CN113159568A - System and method for estimating insurance risk - Google Patents
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- CN113159568A CN113159568A CN202110419781.8A CN202110419781A CN113159568A CN 113159568 A CN113159568 A CN 113159568A CN 202110419781 A CN202110419781 A CN 202110419781A CN 113159568 A CN113159568 A CN 113159568A
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Abstract
The invention relates to a system and a method for estimating insurance risk, which comprises an insurance entry module, an insurance processing module, an insurance data analysis module, an insurance retrieval module, an insurance comparison module and a data generation module; the letter input module is connected with one-way input of the letter-keeping processing module, the letter-keeping data analysis module, the letter-keeping retrieval module and the letter-keeping comparison module are all connected with two-way output of the letter-keeping processing module, and the letter-keeping processing module is connected with one-way output of the data generation module. According to the insurance letter risk estimation system and method, data comparison calculation is carried out in a big data statistics mode, personnel participation is reduced, uncertain factors are avoided, time for verifying information of both parties of a guarantee is shortened, manpower is saved, meanwhile, deviation caused by personnel participation is reduced, and result reliability is improved.
Description
Technical Field
The invention relates to the technical field of risk assessment, in particular to a system and a method for estimating insurance policy risk.
Background
The insurance letter is also called a guarantee certificate, and refers to a written credit guarantee certificate which is issued by a bank, an insurance company, a guarantee company or an individual to a third party according to the request of an applicant. The bank-issued warranty is generally referred to as a guarantee letter, and the written warranty issued by other guarantors is generally referred to as a guarantee certificate. Ensuring that a certain amount of money, a certain period of time, or an economic obligation is fulfilled on behalf of the insurer when the applicant fails to fulfill his obligation or obligation under the two-party agreement. The insurance policy is the insurance certificate, and for convenience, common companies and banks print insurance certificates with certain formats, and the functions of the insurance policy include goods delivery by insurance, issuing a clean bill of lading by insurance, and backing up a pre-borrowing bill of lading by insurance.
However, the existing insurance policy usually needs a lot of personnel to participate in the generation process, the information of both security parties is verified, the verification is time-consuming and labor-consuming, and meanwhile, due to the fact that certain deviation is generated on the analysis result by the participation of the personnel, the result reliability is reduced.
Disclosure of Invention
The invention aims to provide a system for estimating insurance policy risk, which aims to solve the problems that the prior insurance policy proposed by the background art often needs a lot of personnel to participate in the generation process, the information of both parties of the insurance policy is verified, the verification is time-consuming and labor-consuming, and meanwhile, the participation of personnel often generates certain deviation on the analysis result, so that the result reliability is reduced.
In order to achieve the purpose, the invention provides the following technical scheme: a kind of insurance risk estimation system, including entering module, insurance processing module, insurance data analysis module, insurance search module, insurance contrast module and data generation module of the insurance;
the letter input module is connected with one-way input of the letter-keeping processing module, the letter-keeping data analysis module, the letter-keeping retrieval module and the letter-keeping comparison module are all connected with two-way output of the letter-keeping processing module, and the letter-keeping processing module is connected with one-way output of the data generation module.
The insurance letter input module is used for receiving the insurance letter in the standard format and uploading the insurance letter in the standard format to the insurance letter processing module;
the insurance letter processing module is used for receiving the insurance letter sent by the insurance letter input module and sending the insurance letter data obtained by processing to the insurance letter data analysis module;
the insurance function data analysis module is used for receiving and analyzing the insurance function data sent by the insurance function processing module;
the insurance letter retrieval module is used for performing big data retrieval on the content uploaded by the insurance letter input module, comparing the authenticity of the content, reducing the false probability and the insurance risk, and feeding back the retrieval result to the insurance letter processing module;
the insurance policy comparison module is used for comparing the insurance policy contents, reducing the repetition rate of the insurance policy contents through big data comparison and avoiding repeated guarantee;
and the data generation module is used for generating a formatted document, forming the content of the insurance envelope and using the content as the insurance envelope contract.
The invention also provides a method for estimating the risk of the insurance letter, which comprises the following steps:
s1, inputting the insurance letter into the insurance letter input module according to the standard format, and uploading the insurance letter to the insurance letter processing module through the insurance letter input module;
s2, the uploaded data carries out big data retrieval on the uploaded content through the insurance letter retrieval module, the authenticity of the content is compared, the false probability is reduced, the guarantee risk is reduced, and the retrieval result is fed back to the insurance letter processing module;
s3, the insurance letter processing module analyzes the information through the insurance letter data analysis module, compares the dimension characteristics and determines the risk coefficient;
s4, the insurance policy comparison module compares the insurance policy contents, reduces the repetition rate of the insurance policy contents through big data comparison and avoids repeated guarantee;
and S5, generating a formatted document after the steps are completed, and forming the content of the insurance envelope to be used for signing the insurance envelope.
Preferably, said insurance policy includes, in a standard format, guarantor information, guarantor-specified guarantor assets, guarantor information, and guarantor target;
the information of the guarantor comprises a name, identity card information, bank assets, fixed assets, credit investigation conditions and the like;
the guarantor designates a guaranty asset as including an asset condition, an asset valuation, and the like;
the information of the insured life comprises name, ID card information, fixed assets of bank assets, credit investigation condition, etc.;
the guaranteed target includes asset status, asset valuation, development prospect analysis report and business report.
Preferably, the uploaded data of S2 is subjected to big data retrieval on the uploaded content through a preserved letter retrieval module, and the mapping data of the preset risk type and the target keyword set is obtained by obtaining each historical risk data in the designated historical risk set and the corresponding historical risk type thereof and analyzing the risk type according to a preset analysis strategy;
and according to the preset risk type, carrying out data retrieval on the guarantee assets, and avoiding the bad asset information of the guarantee assets.
Preferably, the step of analyzing the content of the insurance policy by the insurance data analysis module through the S3 insurance processing module further includes the following steps:
s31: setting a maximum collaborative training risk evaluation model as Tmax;
s32: performing feature screening by using feature engineering aiming at an initial training data set, wherein the feature engineering is a maximum correlation minimum redundancy combined maximum mutual information coefficient feature selection strategy, is recorded as MR-MIC, and is combined with a reference risk assessment model and the judgment accuracy rate thereof to obtain a screened training data set;
s33: setting a grid division size parameter B, generating various positive integer combinations of (m, n) meeting the condition that m is multiplied by n < B, wherein m and n are values of grid transverse division and grid longitudinal division;
s34: according to a dynamic distribution principle, inputting enough data into a coordinate axis according to characteristics, traversing each group (m, n), uniformly dividing a characteristic value space of X into m parts, finding a division of a characteristic Y which enables mutual information between the characteristic X and the characteristic Y to be maximum by utilizing dynamic programming, fixing the division of the characteristic Y, finding a division of the characteristic X which enables the mutual information between the characteristic X and the characteristic Y to be maximum by utilizing the dynamic programming, then fixing the division of the characteristic X, finding a division of the characteristic Y which enables the mutual information between the characteristic X and the characteristic Y to be maximum by utilizing the dynamic programming, and finally outputting a maximum mutual information value Imn (X, Y) corresponding to each group (m, n);
the maximum mutual information coefficient for each pair X and Y is calculated according to the following equation (1)
：
Calculating the interaction coefficient of the target object by the formula (1) to obtain a risk coefficient;
preferably, in S4, the coverage content repetition rate is reduced by the big data contrast, the keyword is searched by the coverage management system, and the keyword is analyzed for the specific feature, thereby identifying the target data in a targeted manner.
Compared with the prior art, the invention has the beneficial effects that: according to the insurance letter risk estimation system and method, data comparison calculation is carried out in a big data statistics mode, personnel participation is reduced, uncertain factors are avoided, time for verifying information of both parties of a guarantee is shortened, manpower is saved, meanwhile, deviation caused by personnel participation is reduced, and result reliability is improved.
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FIG. 1 is a schematic view of the working process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a kind of insurance risk estimation system, including entering module, insurance processing module, insurance data analysis module, insurance search module, insurance contrast module and data generation module of the insurance;
the letter input module is connected with one-way input of the letter-keeping processing module, the letter-keeping data analysis module, the letter-keeping retrieval module and the letter-keeping comparison module are all connected with two-way output of the letter-keeping processing module, and the letter-keeping processing module is connected with one-way output of the data generation module.
The insurance letter input module is used for receiving the insurance letter in the standard format and uploading the insurance letter in the standard format to the insurance letter processing module;
the insurance letter processing module is used for receiving the insurance letter sent by the insurance letter input module and sending the insurance letter data obtained by processing to the insurance letter data analysis module;
the insurance function data analysis module is used for receiving and analyzing the insurance function data sent by the insurance function processing module;
the insurance letter retrieval module is used for performing big data retrieval on the content uploaded by the insurance letter input module, comparing the authenticity of the content, reducing the false probability and the insurance risk, and feeding back the retrieval result to the insurance letter processing module;
the insurance policy comparison module is used for comparing the insurance policy contents, reducing the repetition rate of the insurance policy contents through big data comparison and avoiding repeated guarantee;
and the data generation module is used for generating a formatted document, forming the content of the insurance envelope and using the content as the insurance envelope contract.
The invention also provides a method for estimating the risk of the insurance letter, which comprises the following steps:
s1, inputting the insurance letter into the insurance letter input module according to the standard format, and uploading the insurance letter to the insurance letter processing module through the insurance letter input module;
s2, the uploaded data carries out big data retrieval on the uploaded content through the insurance letter retrieval module, the authenticity of the content is compared, the false probability is reduced, the guarantee risk is reduced, and the retrieval result is fed back to the insurance letter processing module;
s3, the insurance letter processing module analyzes the information through the insurance letter data analysis module, compares the dimension characteristics and determines the risk coefficient;
s4, the insurance policy comparison module compares the insurance policy contents, reduces the repetition rate of the insurance policy contents through big data comparison and avoids repeated guarantee;
and S5, generating a formatted document after the steps are completed, and forming the content of the insurance envelope to be used for signing the insurance envelope.
Further, said insurance policy includes, in a standard format, guarantor information, guarantor-specified guarantor assets, guarantor information, and guarantor target;
the information of the guarantor comprises a name, identity card information, bank assets, fixed assets, credit investigation conditions and the like;
the guarantor designates a guaranty asset as including an asset condition, an asset valuation, and the like;
the information of the insured life comprises name, ID card information, fixed assets of bank assets, credit investigation condition, etc.;
the guaranteed target includes asset status, asset valuation, development prospect analysis report and business report.
Further, the uploaded data of S2 is subjected to big data retrieval on the uploaded content through a preserved letter retrieval module, and the mapping data of the preset risk type and the target keyword set is obtained by obtaining each historical risk data in the designated historical risk set and the corresponding historical risk type thereof and analyzing the risk type according to a preset analysis strategy;
and according to the preset risk type, carrying out data retrieval on the guarantee assets, and avoiding the bad asset information of the guarantee assets.
Further, the step of analyzing the content of the insurance letter by the S3 insurance letter processing module through the insurance letter data analysis module includes the following steps:
s31: setting a maximum collaborative training risk evaluation model as Tmax;
s32: performing feature screening by using feature engineering aiming at an initial training data set, wherein the feature engineering is a maximum correlation minimum redundancy combined maximum mutual information coefficient feature selection strategy, is recorded as MR-MIC, and is combined with a reference risk assessment model and the judgment accuracy rate thereof to obtain a screened training data set;
s33: setting a grid division size parameter B, generating various positive integer combinations of (m, n) meeting the condition that m is multiplied by n < B, wherein m and n are values of grid transverse division and grid longitudinal division;
s34: according to a dynamic distribution principle, inputting enough data into a coordinate axis according to characteristics, traversing each group (m, n), uniformly dividing a characteristic value space of X into m parts, finding a division of a characteristic Y which enables mutual information between the characteristic X and the characteristic Y to be maximum by utilizing dynamic programming, fixing the division of the characteristic Y, finding a division of the characteristic X which enables the mutual information between the characteristic X and the characteristic Y to be maximum by utilizing the dynamic programming, then fixing the division of the characteristic X, finding a division of the characteristic Y which enables the mutual information between the characteristic X and the characteristic Y to be maximum by utilizing the dynamic programming, and finally outputting a maximum mutual information value Imn (X, Y) corresponding to each group (m, n);
the maximum mutual information coefficient for each pair X and Y is calculated according to the following equation (1)
：
Calculating the interaction coefficient of the target object by the formula (1) to obtain a risk coefficient;
further, in S4, the coverage content repetition rate is reduced by the big data contrast, the keyword is searched by the coverage management system, and the keyword is analyzed for the specific feature, thereby identifying the target data with a target.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes in the embodiments and/or modifications of the invention can be made, and equivalents may be substituted for elements thereof without departing from the scope of the invention
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (9)
1. A kind of insurance risk estimation system, wherein include the entering module of insurance letter, insurance letter processing module, insurance letter data analysis module, insurance letter search module, insurance letter comparison module and data generation module;
the insurance letter input module is used for receiving the insurance letter in the standard format and uploading the insurance letter in the standard format to the insurance letter processing module;
the insurance letter processing module is used for receiving the insurance letter sent by the insurance letter input module and sending the insurance letter data obtained by processing to the insurance letter data analysis module;
the insurance function data analysis module is used for receiving and analyzing the insurance function data sent by the insurance function processing module;
the insurance letter retrieval module is used for performing big data retrieval on the content uploaded by the insurance letter input module, comparing the authenticity of the content, reducing the false probability and the insurance risk, and feeding back the retrieval result to the insurance letter processing module;
the insurance policy comparison module is used for comparing the insurance policy contents, reducing the repetition rate of the insurance policy contents through big data comparison and avoiding repeated guarantee;
and the data generation module is used for generating a formatted document, forming the content of the insurance envelope and using the content as the insurance envelope contract.
2. A method for estimating risk of warranty, characterized by: the method comprises the following steps:
s1, inputting the insurance letter into the insurance letter input module according to the standard format, and uploading the insurance letter to the insurance letter processing module through the insurance letter input module;
s2, the uploaded data carries out big data retrieval on the uploaded content through the insurance letter retrieval module, the authenticity of the content is compared, the false probability is reduced, the guarantee risk is reduced, and the retrieval result is fed back to the insurance letter processing module;
s3, the insurance letter processing module analyzes the information through the insurance letter data analysis module, compares the dimension characteristics and determines the risk coefficient;
s4, the insurance policy comparison module compares the insurance policy contents, reduces the repetition rate of the insurance policy contents through big data comparison and avoids repeated guarantee;
and S5, generating a formatted document after the steps are completed, and forming the content of the insurance envelope to be used for signing the insurance envelope.
3. A method of risk assessment of a letter of care according to claim 2, characterized by: the said insurance policy includes, in a standard format, guarantor information, guarantor-specified guaranty assets, insured subject information, and insured subject.
4. A letter-of-care risk estimation method according to claim 3, characterized by: the guarantor information includes a name, identification card information, bank assets, fixed assets, and credit investigation conditions.
5. A letter-of-care risk estimation method according to claim 3, characterized by: the guarantor specifies a guaranty asset comprising an asset condition, an asset valuation.
6. A letter-of-care risk estimation method according to claim 3, characterized by: the information of the insured life includes name, ID card information, fixed assets of bank assets and credit investigation condition.
7. A letter-of-care risk assessment system according to claim 3, wherein: the vouched targets include asset status, asset valuation, development prospect analysis reports, and business reports.
8. A letter-of-care risk estimation method according to claim 3, characterized by: the uploaded data of the S2 is subjected to big data retrieval on the uploaded content through a preserved letter retrieval module, and the mapping data of the preset risk type and the target keyword set is obtained by obtaining each historical risk data in the appointed historical risk set and the corresponding historical risk type and analyzing the risk type according to the preset analysis strategy;
and according to the preset risk type, carrying out data retrieval on the guarantee assets, and avoiding the bad asset information of the guarantee assets.
9. A method of risk assessment of a letter of care according to claim 2, characterized by: and S4, reducing the repetition rate of the content of the insurance policy through big data contrast, searching the keywords through the insurance policy management system, analyzing specific characteristics and identifying target data in a targeted manner.
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| CN202110419781.8A CN113159568A (en) | 2021-04-19 | 2021-04-19 | System and method for estimating insurance risk |
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| CN202110419781.8A CN113159568A (en) | 2021-04-19 | 2021-04-19 | System and method for estimating insurance risk |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024066045A1 (en) * | 2022-09-27 | 2024-04-04 | 深圳先进技术研究院 | Guarantee information extraction and value prediction method and system, terminal, and storage medium |
| CN118154333A (en) * | 2024-05-13 | 2024-06-07 | 福州无比欢信息科技有限公司 | Intelligent electronic policy service management system based on Internet |
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| CN110033272A (en) * | 2019-04-03 | 2019-07-19 | 中国工商银行股份有限公司 | Letter of guarantee data processing method, equipment and system based on block chain |
| CN111738870A (en) * | 2020-07-28 | 2020-10-02 | 工保科技(浙江)有限公司 | Method and platform for identifying insurance risk of engineering performance guarantee based on characteristic engineering |
| CN112669129A (en) * | 2020-12-11 | 2021-04-16 | 深圳市融锋科技有限公司 | Insurance letter generation method and system, electronic equipment and computer readable storage medium |
-
2021
- 2021-04-19 CN CN202110419781.8A patent/CN113159568A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110033272A (en) * | 2019-04-03 | 2019-07-19 | 中国工商银行股份有限公司 | Letter of guarantee data processing method, equipment and system based on block chain |
| CN111738870A (en) * | 2020-07-28 | 2020-10-02 | 工保科技(浙江)有限公司 | Method and platform for identifying insurance risk of engineering performance guarantee based on characteristic engineering |
| CN112669129A (en) * | 2020-12-11 | 2021-04-16 | 深圳市融锋科技有限公司 | Insurance letter generation method and system, electronic equipment and computer readable storage medium |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024066045A1 (en) * | 2022-09-27 | 2024-04-04 | 深圳先进技术研究院 | Guarantee information extraction and value prediction method and system, terminal, and storage medium |
| CN118154333A (en) * | 2024-05-13 | 2024-06-07 | 福州无比欢信息科技有限公司 | Intelligent electronic policy service management system based on Internet |
| CN118154333B (en) * | 2024-05-13 | 2024-07-12 | 福州无比欢信息科技有限公司 | Intelligent electronic policy service management system based on Internet |
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