CN109919783A - Risk Identification Method, device, equipment and the storage medium of vehicle insurance Claims Resolution case - Google Patents
Risk Identification Method, device, equipment and the storage medium of vehicle insurance Claims Resolution case Download PDFInfo
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- CN109919783A CN109919783A CN201910100657.8A CN201910100657A CN109919783A CN 109919783 A CN109919783 A CN 109919783A CN 201910100657 A CN201910100657 A CN 201910100657A CN 109919783 A CN109919783 A CN 109919783A
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Abstract
The embodiment of the invention provides Risk Identification Method, device, equipment and the storage mediums of a kind of vehicle insurance Claims Resolution case, this method comprises: obtaining the case information of history vehicle insurance Claims Resolution case；Data analysis is carried out to the case information of history vehicle insurance Claims Resolution case, the corresponding characteristic information of case to extract different risk classifications；Anti- fraud model training is carried out according to the corresponding characteristic information of the case of different risk classifications；The anti-fraud model obtained according to training carries out risk classifications identification to vehicle insurance to be identified Claims Resolution case.The embodiment of the present invention solve the problems, such as vehicle insurance Claims Resolution high risk, it is counter cheat high cost, poor efficiency, effectively reduce because setting loss fault the bring amount of money leak.
Description
Technical field
The present invention relates to big data analysis technical fields, specifically, the present invention relates to a kind of wind of vehicle insurance Claims Resolution case
Dangerous recognition methods, device, equipment and storage medium.
Background technique
With the development of insurance industry, all kinds of insurance industries are grown rapidly, especially vehicle insurance industry.Vehicle insurance Claims Resolution needs elder generation
It determines vehicle model, and then determines the price of respective accessory according to vehicle model, during completing setting loss, be frequently present of some take advantage of
Swindleness behavior or leakage behavior.Wherein, fraud i.e. this case itself is not belonging to the behavior that insurance responsibility but gains Claims Resolution by cheating,
Such as a user reports a case to the security authorities, but it is last by verifying, it is that the traffic accident that reporter oneself forges is existing that this traffic accident, which occurs, for discovery
, here it is frauds.Leakage behavior refers to that this traffic accident is true traffic accident, but reporter does not report true vehicle
Damaed cordition, such as vehicle damaed cordition is the damage of a spare and accessory parts, Ru Qiangang, it is only a little scratched in fact, can
With maintenance, but user or the people of repair shop have to change one it is new, here it is leakage behaviors.
Existing settlement of insurance claim industry mostly uses greatly the mode of manual research to go to evade fraud, risk of leakage, but artificial
Research cost is high, low efficiency, judge by accident, phenomenon of failing to judge it is more, identify not accurate enough.
Summary of the invention
The present invention provides Risk Identification Method, device, electronic equipment and the storage medium of a kind of vehicle insurance Claims Resolution case, solutions
Vehicle insurance of having determined Claims Resolution high risk, the anti-problem for cheating high cost, poor efficiency.The technical solution is as follows:
In a first aspect, the present invention provides a kind of Risk Identification Methods of vehicle insurance Claims Resolution case, this method comprises:
Obtain the case information of history vehicle insurance Claims Resolution case；
Data analysis is carried out to the case information of history vehicle insurance Claims Resolution case, to extract the case of different risk classifications
Corresponding characteristic information；
Anti- fraud model training is carried out according to the corresponding characteristic information of the case of different risk classifications；
The anti-fraud model obtained according to training carries out risk classifications identification to vehicle insurance to be identified Claims Resolution case.
Optionally, risk is carried out to vehicle insurance to be identified Claims Resolution case in the anti-fraud model obtained according to training
After type identification, comprising:
Value-at-risk assessment is carried out to the risk classifications identified according to preset risk assessment rule.
Optionally, the anti-fraud model obtained according to training carries out risk class to vehicle insurance to be identified Claims Resolution case
Type identification, comprising:
Establish the data link with settlement of insurance claim system；
Meet preset fraud in the vehicle insurance Claims Resolution case uploaded by the data link to the settlement of insurance claim system
The vehicle insurance Claims Resolution case of anticipation condition is identified；
Risk classifications identification is carried out to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model.
Optionally, the corresponding characteristic information of the case according to different risk classifications carries out anti-fraud model training, packet
It includes:
Classified using the corresponding characteristic information of case of the branch mailbox algorithm to each risk classifications；
Determine the information content of the corresponding each characteristic information classification of the case of each risk classifications；
According to the information content of the corresponding each characteristic information classification of the case of each risk classifications, decision Tree algorithms structure is utilized
Build the anti-fraud model.
Optionally, the method also includes:
The quantity of the vehicle insurance Claims Resolution case of each risk classifications is counted, and based on the vehicle insurance of each risk classifications Claims Resolution case
The each risk classifications of quantity statistics vehicle insurance Claims Resolution case probability of happening.
Second aspect, the present invention provides a kind of risk identification device of vehicle insurance Claims Resolution case, which includes:
Data acquisition module, for obtaining the case information of history vehicle insurance Claims Resolution case；
Characteristic extracting module carries out data analysis for the case information to history vehicle insurance Claims Resolution case, to extract
The corresponding characteristic information of the case of different risk classifications；
Model training module carries out anti-fraud model instruction for the corresponding characteristic information of case according to different risk classifications
Practice；
Risk identification module, the anti-fraud model for being obtained according to training carry out vehicle insurance to be identified Claims Resolution case
Risk classifications identification.
Optionally, the risk identification module, comprising:
Configuration unit, for establishing and the data link of settlement of insurance claim system；
Unit is identified, is accorded in the vehicle insurance Claims Resolution case for being uploaded by the data link to the settlement of insurance claim system
The vehicle insurance Claims Resolution case for closing preset fraud anticipation condition is identified；
Recognition unit, for carrying out risk class to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model
Type identification.
Optionally, the model training module, comprising:
Taxon, for being classified using branch mailbox algorithm to the corresponding characteristic information of the case of each risk classifications；
Statistic unit, the information content of the corresponding each characteristic information classification of case for determining each risk classifications；
Model construction unit, the information for the corresponding each characteristic information classification of the case according to each risk classifications
Amount constructs the anti-fraud model using decision Tree algorithms.
The third aspect, the present invention provides a kind of electronic equipment, which includes:
One or more processors；
Memory；
One or more application program, wherein one or more of application programs are stored in the memory and quilt
It is configured to be executed by one or more of processors, one or more of programs are configured to: executing above-mentioned vehicle insurance Claims Resolution
The Risk Identification Method of case.
Fourth aspect, the present invention provides a kind of storage mediums, are stored thereon with computer program, and the program is by processor
The Risk Identification Method of above-mentioned vehicle insurance Claims Resolution case is realized when execution.
Technical solution provided in an embodiment of the present invention has the benefit that through the case to history vehicle insurance Claims Resolution case
Part information carries out data analysis, the corresponding characteristic information of case of different risk classifications is extracted, then according to different risk classifications
The corresponding characteristic information of case carry out anti-fraud model training, to be managed using the anti-fraud model trained vehicle insurance to be identified
It pays for case and carries out risk classifications identification.The embodiment of the present invention solves vehicle insurance Claims Resolution high risk, the anti-high cost, inefficient of cheating
Problem is effectively reduced because of setting loss fault bring amount of money leakage.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, institute in being described below to the embodiment of the present invention
Attached drawing to be used is needed to be briefly described.
Fig. 1 is a kind of flow diagram of the Risk Identification Method of vehicle insurance Claims Resolution case provided in an embodiment of the present invention；
Fig. 2 is the specific of step S13 in a kind of Risk Identification Method of vehicle insurance Claims Resolution case provided in an embodiment of the present invention
Flow diagram；
Fig. 3 is the specific of step S14 in a kind of Risk Identification Method of vehicle insurance Claims Resolution case provided in an embodiment of the present invention
Flow diagram；
Fig. 4 is a kind of structural schematic diagram of the risk identification device of vehicle insurance Claims Resolution case provided in an embodiment of the present invention；
Fig. 5 is the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, component and/or their group.It should be understood that when we claim member
Part is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be
Intermediary element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or wirelessly coupling.It is used herein to arrange
Diction "and/or" includes one or more associated wholes for listing item or any cell and all combinations.
How technical solution of the present invention and technical solution of the present invention are solved with specifically embodiment below above-mentioned
Technical problem is described in detail.These specific embodiments can be combined with each other below, for the same or similar concept
Or process may repeat no more in certain embodiments.Below in conjunction with attached drawing, the embodiment of the present invention is described.
Embodiment one
The embodiment of the invention provides a kind of Risk Identification Methods of vehicle insurance Claims Resolution case by the application, as shown in Figure 1, the party
Method includes: step S101, step S102, step S103 and step S103S104.
Step S101, the case information of history vehicle insurance Claims Resolution case is obtained.
Specifically, the data source of history vehicle insurance Claims Resolution case can be foundation and have the insurance company of cooperative relationship in the past
The vehicle insurance Claims Resolution case data of certain time period.
Wherein, case packet includes the dangerous time, and reporter reports a case to the security authorities the time, reason and case risk classifications etc. occurs.
If not fraud case, the amount of money for the money that final insurance company compensates can also be provided, the zero of vehicle maintenance or replacement
The information such as accessory inventory and price are specified.
Step S102, data analysis is carried out to the case information of history vehicle insurance Claims Resolution case, to extract different risks
The corresponding characteristic information of the case of type.
Wherein, risk classifications mainly include fraud or leakage.
In practical applications, the vehicle insurance Claims Resolution case of same risk type usually has the same or similar feature.Such as
One fraud rule may is that 1, female driver；2, the time of being in danger is 12 points at night；3, it has just taken driver's license second day and has just been in danger.
In the present embodiment, by history vehicle insurance settle a claim case case information carry out data analysis, to those by
Insurance company is considered that the case that fraud perhaps leaks carries out analysis comparison and looks at which these frauds or leakage case have be total to
Same feature, then extracts.
Step S103, anti-fraud model training is carried out according to the corresponding characteristic information of the case of different risk classifications.
Step S104, the anti-fraud model obtained according to training carries out risk classifications to vehicle insurance to be identified Claims Resolution case
Identification.
The Risk Identification Method of vehicle insurance Claims Resolution case provided in an embodiment of the present invention, by history vehicle insurance Claims Resolution case
Case information carries out data analysis, the corresponding characteristic information of case of different risk classifications is extracted, then according to different risk classes
The corresponding characteristic information of the case of type carries out anti-fraud model training, to utilize the anti-fraud model trained to vehicle insurance to be identified
Case of settling a claim carries out risk classifications identification.The embodiment of the present invention solves vehicle insurance Claims Resolution high risk, counter cheats high cost, poor efficiency
The problem of, it effectively reduces because of setting loss fault bring amount of money leakage.
In a specific embodiment, in the anti-fraud model obtained according to training to vehicle insurance to be identified Claims Resolution case
After progress risk classifications identification, further includes: carry out risk to the risk classifications identified according to preset risk assessment rule
Value assessment.
In the present embodiment, risk is carried out to vehicle insurance to be identified Claims Resolution case in the anti-fraud model obtained according to training
After type identification, also value-at-risk assessment can be carried out to the risk classifications identified according to preset risk assessment rule.For example,
100 points be full marks value-at-risk, value-at-risk it is high be exactly high risk, value-at-risk it is low be exactly low-risk.The present invention passes through continuous
With data training pattern, allowing model which case is recognized accurately is high risk, which is low-risk.
In a specific embodiment, as shown in Fig. 2, step S103, optionally, the case according to different risk classifications
The corresponding characteristic information of part carries out anti-fraud model training, specifically includes the following steps:
Step S1031, classified using the corresponding characteristic information of case of the branch mailbox algorithm to each risk classifications；
Step S1032, the information content IV of the corresponding each characteristic information classification of the case of each risk classifications is determined.
Wherein, information content IV is for measuring this feature information category to the predictive ability of a certain risk classifications.Wherein, information
The corresponding relationship for measuring IV and predictive ability is as shown in table 1:
Table 1
| IV<0.02 | This feature is not previously predicted ability substantially |
| 0.02≤IV < 0.1 | This feature predictive ability is weaker |
| 0.1≤IV < 0.3 | This feature predictive ability is medium |
| 0.3≤IV < 0.5 | This feature predictive ability is stronger |
| 0.5<IV | This feature predictive ability is excessively good, is not suitable for building Rating Model |
Step S1033, according to the information content of the corresponding each characteristic information classification of the case of each risk classifications, using certainly
Plan tree algorithm constructs the anti-fraud model.
In a specific embodiment, as shown in figure 3, step S104, the anti-fraud model pair obtained according to training
Vehicle insurance Claims Resolution case to be identified carries out risk classifications identification, specifically includes the following steps:
Step S1041, the data link of foundation and settlement of insurance claim system；
Step S1042, meet in the vehicle insurance Claims Resolution case uploaded by the data link to the settlement of insurance claim system
The vehicle insurance Claims Resolution case of preset fraud anticipation condition is identified；
Step S1043, risk classifications are carried out to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model
Identification.
The embodiment of the present invention is docked after establishing anti-fraud model with the realization of the settlement of insurance claim system of insurance company, right
The vehicle insurance Claims Resolution case for meeting preset fraud anticipation condition after having connect in their case can trigger anti-fraud model, automatic real
Now the risk classifications of vehicle insurance to be identified Claims Resolution case are identified.
In a specific embodiment, the embodiment of the invention provides a kind of vehicle insurance Claims Resolution case Risk Identification Method,
It is further comprising the steps of:
Count the quantity of the vehicle insurance Claims Resolution case of each risk classifications；
And the vehicle insurance Claims Resolution case of each risk classifications of quantity statistics based on the vehicle insurance of each risk classifications Claims Resolution case
Probability of happening.
In the embodiment of the present invention, after the automatic risk classifications identification for realizing case of settling a claim to vehicle insurance to be identified, pass through
These are triggered into the case number of packages of anti-fraud model divided by total case number of packages, i.e., the vehicle insurance Claims Resolution case of statistics available each risk classifications
Probability of happening.
The Risk Identification Method of vehicle insurance Claims Resolution case provided by the invention can train anti-fraud mould according to historical data
Type, and be automatically divided into case using anti-fraud model, leakage class, closes the various classification such as rule class at fraud class, and to each classification
Probabilistic forecasting is carried out, case is promoted and shifts to an earlier date cognitive ability.
Embodiment two
The embodiment of the invention provides a kind of risk identification devices of vehicle insurance Claims Resolution case by the application, as shown in figure 4, the vehicle
The risk identification device of danger Claims Resolution case may include: data acquisition module 401, characteristic extracting module 402, model training module
403 and risk identification module 404, wherein
Data acquisition module 401, for obtaining the case information of history vehicle insurance Claims Resolution case；
Characteristic extracting module 402 carries out data analysis for the case information to history vehicle insurance Claims Resolution case, to mention
Take the corresponding characteristic information of the case of different risk classifications；
Model training module 403 carries out anti-fraud mould for the corresponding characteristic information of case according to different risk classifications
Type training；
Risk identification module 404, the anti-fraud model for being obtained according to training is to vehicle insurance to be identified Claims Resolution case
Carry out risk classifications identification.
In a specific embodiment, the model training module 403, specifically includes with lower unit:
Taxon, for being classified using branch mailbox algorithm to the corresponding characteristic information of the case of each risk classifications；
Statistic unit, the information content of the corresponding each characteristic information classification of case for determining each risk classifications；
Model construction unit, the information for the corresponding each characteristic information classification of the case according to each risk classifications
Amount constructs the anti-fraud model using decision Tree algorithms.
In a specific embodiment, the risk identification module 404, specifically includes with lower unit:
Configuration unit, for establishing and the data link of settlement of insurance claim system；
Unit is identified, is accorded in the vehicle insurance Claims Resolution case for being uploaded by the data link to the settlement of insurance claim system
The vehicle insurance Claims Resolution case for closing preset fraud anticipation condition is identified；
Recognition unit, for carrying out risk class to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model
Type identification.
In a specific embodiment, the risk identification module 404, in the anti-fraud obtained according to training
After model carries out risk classifications identification to vehicle insurance to be identified Claims Resolution case, it is also used to according to preset risk assessment rule to knowledge
Not Chu risk classifications carry out value-at-risk assessment.
In a specific embodiment, the risk identification device of the vehicle insurance Claims Resolution case of the present embodiment further includes attached not shown
Statistical module out, the statistical module, the quantity of the vehicle insurance Claims Resolution case for counting each risk classifications, and it is based on each wind
The probability of happening of the vehicle insurance Claims Resolution case of each risk classifications of quantity statistics of the vehicle insurance Claims Resolution case of dangerous type.
The reason of vehicle insurance shown in the embodiment of the present invention one can be performed in the risk identification device of the vehicle insurance Claims Resolution case of the present embodiment
The Risk Identification Method of case is paid for, realization principle is similar, and details are not described herein again.
Embodiment three
The embodiment of the invention provides a kind of electronic equipment, as shown in figure 5, electronic equipment shown in fig. 5 500 includes: place
Manage device 5001 and transceiver 5004.Wherein, processor 5001 is connected with transceiver 5004, is such as connected by bus 5002.It is optional
, electronic equipment 500 can also include memory 5003.It should be noted that transceiver 5004 is not limited to one in practical application
A, the structure of the electronic equipment 500 does not constitute the restriction to the embodiment of the present invention.
Processor 5001 can be CPU, general processor, DSP, ASIC, FPGA or other programmable logic device, crystalline substance
Body pipe logical device, hardware component or any combination thereof.It, which may be implemented or executes, combines described by the disclosure of invention
Various illustrative logic blocks, module and circuit.Processor 5001 is also possible to realize the combination of computing function, such as wraps
It is combined containing one or more microprocessors, DSP and the combination of microprocessor etc..
Bus 5002 may include an access, and information is transmitted between said modules.Bus 5002 can be pci bus or
Eisa bus etc..Bus 5002 can be divided into address bus, data/address bus, control bus etc..Only to be used in Fig. 5 convenient for indicating
One thick line indicates, it is not intended that an only bus or a type of bus.
Memory 5003 can be ROM or can store the other kinds of static storage device of static information and instruction, RAM
Or the other kinds of dynamic memory of information and instruction can be stored, it is also possible to EEPROM, CD-ROM or other CDs
Storage, optical disc storage (including compression optical disc, laser disc, optical disc, Digital Versatile Disc, Blu-ray Disc etc.), magnetic disk storage medium
Or other magnetic storage apparatus or can be used in carry or store have instruction or data structure form desired program generation
Code and can by any other medium of computer access, but not limited to this.
Optionally, memory 5003 is used to store the application code for executing the present invention program, and by processor 5001
It is executed to control.Processor 5001 is for executing the application code stored in memory 5003, to realize implementation shown in Fig. 4
The movement of the risk identification device for the vehicle insurance Claims Resolution case that example provides.
Technical solution provided in an embodiment of the present invention has the benefit that through the case to history vehicle insurance Claims Resolution case
Part information carries out data analysis, the corresponding characteristic information of case of different risk classifications is extracted, then according to different risk classifications
The corresponding characteristic information of case carry out anti-fraud model training, to be managed using the anti-fraud model trained vehicle insurance to be identified
It pays for case and carries out risk classifications identification.The embodiment of the present invention solves vehicle insurance Claims Resolution high risk, the anti-high cost, inefficient of cheating
Problem is effectively reduced because of setting loss fault bring amount of money leakage.
Example IV
The embodiment of the invention provides a kind of storage medium, it is stored with computer program on the storage medium, the program quilt
Method shown in embodiment one to three any embodiment of embodiment is realized when processor executes.
The embodiment of the invention provides a kind of storage mediums by the application, compared with prior art, by managing history vehicle insurance
The case information for paying for case carries out data analysis, extracts the corresponding characteristic information of case of different risk classifications, then according to not
With the case of risk classifications, corresponding characteristic information carries out anti-fraud model training, to be treated using the anti-fraud model trained
Identify that vehicle insurance Claims Resolution case carries out risk classifications identification.The embodiment of the present invention solve vehicle insurance Claims Resolution high risk, anti-fraud it is high at
Originally, inefficient problem is effectively reduced because of setting loss fault bring amount of money leakage.
The embodiment of the invention provides a kind of storage mediums to be suitable for above method embodiment.Details are not described herein.
It should be understood that although each step in the flow chart of attached drawing is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, execution sequence, which is also not necessarily, successively to be carried out, but can be with other
At least part of the sub-step or stage of step or other steps executes in turn or alternately.
It will be appreciated by those of skill in the art that although some embodiments in this include included in other embodiments
Certain features rather than other feature, but the combination of the feature of different embodiments means to be within the scope of the present invention simultaneously
And form different embodiments.For example, in the following claims, the one of any of embodiment claimed all may be used
Come in a manner of in any combination using.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of Risk Identification Method of vehicle insurance Claims Resolution case characterized by comprising
Obtain the case information of history vehicle insurance Claims Resolution case；
Data analysis is carried out to the case information of history vehicle insurance Claims Resolution case, it is corresponding with the case for extracting different risk classifications
Characteristic information；
Anti- fraud model training is carried out according to the corresponding characteristic information of the case of different risk classifications；
The anti-fraud model obtained according to training carries out risk classifications identification to vehicle insurance to be identified Claims Resolution case.
2. the method according to claim 1, wherein in the anti-fraud model pair obtained according to training
Vehicle insurance Claims Resolution case to be identified carries out after risk classifications identification, comprising:
Value-at-risk assessment is carried out to the risk classifications identified according to preset risk assessment rule.
3. the method according to claim 1, wherein the anti-fraud model obtained according to training is treated
Identify that vehicle insurance Claims Resolution case carries out risk classifications identification, comprising:
Establish the data link with settlement of insurance claim system；
Meet preset fraud in the vehicle insurance Claims Resolution case for uploading the settlement of insurance claim system by the data link to prejudge
The vehicle insurance Claims Resolution case of condition is identified；
Risk classifications identification is carried out to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model.
4. the method according to claim 1, wherein the corresponding feature of the case according to different risk classifications
Information carries out anti-fraud model training, comprising:
Classified using the corresponding characteristic information of case of the branch mailbox algorithm to each risk classifications；
Determine the information content of the corresponding each characteristic information classification of the case of each risk classifications；
According to the information content of the corresponding each characteristic information classification of the case of each risk classifications, institute is constructed using decision Tree algorithms
State anti-fraud model.
5. method according to claim 1-4, which is characterized in that the method also includes:
Count the quantity of the vehicle insurance Claims Resolution case of each risk classifications, and the number based on the vehicle insurance of each risk classifications Claims Resolution case
Amount counts the probability of happening of the vehicle insurance Claims Resolution case of each risk classifications.
6. a kind of risk identification device of vehicle insurance Claims Resolution case characterized by comprising
Data acquisition module, for obtaining the case information of history vehicle insurance Claims Resolution case；
Characteristic extracting module carries out data analysis for the case information to history vehicle insurance Claims Resolution case, to extract difference
The corresponding characteristic information of the case of risk classifications；
Model training module carries out anti-fraud model training for the corresponding characteristic information of case according to different risk classifications；
Risk identification module, the anti-fraud model for being obtained according to training carry out risk to vehicle insurance to be identified Claims Resolution case
Type identification.
7. device according to claim 6, which is characterized in that the risk identification module, comprising:
Configuration unit, for establishing and the data link of settlement of insurance claim system；
Unit is identified, is met in the vehicle insurance Claims Resolution case for being uploaded by the data link to the settlement of insurance claim system pre-
If fraud anticipation condition vehicle insurance Claims Resolution case be identified；
Recognition unit, for carrying out risk classifications knowledge to the vehicle insurance to be identified Claims Resolution case identified using the anti-fraud model
Not.
8. device according to claim 6, which is characterized in that the model training module, comprising:
Taxon, for being classified using branch mailbox algorithm to the corresponding characteristic information of the case of each risk classifications；
Statistic unit, the information content of the corresponding each characteristic information classification of case for determining each risk classifications；
Model construction unit, for the information content of the corresponding each characteristic information classification of the case according to each risk classifications, benefit
The anti-fraud model is constructed with decision Tree algorithms.
9. a kind of electronic equipment, characterized in that it comprises:
One or more processors；
Memory；
One or more application program, wherein one or more of application programs are stored in the memory and are configured
To be executed by one or more of processors, one or more of programs are configured to: executing -5 according to claim 1
The Risk Identification Method of the Claims Resolution case of vehicle insurance described in one.
10. a kind of storage medium, is stored thereon with computer program, which is characterized in that the realization when program is executed by processor
The Risk Identification Method of the described in any item vehicle insurance Claims Resolution cases of claim 1-5.
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| CN111242790A (en) * | 2020-01-02 | 2020-06-05 | 平安科技（深圳）有限公司 | Risk identification method, electronic device and storage medium |
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| CN114357225A (en) * | 2021-12-09 | 2022-04-15 | 之江实验室 | Vehicle insurance claim settlement fraud risk identification method and system based on cross-case image comparison |
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| CN116308434A (en) * | 2023-05-12 | 2023-06-23 | 杭州大鱼网络科技有限公司 | Insurance fraud identification method and system |
| CN116308434B (en) * | 2023-05-12 | 2023-08-11 | 杭州大鱼网络科技有限公司 | Insurance fraud identification method and system |
| CN117078441A (en) * | 2023-10-16 | 2023-11-17 | 之江实验室 | Method, apparatus, computer device and storage medium for identifying claims fraud |
| CN117078441B (en) * | 2023-10-16 | 2024-02-06 | 之江实验室 | Method, apparatus, computer device and storage medium for identifying claims fraud |
| CN117273963A (en) * | 2023-11-21 | 2023-12-22 | 之江实验室 | Risk identification method and device based on car insurance scene |
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