0036] Furthermore, the specific preprocessing process for the data related to the authenticity and accuracy of port safety production liability insurance information is as follows: Preprocessing includes data cleaning, data transformation, and feature extraction; Data cleaning includes filling missing values, deleting outliers and duplicate values; Data transformation: Normalizing the data related to the authenticity and accuracy of port safety production liability insurance information; Feature extraction: Using principal component analysis to perform feature processing on the data related to the authenticity and accuracy of port safety production liability insurance information.

[0037] In this embodiment, policy amount data was missing in 5 samples. It was decided to fill these missing values ​​with the average value of this feature. It was found that the policy amount of 20 samples was much higher than that of other samples. These may be measurement errors, so they were deleted. It was found that the data of 10 samples were completely duplicated. These duplicate samples were deleted. Data cleaning ensured the integrity and accuracy of the dataset and reduced the impact of noise and errors on the model.

[0038] The specific process of principal component analysis is as follows: standardize the data, calculate the covariance matrix of the data, calculate the eigenvalues ​​and eigenvectors of the covariance matrix, select principal components, project the original data onto the principal components, and obtain new features.

[0039] Furthermore, the specific analysis process for analyzing the authenticity-related data of the preprocessed port safety production liability insurance information is as follows: a comprehensive analysis is conducted on the average data matching rate, document consistency ratio, and historical claim frequency of multiple basic information items of the policy. The average data matching rate, document consistency ratio, and historical claim frequency of multiple basic information items of the port safety production liability insurance information are multiplied by their corresponding weights and processed to obtain the authenticity evaluation coefficient of the port safety production liability insurance information.

[0040] In this embodiment, the formula for obtaining the authenticity assessment coefficient of port safety production liability insurance information is as follows:

[0041] ;

[0042] ;

[0043] In the formula, This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This is represented by the average data matching rate of multiple basic information items in the policy. Expressed as document consistency ratio, This is represented by historical claims frequency. The weighting factor represents the average data matching rate of multiple basic information items in the policy. The weighting factor is represented as the document consistency ratio. The weighting factor is represented as the historical claim frequency. It is expressed as a natural constant.

[0044] In this embodiment, the weighting factors of the average data matching rate of multiple basic information of the policy, the document consistency ratio, and the historical claims frequency represent the numerical values ​​of the influence of the authenticity assessment coefficient and the accuracy assessment coefficient of the port safety production liability insurance information on the verification assessment coefficient of the port safety production liability insurance information, respectively.

[0045] When using this system, the weighting factors for the average data matching rate, document consistency ratio, and historical claims frequency of multiple basic policy information are directly obtained from the port safety production liability insurance information database. The correspondence between these factors can be a pre-defined mapping relationship. For example, a mapping set can be constructed using historical data, which includes the average data matching rate, document consistency ratio, and historical claims frequency of multiple basic policy information, as well as their corresponding weighting factors. The real-time values ​​of the average data matching rate, document consistency ratio, and historical claims frequency of multiple basic policy information are then input into the mapping set to obtain the weighting factors for these factors. The mapping relationship can be one-to-one or many-to-one.

[0046] A high average data matching rate usually means that the information in the policy is highly consistent with the actual business data. This will help improve the document consistency ratio. A higher document consistency ratio usually means that the insurance documents are more accurate and complete. This will help reduce claims disputes caused by document errors or incompleteness, which may reduce the frequency of historical claims. A higher average data matching rate may reduce insurance fraud and incorrect insurance, thereby reducing the risk of claims and thus reducing the frequency of historical claims.

[0047] In one specific embodiment, the following table shows an example of the authenticity assessment coefficient data for port safety production liability insurance information.

[0048] Table 1. Example of data for assessing the authenticity of port safety production liability insurance information.

[0049]

[0050] When the weighting factors for the average data matching rate of multiple basic policy information, the document consistency ratio, and the historical claims frequency are 0.1, 0.1, and 0.8 respectively, such as Figure 2 The image shown is an image of the authenticity assessment coefficient of port safety production liability insurance information in a port safety production liability insurance information verification method based on safety risk provided in an embodiment of this application. Figure 2 As can be seen from the data in Table 1, when the document consistency ratio and historical claims frequency remain constant, the higher the average data matching rate of multiple basic information items in the policy, the higher the authenticity assessment coefficient of the port safety production liability insurance information.

[0051] Furthermore, the specific analysis process for analyzing the accuracy-related data of the preprocessed port safety production liability insurance information is as follows: A comprehensive analysis is conducted on the data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of the insured object. The data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of the insured object of the port safety production liability insurance information are multiplied by their corresponding weights and processed to obtain the accuracy evaluation coefficient of the port safety production liability insurance information.

[0052] In this embodiment, the formula for obtaining the accuracy assessment coefficient of port safety production liability insurance information is as follows:

[0053] ;

[0054] ;

[0055] In the formula, This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This is expressed as the data validation pass rate. Expressed as document consistency ratio, This is represented as the historical error rate of information entry. Expressed as the verification rate of the insured object, This is represented as a weighting factor for the data validation pass rate. The weighting factor is represented as the document consistency ratio. This represents a weighting factor for the historical error rate of information entry. This is represented as a weighting factor for the verification rate of the insured object.

[0056] The weighting factors of data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects represent the numerical values ​​of the degree of influence of data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects on the accuracy assessment coefficient of port safety production liability insurance information.

[0057] When using the system, the weighting factors for data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects are directly obtained from the port safety production liability insurance information database. The correspondence between these factors can be a pre-defined mapping relationship. For example, a mapping set can be constructed using historical data for data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects, along with their corresponding weighting factors. The real-time values ​​of data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects are then input into the mapping set to obtain the weighting factors for these factors. The mapping relationship can be one-to-one or many-to-one.

[0058] A high data validation pass rate means that the data in the policy is correct and meets the requirements, which usually leads to an increase in the document consistency ratio. A high data validation pass rate usually means that the information entry is accurate, so the historical error rate will be low. A high insured object verification rate means that the insured object has been effectively verified, which helps to improve the data validation pass rate and document consistency ratio.

[0059] Furthermore, the specific process of subtracting the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is as follows: obtain the authenticity assessment threshold of port safety production liability insurance information from the database, subtract the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information to obtain the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information.

[0060] In this embodiment, for example, the authenticity assessment threshold of the port safety production liability insurance information obtained from the database is 0.7, and the calculated authenticity assessment coefficient of the port safety production liability insurance information is 0.8. Then, the difference between the authenticity assessment coefficient of the port safety production liability insurance information and the authenticity assessment threshold of the port safety production liability insurance information is 0.8-0.7=0.1.

[0061] The authenticity assessment threshold for port safety production liability insurance information obtained from the database is 0.7, while the calculated authenticity assessment coefficient for port safety production liability insurance information is 0.5. Therefore, the difference between the authenticity assessment coefficient and the authenticity assessment threshold for port safety production liability insurance information is 0.5-0.7=-0.2.

[0062] By calculating the difference, the authenticity assessment results of port safety production liability insurance information can be quantified, which facilitates risk management and decision-making.

[0063] Furthermore, based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information, the specific matching process for preliminary optimization measures to improve the authenticity of port safety production liability insurance information is as follows: The difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is compared with a preset tolerance range. If the difference is within the tolerance range, the port safety production liability insurance information is marked as having passed the authenticity check. If the difference is outside the tolerance range, preliminary optimization measures are implemented for the port safety production liability insurance information. These preliminary optimization measures include data verification, risk assessment level correction, and policy information correction.

[0064] In this embodiment, the tolerance range is assumed to be ±0.05. If the difference is -0.05, within the tolerance range of ±0.05, the insurance information is marked as having passed the authenticity test. If the difference is +0.1, preliminary optimization measures are performed. The preliminary optimization measures include data verification, risk assessment level correction, and policy information correction.

[0065] By setting tolerance ranges, risks can be effectively controlled, ensuring that only insurance information that meets certain authenticity standards can pass the inspection.

[0066] Data verification refers to comparing the data in the policy with industry standards or historical data. Specifically, through the EDI system, insurance companies and port enterprises can automatically exchange policy information, reducing the possibility of human input errors. Risk assessment level correction refers to adjusting the risk assessment level based on the verification results. Specifically, through real-time data analysis technology, such as stream processing, it responds instantly to data changes and adjusts the risk assessment level. Policy information correction refers to correcting erroneous information found during the verification process. Specifically, it applies data cleaning techniques, such as deduplication, correction, and completion, to correct erroneous or incomplete data.

[0067] Furthermore, the specific comparison process for comparing the verification evaluation coefficient of port safety production liability insurance information with the verification evaluation threshold of port safety production liability insurance information is as follows: The verification evaluation threshold of port safety production liability insurance information is obtained from the database; the verification evaluation coefficient of port safety production liability insurance information is compared with the verification evaluation threshold; if the verification evaluation coefficient of port safety production liability insurance information is greater than or equal to the verification evaluation threshold, the port safety production liability insurance information is marked as having passed verification; if the verification evaluation coefficient of port safety production liability insurance information is less than the verification evaluation threshold, the port safety production liability insurance information undergoes in-depth optimization; in-depth optimization includes establishing a predictive model and predicting insurance fraud behavior.

[0068] In this embodiment, for example, assuming the verification assessment threshold is 0.90, by analyzing the insurance information data, the verification assessment coefficient of the port safety production liability insurance information is calculated to be 0.92, which is greater than the threshold of 0.90, and the insurance information is marked as passed verification. If by analyzing the insurance information data, the verification assessment coefficient of the port safety production liability insurance information is calculated to be 0.85, which is less than the threshold of 0.90, deep optimization is performed, including establishing a prediction model and predicting insurance fraud behavior.

[0069] By comparing the verification evaluation coefficient with the threshold, potential insurance fraud and risks can be quickly identified.

[0070] Furthermore, the specific process of establishing the prediction model is as follows: obtain the recurrent neural network model from the database, determine the number of layers, the number of neurons in each layer, and the activation function of the recurrent neural network, divide the data related to the authenticity of the preprocessed port safety production liability insurance information and the data related to the accuracy of the preprocessed port safety production liability insurance information into training set and test set according to a certain ratio, input the training set into the recurrent neural network for training to obtain the preliminary prediction model, use the test set to evaluate the performance of the preliminary prediction model, and obtain the prediction model by adjusting the hyperparameters and model structure.

[0071] In this embodiment, a predefined recurrent neural network model structure is retrieved from a database or model library. A two-layer long short-term memory network is set up, with each layer containing 128 neurons and the activation function being ReLU. 70% of the data is randomly selected from the dataset as the training set, and the remaining 30% is used as the test set. The RNN model is trained using the training set data, and the performance of the trained preliminary prediction model is evaluated using the test set to obtain the test results. If the model accuracy is lower than 90%, the model structure can be adjusted by increasing the number of layers or neurons to improve the model accuracy. If the model accuracy exceeds 90%, it indicates that the preliminary prediction model meets the requirements, and the prediction model is obtained. By inputting the basic information of the insurance, the probability of insurance fraud is obtained.

[0072] The ReLU function stands for Rectified Linear Unit, and its specific expression is: f(x) = max(0,x).

[0073] Recurrent neural networks can capture long-term dependencies in time series data, improving prediction accuracy.

[0074] Furthermore, the specific method for obtaining the verification and evaluation coefficient of port safety production liability insurance information is as follows: In the formula, This represents the verification and assessment coefficient for port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This represents the weighting factor for the authenticity assessment coefficient of port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This represents the weighting factor for the accuracy assessment coefficient of port safety production liability insurance information.

[0075] In this embodiment, the weighting factors of the authenticity assessment coefficient and the accuracy assessment coefficient of the port safety production liability insurance information respectively represent the numerical values ​​of the influence of the authenticity assessment coefficient and the accuracy assessment coefficient of the port safety production liability insurance information on the verification assessment coefficient of the port safety production liability insurance information.

[0076] When using this system, the weighting factors for the authenticity assessment coefficient and the accuracy assessment coefficient of port safety production liability insurance information are directly obtained from the port safety production liability insurance information database. The correspondence can be a pre-defined mapping relationship. For example, a mapping set can be constructed using historical data, showing the authenticity and accuracy assessment coefficients of port safety production liability insurance information, along with their corresponding weighting factors. The real-time values ​​of these coefficients are then input into the mapping set to obtain the weighting factors for both the authenticity and accuracy assessment coefficients. The mapping relationship can be one-to-one or many-to-one.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for verifying port safety production liability insurance information based on safety risks, characterized in that, Includes the following steps: Collect data related to the authenticity and accuracy of port safety production liability insurance information, preprocess the data related to the authenticity and accuracy of port safety production liability insurance information, and obtain preprocessed data related to the authenticity and accuracy of port safety production liability insurance information. The authenticity-related data of the pre-processed port safety production liability insurance information are analyzed to obtain the authenticity evaluation coefficient of the port safety production liability insurance information. The accuracy-related data of the pre-processed port safety production liability insurance information are analyzed to obtain the accuracy evaluation coefficient of the port safety production liability insurance information. The authenticity evaluation coefficient and the accuracy evaluation coefficient of the port safety production liability insurance information are comprehensively evaluated to obtain the verification evaluation coefficient of the port safety production liability insurance information. The authenticity assessment threshold and verification assessment threshold of port safety production liability insurance information are obtained from the database. The difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is calculated. Based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information, preliminary optimization measures for the authenticity of port safety production liability insurance information are obtained. The verification assessment coefficient and the verification assessment threshold of port safety production liability insurance information are compared. Based on the threshold comparison results, the verification of policy information is further optimized. The data related to the authenticity of port safety production liability insurance information includes: the average data matching rate of multiple basic policy information items, document consistency ratio, and historical claims frequency. The specific analysis process for analyzing the authenticity-related data of pre-processed port safety production liability insurance information is as follows: A comprehensive analysis was conducted on the average data matching rate, document consistency ratio, and historical claim frequency of various basic information of the policy. The average data matching rate, document consistency ratio, and historical claim frequency of various basic information of the port safety production liability insurance policy were multiplied by their corresponding weights and processed to obtain the authenticity assessment coefficient of the port safety production liability insurance information. The accuracy-related data for port safety production liability insurance information includes: data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects. The specific analysis process for analyzing the accuracy-related data of pre-processed port safety production liability insurance information is as follows: A comprehensive analysis was conducted on the data verification pass rate, document consistency ratio, historical error rate of information entry, and verification rate of insured objects for port safety production liability insurance information. The accuracy evaluation coefficient of port safety production liability insurance information was obtained by multiplying these four factors by their corresponding weights and processing the results. The specific formula for obtaining the authenticity assessment coefficient of the port safety production liability insurance information is as follows: ； ； In the formula, This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This is represented by the average data matching rate of multiple basic information items in the policy. Expressed as document consistency ratio, This is represented by historical claims frequency. The weighting factor represents the average data matching rate of multiple basic information items in the policy. The weighting factor is represented as the document consistency ratio. The weighting factor is represented as the historical claim frequency. Represented as natural constants; The specific formula for obtaining the accuracy assessment coefficient of the port safety production liability insurance information is as follows: ； ； In the formula, This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This is expressed as the data validation pass rate. Expressed as document consistency ratio, This is represented as the historical error rate of information entry. Expressed as the verification rate of the insured object, This is represented as a weighting factor for the data validation pass rate. The weighting factor is represented as the document consistency ratio. This represents a weighting factor for the historical error rate of information entry. This is represented as a weighting factor for the verification rate of the insured object; The specific method for obtaining the verification and evaluation coefficient of the port safety production liability insurance information is as follows: ； In the formula, This represents the verification and assessment coefficient for port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. This represents the weighting factor for the authenticity assessment coefficient of port safety production liability insurance information. This is represented by the accuracy assessment coefficient for port safety production liability insurance information. The weighting factor is used to evaluate the accuracy of port safety production liability insurance information. Deep optimization includes building predictive models and predicting insurance fraud. The specific process for establishing the prediction model is as follows: The recurrent neural network model is obtained from the database. The number of layers, the number of neurons in each layer, and the activation function of the recurrent neural network are determined. The authenticity-related data and the accuracy-related data of the preprocessed port safety production liability insurance information are divided into training set and test set according to a certain ratio. The training set is input into the recurrent neural network for training to obtain a preliminary prediction model. The performance of the preliminary prediction model is evaluated using the test set. The prediction model is obtained by adjusting the hyperparameters and model structure. The specific preprocessing process for preprocessing the data related to the authenticity and accuracy of port safety production liability insurance information is as follows: Preprocessing includes data cleaning, data transformation, and feature extraction; Data cleaning includes filling in missing values, removing outliers and duplicates; Data transformation: Normalize the data related to the authenticity of port safety production liability insurance information and the data related to the accuracy of port safety production liability insurance information; Feature extraction: Principal component analysis was used to perform feature processing on data related to the authenticity and accuracy of port safety production liability insurance information.

2. The port safety production liability insurance information verification method based on safety risk as described in claim 1, characterized in that, The specific process of subtracting the authenticity assessment coefficient of port safety production liability insurance information from the authenticity assessment threshold of port safety production liability insurance information is as follows: The authenticity assessment threshold of port safety production liability insurance information is obtained from the database. The difference between the authenticity assessment coefficient of port safety production liability insurance information and the authenticity assessment threshold is obtained.

3. The method for verifying port safety production liability insurance information based on safety risks as described in claim 1, characterized in that, The specific matching process for obtaining preliminary optimization measures for the authenticity of port safety production liability insurance information based on the difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is as follows: The difference between the authenticity assessment coefficient and the authenticity assessment threshold of port safety production liability insurance information is compared with a preset tolerance range. If the difference is within the tolerance range, the port safety production liability insurance information is marked as having passed the authenticity test. If the difference is outside the tolerance range, preliminary optimization measures are taken for the port safety production liability insurance information. The initial optimization measures include data verification, risk assessment level correction, and policy information correction.

4. The method for verifying port safety production liability insurance information based on safety risks as described in claim 1, characterized in that, The specific comparison process for comparing the verification and evaluation coefficient of port safety production liability insurance information with the verification and evaluation threshold of port safety production liability insurance information is as follows: The verification and assessment threshold for port safety production liability insurance information is obtained from the database. The verification and assessment coefficient of the port safety production liability insurance information is compared with the verification and assessment threshold. If the verification and assessment coefficient of the port safety production liability insurance information is greater than or equal to the verification and assessment threshold, the port safety production liability insurance information is marked as having passed verification. If the verification and assessment coefficient of the port safety production liability insurance information is less than the verification and assessment threshold, the port safety production liability insurance information is further optimized.

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