CN109993661A - A kind of settlement of insurance claim data analysing method and system - Google Patents
A kind of settlement of insurance claim data analysing method and system Download PDFInfo
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Abstract
The invention belongs to data analysis technique fields, it is related to a kind of big data analysis technology, more particularly to it is a kind of based on settlement of insurance claim data analysing method and system, method includes the following steps: S1, obtain settlement of insurance claim data, according to relation analysis model input requirements, Claims Resolution data are handled according to relation analysis model input requirements, obtain object set；S1 is obtained object set and is input in relation analysis model by S2, and relation analysis model carries out analytical calculation to the object set of input, and is obtained according to the screening of regular threshold values and meet affiliated partner set；S3 carries out quantitative analysis to affiliated partner set is met, obtains correlation rule item collection table.This method analyzes each main body different periods operational indicator and multiagent incidence relation situation of change, effectively helps insurance company's settlement of insurance claim control, adjusts Management plan, helps the planning of insurance company's management strategy.
Description
Technical field
The invention belongs to data analysis technique field, it is related to a kind of big data analysis technology, more particularly to a kind of based on guarantor
Danger Claims Resolution data analysing method and system.
Background technique
As people's insurance awareness is higher and higher, the settlement of insurance claim event that insurance risk takes place frequently also persistently increases, insurance company
The data volume of accumulation is increasing, and original analytical technology can not handle big data quantity, analyzes time-consuming also very long, analysis indexes
It is very limited, at the same need very professional data analyst do data processing, establish data model work, to personnel requirement compared with
Height, and final result user is often not yet satisfied with.
Insurance market competition is more and more fierce, and cost efficiency index for management level also starts to require tightly, in order to mention
Overall operation level is risen, analysis and summary insures reason of being in danger, and process reasonability of settling a claim obtains rule of being in danger, can effectively help to protect
Dangerous company's prior involvement, prevents pre- in advance, and people's life and property loss is lowered in subregion individual character operation setting, lower danger enterprise compensate at
This digs algorithm according to onboard data, only need to be familiar with the personnel of business just so being badly in need of one kind based on big data technology analysis system
Business diagnosis can be done, the business that reduces, which understands to link up, to be wasted, and system can be customized to the support of mass data, efficient analysis speed
The settlement of insurance claim data analysing method and system that analysis indexes show.This method analyze each main body different periods operational indicator and
Multiagent incidence relation situation of change effectively helps insurance company's settlement of insurance claim control, adjusts Management plan, helps insurance company
Management strategy planning.
Summary of the invention
The main object of the present invention is to provide a kind of settlement of insurance claim data analysing method and system, to solve the prior art
Any problem in above-mentioned and other potential problems.
In order to achieve the above object, the invention proposes a kind of settlement of insurance claim data analysing method, this method includes following
Step:
S1, access platform obtain settlement of insurance claim data, according to relation analysis model input requirements, to Claims Resolution data according to association
Analysis model input requirements are handled, and object set is obtained；
S1 is obtained object set and is input in relation analysis model by S2, and relation analysis model carries out the object set of input
Analytical calculation, and obtained according to rule threshold screening and meet affiliated partner set；
S3 carries out quantitative analysis to affiliated partner set is met, obtains correlation rule item collection table.
According to the embodiment of the present disclosure, the Claims Resolution data in the S1 are that vehicle damages during vehicle traffic accident settlement of insurance claim
Data of settling a claim and people hurt Claims Resolution data；Wherein, the vehicle damage Claims Resolution data include information of vehicles, information of reporting a case to the security authorities, dam site investigation letter
Breath, setting loss working hour information, setting loss accessory information, setting loss person's information, repair shop's information and policy information；The people hurts Claims Resolution data
Include the wounded's information, household register information, job information, information for hospital, treatment operation information, law court's information, disability authentication information, rule
Teacher's information and careful certainly information.
According to the embodiment of the present disclosure, in the S2 relation analysis model to input object set carry out analytical calculation the step of
Are as follows:
All Claims Resolution data in input object set are carried out division processing using SON algorithm by S2.1, are divided into p file
Block, each blocks of files are 1/p, and p value is the positive integer greater than 0；
S2.2 executes Apriori algorithm using each blocks of files divided through S2.1 as a sample, in one or more texts
The frequent item set being selected in part block is collected as candidate frequent item set；
Frequent item set refers to that support is more than or equal to the set of minimum support (min_sup).Wherein support refers to some collection
Close the frequency occurred in all affairs.The classic applications of frequent item set are basket analysis.
The candidate frequent item set that S2.3 obtains S2.2 merges to obtain final frequent item set, calculates each frequent item set
Support, confidence level and promotion degree；
Obtained support, confidence level, promotion angle value are compared by S2.4 with preset threshold value, and screening, which obtains, meets association pair
As set.
FirstMap: dividing subset finds out the frequency of each item collection according to Apriori algorithm, and exports the sample
Frequent item set.
FirstReduce: the Local frequent itemset of all map task outputs summarizes, and exports global candidate frequent item set.
SecondMap: the frequency of occurrence of each candidate frequent item set is calculated, output candidate is with it in the map task
In support.
SecondReduce: each candidate's frequent item set is added, as a result global support, is supported if support is greater than
Threshold value then retains, and otherwise rejects.
According to the embodiment of the present disclosure, the calculation method of support, confidence level and promotion degree in the S2.3 are as follows:
According to the probability that item collection X in object set occurs simultaneously in entire set N, item collection X support is calculated, formula is such as
Under:
S (X)=σ (X)/N, N value are the positive integer greater than 0；
According to occurring the probability of item collection Y in object set in item collection X, the confidence level of item collection X, formula are calculated are as follows:
C (X → Y)=σ (X ∪ Y)/σ (X),
Item collection X confidence level is known as item collection X promotion degree divided by item collection Y support, and formula is as follows:
l(X → Y) = c(X → Y)/s(Y)。
According to the embodiment of the present disclosure, support, confidence level, promotion angle value are compared with rule threshold in the S2.4
Condition are as follows:
If item collection support is greater than 0.17, confidence level is greater than 0.68, and it is effective item collection that promotion degree, which is greater than 3, is otherwise void item
Collection, crops invalid item collection.
Another object of the present invention provides the system of above-mentioned settlement of insurance claim data analysing method, the settlement of insurance claim data point
Analysis system: Access Management, source data management module, source data import modul, association analysis Reports module and customized report
Table module；
Wherein, the Access Management accesses validity period for accessing key providing management for user's access platform application
Management；
The source data import modul is that insurance data is imported to processing entrance, and module externally provides data and imports integrated interface,
Access security management and control, access authentication processing；
The source data management module imports self-defined report module for source data to be established pipeline,
The self-defined report module, after the source data format description that data providing provides, according to format labeled data word
Duan Hanyi establishes big data platform data list structure, setting source data field and big data platform literary name section mapping relations, field
Type set, data format and dividing method；
The association analysis Reports module is finally calculated for generating correlation rule item collection table according to calculation and analysis methods
Final analysis data sheet, then final analysis data sheet is sent to self-defined report module.
According to the embodiment of the present disclosure, the system also includes: statement management module, dispatching management module and report push mould
Block；
The statement management module, for the report browsing user right distribution of self-defined report module transmission, report will to be received
The generation of table, filing setting；
The dispatching management module, for calling the executing each module of the task by the dispatching cycle of setting, monitor task was executed
Journey, task abnormity processing, tactical management dispatching cycle, mainly to source data periodic synchronous, property reporting period is generated, and report is raw
Message informing after；
The report pushing module is to notify to need to browse in a manner of short message to use that need to share the short chained address of chart generation
Family, user receive and click short chained address browsing report data after short message, and there are password authentifications when browsing, read timeliness setting
Processing function.
The beneficial effects of the present invention are: due to the adoption of the above technical scheme, the present invention is based on big data technology analysis system,
Algorithm is dug according to onboard data, the personnel that need to be only familiar with business can do business diagnosis, and the business that reduces understands communication waste, system
Can be to the support of mass data, efficient analysis speed, settlement of insurance claim data analysing method and be that customized analysis indexes show
System.This method analyzes each main body different periods operational indicator and multiagent incidence relation situation of change, effectively helps to insure public affairs
Settlement of insurance claim control is taken charge of, Management plan is adjusted, helps the planning of insurance company's management strategy.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of settlement of insurance claim data analysing method of the present invention.
Fig. 2 is a kind of logic diagram of settlement of insurance claim data analysis system of the present invention.
Specific embodiment
Technical solution is further described below in conjunction with attached drawing in the embodiment of the present invention.
As shown in Figure 1, a kind of settlement of insurance claim data analysing method of the present invention, method includes the following steps:
S1 obtains settlement of insurance claim data, defeated according to relation analysis model to Claims Resolution data according to relation analysis model input requirements
Enter requirement to be handled, obtains object set；
S1 is obtained object set and is input in relation analysis model by S2, and relation analysis model carries out the object set of input
Analytical calculation, and obtained according to rule threshold screening and meet affiliated partner set；
S3 carries out quantitative analysis to affiliated partner set is met, obtains correlation rule item collection table.
According to the embodiment of the present disclosure, the Claims Resolution data in the S1 are that vehicle damages during vehicle traffic accident settlement of insurance claim
Data of settling a claim and people hurt Claims Resolution data；Wherein, the vehicle damage Claims Resolution data include information of vehicles, information of reporting a case to the security authorities, dam site investigation letter
Breath, setting loss working hour information, setting loss accessory information, setting loss person's information, repair shop's information and policy information；The people hurts Claims Resolution data
Include the wounded's information, household register information, job information, information for hospital, treatment operation information, law court's information, disability authentication information, rule
Teacher's information and careful certainly information.
According to the embodiment of the present disclosure, in the S2 relation analysis model to input object set carry out analytical calculation the step of
Are as follows:
All Claims Resolution data in input object set are carried out division processing using SON algorithm by S2.1, are divided into p file
Block, the value range of p are the positive integer greater than 0；
S2.2 executes Apriori algorithm using each blocks of files divided through S2.1 as a sample, in one or more texts
The frequent item set being selected in part block is collected as candidate frequent item set；
The candidate frequent item set that S2.3 obtains S2.2 merges to obtain final frequent item set, calculates the support of each frequent item set
Degree, confidence level and promotion degree；
Obtained support, confidence level, promotion angle value are compared by S2.4 with preset threshold value, and screening, which obtains, meets association pair
As set.
According to the embodiment of the present disclosure, the calculation method of support, confidence level and promotion degree in the S2.3 are as follows:
According to item collection X in object set in entire set N, in appearance simultaneously probability, calculate item collection X support, formula is such as
Under: the value range of s (X)=σ (X)/N, N are the positive integer greater than 0.
According to occurring the probability of item collection Y in object set in item collection X, the confidence of item collection X, formula are calculated are as follows:
C (X → Y)=σ (X ∪ Y)/σ (X),
Item collection X confidence level is known as item collection X promotion degree divided by item collection Y support, and formula is as follows:
l(X → Y) = c(X → Y)/s(Y)。
According to the embodiment of the present disclosure, support, confidence level, promotion angle value are compared with preset threshold value in the S2.4
Compared with condition are as follows:
If item collection support is greater than 0.17, confidence level is greater than 0.68, otherwise it is invalid item collection that it is effective item collection that promotion degree, which is greater than 3,
Crop invalid item collection.
It is illustrated in figure 2 a kind of settlement of insurance claim data analysis system of the present invention, the settlement of insurance claim data analysis system packet
Include Access Management, source data management module, source data import modul, association analysis Reports module and self-defined report mould
Block；
Wherein, the Access Management accesses validity period for accessing key providing management for user's access platform application
Management；
The source data import modul is that insurance data is imported to processing entrance, and module externally provides data and imports integrated interface,
Access security management and control, access authentication processing；
The source data management module imports self-defined report module for source data to be established pipeline,
The self-defined report module, after the source data format description that data providing provides, according to format labeled data word
Duan Hanyi establishes big data platform data list structure, setting source data field and big data platform literary name section mapping relations, field
Type set, data format and dividing method；
The association analysis Reports module is finally calculated for generating correlation rule item collection table according to calculation and analysis methods
Final analysis data sheet, then final analysis data sheet is sent to self-defined report module.
According to the embodiment of the present disclosure, the system also includes: statement management module, dispatching management module and report push mould
Block；
The statement management module, for the report browsing user right distribution of self-defined report module transmission, report will to be received
The generation of table, filing setting；
The dispatching management module, for calling the executing each module of the task by the dispatching cycle of setting, monitor task was executed
Journey, task abnormity processing, tactical management dispatching cycle, mainly to source data periodic synchronous, property reporting period is generated, and report is raw
Message informing after；
The report pushing module is to notify to need to browse in a manner of short message to use that need to share the short chained address of chart generation
Family, user receive and click short chained address browsing report data after short message, and there are password authentifications when browsing, read timeliness setting
Processing function.
Embodiment: when settlement of insurance claim data need to access big data platform, third party access side needs to register in platform
User account number, and apply accessing key, after platform granting accesses key, the integrated interface that system provides uses Restful API,
Access side needs to be integrated according to API set at method, and access-in management platform manages insertion authority, access frequency control, access
Key providing etc.；
The source data management module, when receiving third party's data, the data that need third party's primary insurance to settle a claim are carried out
Formatting is stored in platform, and the Suresh Kumar of third party insurance Claims Resolution data is doomed that platform is needed to support multi-format data, and
Data are parsed, parsing data are mapped in Hive table structure corresponding field, source data management for source field with
The effect of Hive table aiming field mapping management will be set in mapping to field type, length, for call parameters such as skies
It sets, system data receiving interface, which adopts suitable configurator mode and makes view by oneself, receives data format, supports XML, JSON, provides
Restful api interface receives data and formats into stream text Json format, is formatted into Json data
Hive list structured data simultaneously saves, basis of formation database.
The source data import modul, for receiving user's primary insurance Claims Resolution data, at settlement of insurance claim data fault-tolerant
Reason, record data receiver handles log, while carrying out current limliting processing to user's settlement of insurance claim data, to prevent big data quantity and height
Concurrently to systematic influence, experiencing on other users influences, and imports and is cached using kafka message queue, when data import shadow
It rings to when user's calculating experience, information rate will be limited, attenuating reports an error, and effectively promotes data and imports chance of success, attenuating
Settlement of insurance claim data error rate.
The self-defined report module according to needed for analysis indexes, is needed to basic settlement of insurance claim number in basic data
According to being processed；
The association analysis Reports module is finally calculated final analysis data, settles a claim in vehicle insurance according to calculation and analysis methods
In, relating generally to object is, car owner, repair shop, setting loss person, and there are in the case of casualties, there are the wounded, hospital, rule institutes, method
Institute's object carries out depth excavation to more preferably analyze incidence relation between each object, Che Sun object repair shop, setting loss person,
As object group, people hurts the hospital of case, the wounded, identifies that institute, rule institute, lawyer, law court's object group are associated by vehicle, driver
Analysis.
Specific association analysis method is: the support by going to calculate item collection appearance in Claims Resolution set of data objects,
Formula is s (X)=σ (X)/N, and the probability for being expressed as item collection X in entire set N while occurring, referred to as item collection X is supported
Degree；
According to occurring the probability of item collection Y in object set in item collection X, formula is c (X → Y)=σ (X ∪ Y)/σ (X), is claimed
For the confidence level of item collection X；
Item collection X confidence level calculates item collection X promotion degree divided by item collection Y support, and formula is l (X → Y)=c (X → Y)/s
(Y)。
Rule of judgment are as follows: item collection support is greater than 0.17, and confidence level is greater than 0.68, and it is effective item collection that promotion degree, which is greater than 3, no
It is then invalid item collection, crops invalid item collection, quantitative analysis is carried out to effective item collection, obtains correlation rule item collection table.
The statement management module is managed to report has been generated, and is carried out update etc. manually to abnormal report, is checked report
Table Update log, calculating logic changes history, convenient to track report change procedure.
The report pushing module carries out outer net browsing management to report, generates easily accessible short link, sends browsing
Browse right and browsing prescriptive jurisdiction function, record access log is arranged in short message.
It settles a claim described in method data object, is to repair accessory information recorded in the process of changing, work in Claims Resolution vehicle damage case
When information or people hurt the wounded recorded in case, hospital, law court, lawyer, medication, operation information.
Wherein settlement of insurance claim data analysis system, it is described to include: Access Management, source data management module, source data
Import modul, self-defined report module, statement management module, dispatching management module, report pushing module.Complete insurance multi-format
Data are synchronized in big data platform, support that real-time online is synchronous, offline batch synchronization mode, system carries out grid to data
Change, establishes data warehouse by theme, the data cube of dimension, data analyst refers to according to source data information according to report
Mark demand, is processed using source data, and layered shaping establishes Data Mart；Entire report making is completed using Reports module to generate
Presentation process, and report can be shared by short message to related personnel's browsing, audit function.
System supports the analysis of carrying big data quantity, computing function, arranges data object calibration and excavates, obtains corresponding knot
Fruit, and various display diagrams are flexibly used, allow data analyst very clear can understand data meaning and data trend.
Access Management described in system is user's access platform application, accesses key providing management, accesses validity period
Management, cut-in method call URL to log in, and access data format registration management, and user's access is safe and efficient, is reliably principle,
In access, application API needs to authenticate, information rate control, data buffer storage processing, abnormal notifier processes occurs in data,
It is external user access entrance administrative center, user requests response feedback, all by access-in management definition process.
Source data management module described in system is that source data establishes pipeline importing data field management definition module,
After the source data format description that data providing provides, according to format labeled data field meanings, big data platform data are established
Table structure, setting source data field and big data platform literary name section mapping relations, field type setting, data format and segmentation side
Method.
System supports insurance industry multi-service scene, by all kinds of means data, Data Format fields be all it is different, XML, TXT,
JSON, CSV are likely to exist, and the big compatible multi-format of number platform, adaptation multi-data source imports, and allow external data by two-dimensional table
Examination storage, convenient for subsequent data inquiry, filtering and later period layered shaping.
Source data import modul described in system is that insurance data imports processing entrance, and module externally provides data importing
Integrated interface accesses security management and control, and third party's interface configuration processing is called in access authentication processing, and platform is supported at periodic scheduling
It manages source data to import, temporally cycle set increases field, and when reaching scheduling specified time, system can actively initiate data importing
External system data, are imported into platform by processing, and update that follow-up data calculates and report data updates sequence of maneuvers, allow report
Data update in time, lower manual intervention, improve system intelligent, enhance user experience.
Self-defined report module described in system is report-creating module, data, report generation preview function, Yong Huke
To analyze associated data by the association analysis method according to source data, while carrying out online table data and merging, be associated with, go
Weight function calculation, generates the snowflake graphic data cube for pressing dimension, on data cube, sets data presentation mode,
Can support various data drawing list pattern, can by broken line, column, radar, cheese, figure present, the backtracking of report source.
Statement management module described in system, for report browsing user right distribution, report generation period, filing processing
Setting.
Dispatching management module described in system is handled, scheduling process for each module period generation, dispatches abnormality processing,
Scheduling strategy management, synchronous to source data periodic scheduling, property reporting period generates, message informing after report generation.
Report pushing module described in system is to notify to need in a manner of short message that need to share the short chained address of chart generation
Browsing user, user, which receives, to be clicked short chained address mode after short message and browses report data, there are password authentification when browsing,
It reads timeliness and processing function is set.
The above content is only the design example and explanation to settlement of insurance claim data analysing method and system, affiliated sheet
Those skilled in the art make various modifications or additions to the described embodiments or by a similar method
Substitution, as long as it does not deviate from the concept of invention or beyond the scope defined by this claim, should belong to guarantor of the invention
Protect range.
Claims (7)
1. a kind of settlement of insurance claim data analysing method, which is characterized in that method includes the following steps:
S1, access platform obtain settlement of insurance claim data, according to relation analysis model input requirements, to Claims Resolution data according to association
Analysis model input requirements are handled, and object set is obtained；
S1 is obtained object set and is input in relation analysis model by S2, and relation analysis model carries out the object set of input
Analytical calculation, and obtained according to rule threshold screening and meet affiliated partner set；
S3 carries out quantitative analysis to affiliated partner set is met, obtains correlation rule item collection table.
2. settlement of insurance claim data analysing method according to claim 1, the Claims Resolution data in the S1 are in vehicular traffic
Vehicle damage Claims Resolution data and people hurt Claims Resolution data during accident insurance Claims Resolution；Wherein, the vehicle damage Claims Resolution data are believed comprising vehicle
It ceases, information of reporting a case to the security authorities, dam site investigation information, setting loss working hour information, setting loss accessory information, setting loss person's information, repair shop's information and guarantor
Single information；The people hurt Claims Resolution data include the wounded's information, household register information, job information, information for hospital, treatment operation information,
Law court's information, disability authentication information, lawyer's information and careful certainly information.
3. settlement of insurance claim data analysing method according to claim 1, which is characterized in that relation analysis model in the S2
The step of analytical calculation is carried out to input object set are as follows:
All Claims Resolution data in input object set are carried out division processing using SON algorithm by S2.1, are divided into p file
Block, each blocks of files are 1/p, and p value is the positive integer greater than 0；
S2.2 executes Apriori algorithm using each blocks of files 1/p divided through S2.1 as a sample, at one or more
The frequent item set being selected in a blocks of files is collected as candidate frequent item set；
The candidate frequent item set that S2.3 obtains S2.2 merges to obtain final frequent item set, calculates the support of each frequent item set
Degree, confidence level and promotion degree；
Obtained support, confidence level, promotion angle value are compared by S2.4 with preset threshold value, and screening, which obtains, meets association pair
As set.
4. the settlement of insurance claim data analysing method according to claim 3, which is characterized in that the branch in the S2.3
The calculation method of degree of holding, confidence level and promotion degree are as follows:
According to the probability that item collection X in object set occurs simultaneously in entire set N, item collection X support is calculated, formula is such as
Under:
S (X)=σ (X)/N, N value are the positive integer greater than 0；
According to occurring the probability of item collection Y in object set in item collection X, the confidence level of item collection X, formula are calculated are as follows:
C (X → Y)=σ (X ∪ Y)/σ (X),
Item collection X confidence level is known as item collection X promotion degree divided by item collection Y support, and formula is as follows:
L (X → Y)=c (X → Y)/s (Y).
5. the settlement of insurance claim data analysing method according to claim 3, which is characterized in that will branch in the S2.4
Degree of holding, confidence level promote the condition that angle value is compared with rule threshold are as follows:
If item collection support is greater than 0.17, confidence level is greater than 0.68, and it is effective item collection that promotion degree, which is greater than 3, is otherwise void item
Collection, crops invalid item collection.
6. a kind of settlement of insurance claim data analysis system, which is characterized in that the settlement of insurance claim data analysis system: access-in management mould
Block, source data management module, source data import modul, association analysis Reports module and self-defined report module；
Wherein, the Access Management for that will receive the application of user's access platform, and accesses key to user and sends out
It puts management and access validity period is managed；
The source data import modul is the insurance data access platform being analysed to according to accessing user, and to access platform
Processing entrance is imported, module externally provides data and imports integrated interface, accesses security management and control, access authentication processing；
The source data management module imports self-defined report mould for source data to be established the insurance data that pipeline will acquire
Block,
The self-defined report module marks number according to format after the source data format description for obtaining data providing offer
According to field meanings, big data platform data list structure, setting source data field and big data platform literary name section mapping relations are established,
Field type setting, data format and dividing method；
The association analysis Reports module is finally calculated for generating correlation rule item collection table according to calculation and analysis methods
Final analysis data sheet, then final analysis data sheet is sent to self-defined report module.
7. system according to claim 6, which is characterized in that the system also includes: statement management module, management and running
Module and report pushing module；
The statement management module, for the report browsing user right distribution of self-defined report module transmission, report will to be received
The generation of table, filing setting；
The dispatching management module, for calling the executing each module of the task by the dispatching cycle of setting, monitor task was executed
Journey, task abnormity processing, tactical management dispatching cycle, mainly to source data periodic synchronous, property reporting period is generated, and report is raw
Message informing after；
The report pushing module is to notify to need to browse in a manner of short message to use that need to share the short chained address of chart generation
Family, user receive and click short chained address browsing report data after short message, and there are password authentifications when browsing, read timeliness setting
Processing function.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110942319A (en) * | 2019-11-20 | 2020-03-31 | 万力轮胎股份有限公司 | Method and device for modeling analysis and prediction of product claim data |
| CN111461902A (en) * | 2020-03-31 | 2020-07-28 | 泰康保险集团股份有限公司 | Premium processing method, device, equipment and computer readable storage medium |
| CN112395277A (en) * | 2020-12-09 | 2021-02-23 | 招商华软信息有限公司 | Vehicle information screening method, device, equipment and storage medium |
| CN114049227A (en) * | 2021-11-09 | 2022-02-15 | 北京优全智汇信息技术有限公司 | On-line settlement system and settlement method for insurance claims |
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