CN107818513A - Methods of risk assessment and device, storage medium, electronic equipment - Google Patents
Methods of risk assessment and device, storage medium, electronic equipment Download PDFInfo
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- CN107818513A CN107818513A CN201711190314.2A CN201711190314A CN107818513A CN 107818513 A CN107818513 A CN 107818513A CN 201711190314 A CN201711190314 A CN 201711190314A CN 107818513 A CN107818513 A CN 107818513A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The present embodiments relate to technical field of data processing, more particularly to a kind of methods of risk assessment and device, storage medium, electronic equipment.The methods of risk assessment can include：The insurance data that user is directed to default insurance kind is passed to air control model, wherein, the air control model is built-up based on big data sample；The historical data and external data of user is obtained by the air control model；The insurance data, the historical data and the external data are based on using the air control model, calculates the probability that is in danger that the user is directed to the default insurance kind.The embodiment of the present invention substantially increases efficiency and the flexibility of risk assessment, can meet that core is protected ageing, save substantial amounts of manpower and material resources cost, it is high that core protects accuracy rate, the user of high value low-risk can be filtered out, effectively intercepts adverse selection user, reduces and compensates cost.
Description
Technical field
The present embodiments relate to technical field of data processing, more particularly to a kind of methods of risk assessment and device, storage
Medium, electronic equipment.
Background technology
With the continuous social and economic development, there is a growing awareness that the importance of insurance.Insurance refer to user according to
The insurance company of damage caused by the risk that contract engagement occurs to insurance company's disbursement insurance expense, to(for) the possibility of contract engagement
The behavior for undertaking compensation insurance gold is lost, therefore, risk assessment of the insurance company to insurance business is particularly important.
At present, methods of risk assessment is mainly artificial nucleus guarantor, i.e., underwriter by the accumulation of the past business experience to
The age at family, professional risk grade, user location, accumulative risk protection amount, health inform, insure amount, insured amount etc.
Risks and assumptions combination is judged to carry out risk assessment.
As from the foregoing, on the one hand, artificial nucleus protect efficiency is low, very flexible, it is impossible to meet that core is protected ageing；The opposing party
Face, due to the professional knowledge of underwriter and the difference of business experience, core may be caused to protect the problem of accuracy rate is low.
It should be noted that information is only used for strengthening the back of the body to the embodiment of the present invention disclosed in above-mentioned background section
The understanding of scape, therefore can include not forming the information to prior art known to persons of ordinary skill in the art.
The content of the invention
The purpose of the embodiment of the present invention is to provide a kind of methods of risk assessment and device, storage medium, electronic equipment, entered
And one or more problem caused by the limitation of correlation technique and defect is at least overcome to a certain extent.
One side according to embodiments of the present invention, there is provided a kind of methods of risk assessment, including：
The insurance data that user is directed to default insurance kind is passed to air control model, wherein, the air control model is based on big number
It is built-up according to sample；
The historical data and external data of user is obtained by the air control model；
The insurance data, the historical data and the external data are based on using the air control model, calculates institute
State the probability that is in danger that user is directed to the default insurance kind.
In a kind of exemplary embodiment of the embodiment of the present invention, methods described also includes：
Risk score according to user for the user described in probability calculation that is in danger of the default insurance kind, and according to the wind
Danger scoring determination is accepted insurance mode.
In a kind of exemplary embodiment of the embodiment of the present invention, methods described also includes：Based on the big data sample
Build the air control model；Wherein, it is described to be included based on the big data sample structure air control model：
Multiple Variable Factors for the default insurance kind are determined based on the big data sample；
Based on the big data sample and the multiple Variable Factors, the weighted value of each Variable Factors is determined；
The air control model is built based on the weighted value of the multiple Variable Factors and each Variable Factors.
It is described to be determined based on the big data sample for described in a kind of exemplary embodiment of the embodiment of the present invention
Multiple Variable Factors of default insurance kind, including：
The correlation of the unitary variant factor and result of being in danger is calculated based on the big data sample,
Using a preset algorithm and the correlation, multiple Variable Factors are filtered out；
It is described to be based on the big data sample and the multiple Variable Factors, the weighted value of each Variable Factors is determined,
Including：
To be in danger result and the multiple Variable Factors execution logic regression algorithm that filters out of the big data sample,
The weighted value for each Variable Factors that calculating sifting goes out.
It is described to be based on the number of insuring using the air control model in a kind of exemplary embodiment of the embodiment of the present invention
According to, the historical data and the external data, the probability that is in danger for calculating the user for the default insurance kind includes：
Each institute is obtained in the air control model according to the insurance data, the historical data and the external data
State the weighted value of Variable Factors and determine the contribution margin of each Variable Factors；
According to the contribution margin of the weighted value of each Variable Factors and each Variable Factors and combine following formula meters
Calculate the probability that is in danger that the user is directed to the default insurance kind：
Wherein, P represents that user is directed to the probability that is in danger of the default insurance kind, ωiRepresent the weight of i-th of Variable Factors
Value, XiRepresent the contribution margin of i-th of Variable Factors, ω0Represent intercept,
It is described that the air control is built based on the big data sample in a kind of exemplary embodiment of the embodiment of the present invention
Model also includes：
Checking data sample is obtained, and the AUC of the air control model is calculated based on the checking data sample；
Judge whether the AUC meets preparatory condition, and when the AUC is unsatisfactory for default bar, based on described big
Data sample rebuilds the air control model, so that the AUC of the air control model rebuild meets default bar
Part.
In a kind of exemplary embodiment of the embodiment of the present invention, the acquisition external data includes：
The air control model includes external interface, and the air control model is obtained and the number of insuring by the external interface
According to the corresponding external data.
In a kind of exemplary embodiment of the embodiment of the present invention, the default insurance kind includes comprehensive accident insurance, described big
Data sample includes comprehensive accident insurance data sample.
In a kind of exemplary embodiment of the embodiment of the present invention, the consumer behavior that the historical data includes user is remembered
Record, membership information, the means of payment and accumulative risk are insured amount；The external data includes credit scoring, blacklist.
In a kind of exemplary embodiment of the embodiment of the present invention, the mode of accepting insurance includes：Premium is lowered to accept insurance, normally
Premium is accepted insurance and raises to accept insurance；
It is described to determine that the mode of accepting insurance includes according to the risk score：
Judge whether the risk score belongs to low-risk scoring section, risk scoring section or excessive risk scoring area
Between；
When judging that the risk score belongs to the low-risk scoring section, it is determined that the mode of accepting insurance is the downward
Premium is accepted insurance；
When judging that the risk score belongs to the risk scoring section, it is determined that the mode of accepting insurance is described normal
Accept insurance；
When judging that the risk score belongs to the excessive risk scoring section, it is determined that the mode of accepting insurance is the up-regulation
Premium is accepted insurance.
One side according to embodiments of the present invention, there is provided a kind of risk assessment device, including：
Data afferent module, the insurance data for user to be directed to default insurance kind are passed to air control model, wherein, the wind
Control model is built-up based on big data sample；
Data acquisition module, for obtaining the historical data and external data of user by the air control model；
Probability evaluation entity, for being based on the insurance data, the historical data and institute using the air control model
External data is stated, calculates the probability that is in danger that the user is directed to the default insurance kind.
One side according to embodiments of the present invention, there is provided a kind of computer-readable recording medium, be stored thereon with calculating
Machine program, the computer program realize the methods of risk assessment described in above-mentioned any one when being executed by processor.
One side according to embodiments of the present invention, there is provided a kind of electronic equipment, including：
Processor；And
Memory, for storing the executable instruction of the processor；
Wherein, the processor is configured to perform described in above-mentioned middle any one via the executable instruction is performed
Methods of risk assessment.
Methods of risk assessment and device provided in an embodiment of the present invention, storage medium, electronic equipment.The methods of risk assessment
The insurance data, the historical data and the external data are based on using the air control model, calculates user's pin
To the probability that is in danger of the default insurance kind, wherein, the air control model is built-up based on big data sample.On the one hand, it is sharp
The probability that is in danger is calculated with air control model, compared to prior art, substantially increases efficiency and the flexibility of risk assessment, Ke Yiman
Sufficient core is protected ageing, saves substantial amounts of manpower and material resources cost；On the other hand, because air control model is to be based on big data sample
Built-up, compared to prior art, core protects accuracy rate height, so as to filter out the user of high value low-risk, effectively blocks
Adverse selection user is cut, reduces and compensates cost.
It should be appreciated that the general description and following detailed description of the above are only exemplary and explanatory, not
Can the limitation present invention.
Brief description of the drawings
Its exemplary embodiment is described in detail by referring to accompanying drawing, above and other feature and advantage of the invention will become
Obtain more obvious.It should be evident that drawings in the following description are only some embodiments of the present invention, it is common for this area
For technical staff, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.Attached
In figure：
Fig. 1 is a kind of flow chart of methods of risk assessment in the embodiment of the present invention；
Fig. 2 is the flow chart of the structure air control model provided in the embodiment of the present invention；
Fig. 3 is the flow chart of the multiple Variable Factors of determination provided in the embodiment of the present invention；
Fig. 4 is a kind of block diagram of risk assessment device in the embodiment of the present invention；
Fig. 5 is the module diagram of the electronic equipment in the embodiment of the present invention.
Fig. 6 is the program product schematic diagram in example embodiment in the embodiment of the present invention.
Embodiment
Example embodiment is described more fully with referring now to accompanying drawing.However, example embodiment can be real in a variety of forms
Apply, and be not understood as limited to embodiment set forth herein；On the contrary, these embodiments are provided so that the disclosure will be comprehensively and complete
It is whole, and the design of example embodiment is comprehensively communicated to those skilled in the art.Identical reference represents in figure
Same or similar part, thus repetition thereof will be omitted.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner
In example.In the following description, there is provided many details fully understand so as to provide to embodiment of the disclosure.However,
It will be appreciated by persons skilled in the art that the technical scheme of the disclosure can be put into practice without one in the specific detail or more
It is more, or other methods, constituent element, material, device, step etc. can be used.In other cases, it is not shown in detail or describes
Known features, method, apparatus, realization, material are operated to avoid each side of the fuzzy disclosure.
Block diagram shown in accompanying drawing is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or these are realized in the module of one or more softwares hardening
A part for functional entity or functional entity, or realized in heterogeneous networks and/or processor device and/or microcontroller device
These functional entitys.
A kind of methods of risk assessment is disclosed first in the present exemplary embodiment, shown in reference picture 1, the methods of risk assessment bag
Include：
Step S110, insurance data that user is directed to default insurance kind is passed to air control model, wherein, the air control model is
It is built-up based on big data sample；
Step S120, the historical data and external data of user is obtained by the air control model；
Step S130, the insurance data, the historical data and the external number are based on using the air control model
According to, calculate the user be directed to the default insurance kind the probability that is in danger.
Methods of risk assessment in the present exemplary embodiment, on the one hand, calculate the probability that is in danger, phase using air control model
Than in prior art, substantially increasing efficiency and the flexibility of risk assessment, can meet that core is protected ageing, save substantial amounts of
Manpower and material resources cost；On the other hand, because air control model is built-up based on big data sample, compared to prior art,
It is high that core protects accuracy rate, so as to filter out the user of high value low-risk, effectively intercepts adverse selection user, reduce compensate into
This.
Below, each step in the vehicle identification method in the present exemplary embodiment will be further described.
In step s 110, the insurance data for user being directed to default insurance kind is passed to air control model, wherein, the air control mould
Type is built-up based on big data sample.
In the present example embodiment, the default insurance kind can include vehicle insurance, serious disease insurance, property insurance etc., this example
Property embodiment is not particularly limited to this.The big data sample can be the insurance data that insurance company accumulates throughout the year, and it can
With including data and the data of being in danger of not being in danger.From data dimension, the big data sample can include internal data and
External data, wherein, internal data can include identity information, risk information, behavioural information of client etc., the external data
Collage-credit data, security information data and internet data etc. can be included.It should be noted that different preset can be directed to
Insurance kind, no air control model is built by big data sample corresponding with default insurance kind.For example, protected in default insurance kind for property
When dangerous, the big data sample of structure air control model can be property insurance data over the years.Alternatively, the default insurance kind can be with
Including comprehensive accident insurance, the big data sample can include comprehensive accident insurance data sample.
Alternatively, shown in reference picture 2, the process that the air control model is built based on the big data sample is said
Bright, the process of the air control model should be built based on the big data sample to be included：
Step S210, multiple Variable Factors for the default insurance kind are determined based on the big data sample.
In the present example embodiment, multiple Variable Factors can be determined according to the different dimensions of big data sample, for example,
Can be determined according to the difference at the age of the user in big data sample, the difference of sex, difference etc. of occupation multiple variables because
Son.
Step S220, based on the big data sample and the multiple Variable Factors, the power of each Variable Factors is determined
Weight values.
In the present example embodiment, can be according to the result of being in danger of each sample data in big data sample (be in danger probability)
The weighted value of each Variable Factors is calculated by following formula.
Wherein, piRepresent the probability that is in danger of i-th of data sample in big data sample, ωiRepresent j-th Variable Factors
Weighted value, XjRepresent the contribution margin of j-th of Variable Factors, ω0Represent intercept,
It should be noted that XjMeet the X in i-th of data samplejIt is 1, X during corresponding Variable FactorsjIn i-th of number
The X is not met according to samplejIt is 0 during corresponding Variable Factors.For example, for Variable Factors female and man, the property in data sample
Not Wei female when, X corresponding to Variable Factors femalejFor 1, X corresponding to Variable Factors manjFor 0；When sex in data sample is male,
X corresponding to Variable Factors femalejFor 0, X corresponding to Variable Factors manjFor 1.
Step S230, the air control is built based on the weighted value of the multiple Variable Factors and each Variable Factors
Model.
In the present example embodiment, a wind can be built according to the weighted value of multiple Variable Factors and each Variable Factors
Table is assessed in danger, and the risk assessment table is defined as into air control model.For example, the air control model is as shown in table 1 below：
Table 1
As seen from the above table, above-mentioned weighted value value is bigger, represents that its probability that is in danger is higher, numerical value is negative, illustrates the probability that is in danger
It is lower.For example, it is the biggest factor of probability of being in danger in the model that insurant's occupational risk grade, which is 4 this factor,.By this
Air control model, we are drawn with the presence of the insured people's following characteristics of latent adverse selection risk：The professional risk bigger grade of insured people, goes out
The risk of danger is higher；Being in danger as the client below member's half a year, rate is higher, and rate of being in danger more than half a year is relatively low；Purchase protection amount exists
40-50 ten thousand rate highest of being in danger；The risk of referrer is far below non-recommended people.The relative risk of new client is higher than frequent customer.
Optionally, in order to refine each Variable Factors, to improve the accuracy rate of air control model, and then improve calculating and be in danger probability
Accuracy rate, as shown in figure 3, described, determined based on the big data sample can for multiple Variable Factors of the default insurance kind
With including：
Step S310, the correlation of the unitary variant factor and result of being in danger is calculated based on the big data sample.
In the present example embodiment, can be based on big data sample and combine be in danger result analyze respectively unitary variant because
The probability that is in danger of each Variable Factors in son, and each Variable Factors of determine the probability of being in danger according to each Variable Factors and result of being in danger
Correlation.Specifically, when the probability that is in danger of Variable Factors is larger, illustrate the correlation of the Variable Factors and result of being in danger compared with
By force, when the probability that is in danger of Variable Factors is smaller, illustrate that the Variable Factors and the continuous item for result of being in danger are weaker.
For example, above-mentioned steps S111 is illustrated by taking the unitary variant factor age as an example.First will according to business experience
The unitary variant factor age is divided into following 8 groups according to the difference at age, [0-17] year, [18-34] year, [35,39] year, [40,
44] year, [45,49] year, [50,54] year, [55,59] year and [60,65] year.It should be noted that above-mentioned every group of age group
A respectively Variable Factors；Then, the result of being in danger based on big data sample and big data sample calculates above-mentioned each age
The probability that is in danger of group, that is, calculate the probability that is in danger of each Variable Factors；Finally, each group age group is determined according to the size for the probability that is in danger
The correlation of (i.e. each Variable Factors) with result of being in danger.
Step S320, using a preset algorithm and the correlation, multiple Variable Factors are filtered out.
In the present example embodiment, the preset algorithm can be stepwise regression method, IV values method or calculate entropy
(ENTROPY) method of value.In order to select more accurate Variable Factors, the preset algorithm can be stepwise regression method,
IV values method and the method for calculating the method for entropy (ENTROPY) value and combining business experience progress aggregative weighted assessment, i.e., it is sharp
With stepwise regression method, the method for IV values method and calculating entropy (ENTROPY) value and carry out aggregative weighted with reference to business experience
The method of assessment, filter out Variable Factors and the stronger Variable Factors of probabilistic correlation that are in danger.
On this basis, it is described to be based on the big data sample and the multiple Variable Factors, determine each variable because
The weighted value of son can include：The result of being in danger of the big data sample is patrolled with the multiple Variable Factors execution filtered out
Collect regression algorithm, the weighted value for each Variable Factors that calculating sifting goes out.
In the present example embodiment, can be according to the result of being in danger of each sample data in big data sample (be in danger probability)
The weighted value of the Variable Factors respectively filtered out is calculated by following formula,
Wherein, piRepresent the probability that is in danger of i-th of data sample in big data sample, ωiRepresent j-th of change filtered out
Measure the weighted value of the factor, XjRepresent the contribution margin of j-th of Variable Factors filtered out, ω0Represent intercept,
It should be noted that XjMeet the X in i-th of data samplejIt is corresponding filter out Variable Factors when be 1, Xj
I-th of data sample does not meet the XjIt is corresponding filter out Variable Factors when be 0.For example, for Variable Factors female and man,
When sex in data sample is female, X corresponding to Variable Factors femalejFor 1, X corresponding to Variable Factors manjFor 0；In data sample
In sex for it is male when, X corresponding to Variable Factors femalejFor 0, X corresponding to Variable Factors manjFor 1.
It is described based on described in the weighted value of the multiple Variable Factors and each Variable Factors structure on the basis of
Air control model can include：Weight based on the multiple Variable Factors filtered out and each Variable Factors filtered out
Value builds the air control model.In the present example embodiment, according to the multiple Variable Factors filtered out and can filter out
The weighted values of each Variable Factors build a risk assessment table, and the risk assessment table is defined as air control model.
Optionally, it is described to be included based on the big data sample structure air control model：Obtain checking data
Sample, and based on the AUC of the checking data sample calculating air control model；Judge whether the AUC meets to preset
Condition, and when the AUC is unsatisfactory for default bar, the air control model is rebuild based on the big data sample, so that
The AUC of the air control model rebuild meets preparatory condition.
In the present example embodiment, the AUC represents discrimination of the air control model to fine or not sample, and AUC is bigger
Represent that air control model is better to the discrimination of fine or not sample, AUC is smaller to represent that air control model is got over to the discrimination of fine or not sample
Difference, the common span of AUC are 0.5~1.Based on this, it is necessary to which data sample will be verified after air control model is built
The air control model is inputted to calculate the AUC of the air control model, and judges whether the AUC is more than 0.5, is more than 0.5 in AUC
When, the sub-control model is defined as to final air control model.When AUC is not more than 0.5, the air control model is rebuild, directly
AUC to the air control model is more than 0.5.The checking data sample can be the declaration form data to have failed.In this example
Property embodiment in, it is described checking data sample can be across time samples, for example, the declaration form data to have failed for 2015.
It should be noted that the AUC for calculating air control model is known technology, therefore here is omitted.
In summary, based on big data sample, multi-angle, multi-level study feature of risk, to build air control model, enter
And protect the accurate potential risk for objectively catching, identifying user in flow in core.Further, since air control model is to be based on big data
Sample is built-up, and compared to prior art, core protects accuracy rate height, so as to filter out the insured people of high value low-risk,
The insured people of adverse selection is effectively intercepted, reduces and compensates cost.
In the step s 120, the historical data and external data of user is obtained by the air control model.
In the present example embodiment, when being passed to the insurance data of user to air control model, air control model can basis
User profile in insurance data obtains the historical data and external data of the user.Wherein, historical data can include user
Consumer behavior record, membership information, the means of payment and accumulative risk protection amount etc..The external data can be commented including credit
Point, blacklist etc..
Optionally, the air control model can include Cache, and the history data store is in the cache
In device.Based on this, the air control model can wherein obtain the historical data of user from cache.It should be noted that can
With in default free time section by history data store in the cache.For example, the default free time section can be with
For 1 o'clock to 5 o'clock in morning, the default free time section can also be 3 o'clock in 11 o'clock to the morning in evening, originally show
Example property embodiment is not particularly limited to this.
Optionally, because external data is huge, therefore the acquisition external data can include：The air control model can be with
Including external interface, the air control model can obtain the outside corresponding with the insurance data by the external interface
Data.In the present example embodiment, multiple external interfaces can be set according to the difference in the source of insurance data, with according to not
Same insurance data source obtains external data from corresponding external interface.For example, it is tourist corporation in the source of insurance data
When, external data can be obtained from the interface of corresponding tourist corporation.
In step s 130, the insurance data, the historical data and described outer are based on using the air control model
Portion's data, calculate the probability that is in danger that the user is directed to the default insurance kind.
Optionally, it is described to be based on the insurance data, the historical data and the outside using the air control model
Data, the probability that is in danger for calculating the user for the default insurance kind can include：
Step S132, according to the insurance data, the historical data and the external data in the air control model
The middle weighted value for obtaining each Variable Factors simultaneously determines the contribution margin of each Variable Factors；
Step S134, according under the contribution margin of the weighted value of each Variable Factors and each Variable Factors and combination
State formula and calculate the probability that is in danger that the user is directed to the default insurance kind：
Wherein, P represents that user is directed to the probability that is in danger of the default insurance kind, ωiRepresent the weight of i-th of Variable Factors
Value, XiRepresent the contribution margin of i-th of Variable Factors, ω0Represent intercept,
In summary, the probability that is in danger is calculated using air control model, compared to prior art, substantially increases risk assessment
Efficiency and flexibility, it can meet that core is protected ageing, save substantial amounts of manpower and material resources cost.
Optionally, in order to quantify the probability that is in danger, methods described can also include：According to user for the default insurance kind
It is in danger the risk score of user described in probability calculation, and mode of accepting insurance is determined according to the risk score.
In the present example embodiment, different risk scores can be set to the probability that is in danger in different sections, is calculating
Be in danger probability when, the probability interval that is in danger belonging to probability that is in danger according to this determines the risk score corresponding to probability of being in danger.Also
The probability that will first can be in danger substitutes intoIn, it is then general according to the risk to calculate the risk probability of the probability that is in danger
Rate determines risk score.
In the present example embodiment, the mode of accepting insurance can include：Premium is lowered to accept insurance, normally accept insurance and raise
Premium is accepted insurance.Risk score can be divided into low-risk scoring section, risk scoring section according to the different of risk score
And excessive risk scoring section, wherein, low-risk scoring section corresponds to low-risk user, and risk scoring section corresponds to risk
User, excessive risk scoring section correspond to excessive risk user.The maximum in low-risk scoring section is less than risk scoring section
Minimum value, the maximum in risk scoring section are less than the minimum value in excessive risk scoring section.
It is described to determine that the mode of accepting insurance include according to the risk score based on this：
Judge whether the risk score belongs to low-risk scoring section, risk scoring section or excessive risk scoring area
Between；
When judging that the risk score belongs to the low-risk scoring section, it is determined that the mode of accepting insurance is the downward
Premium is accepted insurance, i.e., low-risk user is accepted insurance using premium is lowered；
When judging that the risk score belongs to the risk scoring section, it is determined that the mode of accepting insurance is described normal
Accept insurance, i.e., for risk user using normal mode of accepting insurance；
When judging that the risk score belongs to the excessive risk scoring section, it is determined that the mode of accepting insurance is the up-regulation
Premium is accepted insurance, i.e., is accepted insurance for excessive risk user using up-regulation premium.
From the foregoing, it will be observed that using different Insuring ways for the user of different risk scores, the personalization of protection amount is realized
Floating pricing, improves the service experience of user, and provides a kind of new Insuring way.
It should be noted that although describing each step of method in the disclosure with particular order in the accompanying drawings, still,
This, which does not require that or implied, to perform these steps according to the particular order, or has to carry out the step shown in whole
Desired result could be realized.It is additional or alternative, it is convenient to omit some steps, multiple steps to be merged into a step and held
OK, and/or by a step execution of multiple steps etc. are decomposed into.
In an exemplary embodiment of the disclosure, a kind of risk assessment device is additionally provided, as shown in figure 4, the risk is commented
Estimating device 400 can include：Data afferent module 401, data acquisition module 402 and probability evaluation entity 403, wherein：
Data afferent module 401, the insurance data that can be used for user being directed to default insurance kind are passed to air control model, its
In, the air control model is built-up based on big data sample；
Data acquisition module 402, it can be used for the historical data and external data that user is obtained by the air control model；
Probability evaluation entity 403, it can be used for being based on the insurance data, the historical data using the air control model
And the external data, calculate the probability that is in danger that the user is directed to the default insurance kind.
The detail of each risk assessment apparatus module is carried out in corresponding virtual object control method in above-mentioned
Detailed description, therefore here is omitted.
It should be noted that although being referred to some modules or unit of the equipment for execution in above-detailed,
But it is this division it is not enforceable.In fact, according to embodiment of the present disclosure, two or more above-described modules
Either the feature of unit and function can embody in a module or unit.Conversely, an above-described module or
The feature and function of person's unit can be further divided into being embodied by multiple modules or unit.
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can realize the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be implemented as following form, i.e.,：It is complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.), or hardware and software, can unite here
Referred to as " circuit ", " module " or " system ".
The electronic equipment 500 according to the embodiment of the invention is described referring to Fig. 5.The electronics that Fig. 5 is shown
Equipment 500 is only an example, should not bring any restrictions to the function and use range of the embodiment of the present invention.
As shown in figure 5, electronic equipment 500 is showed in the form of universal computing device.The component of electronic equipment 500 can wrap
Include but be not limited to：Above-mentioned at least one processing unit 510, above-mentioned at least one memory cell 520, connection different system component
The bus 530 of (including memory cell 520 and processing unit 510), display unit 540.
Wherein, the memory cell is had program stored therein code, and described program code can be held by the processing unit 510
OK so that the processing unit 510 performs various according to the present invention described in above-mentioned " illustrative methods " part of this specification
The step of illustrative embodiments.For example, the processing unit 510 can perform step S110 as shown in fig. 1, by user
Air control model is passed to for the insurance data for presetting insurance kind, wherein, the air control model is built-up based on big data sample；
Step S120, the historical data and external data of user is obtained by the air control model；Step S130, the air control mould is utilized
Type is based on the insurance data, the historical data and the external data, calculates the user and is directed to the default insurance kind
The probability that is in danger.
Memory cell 520 can include the computer-readable recording medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 5201 and/or cache memory unit 5202, it can further include read-only memory unit (ROM) 5203.
Memory cell 520 can also include program/utility with one group of (at least one) program module 5205
5204, such program module 5205 includes but is not limited to：Operating system, one or more application program, other program moulds
Block and routine data, the realization of network environment may be included in each or certain combination in these examples.
Bus 530 can be to represent the one or more in a few class bus structures, including memory cell bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 500 can also be with one or more external equipments 570 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, the equipment communication interacted with the electronic equipment 500 can be also enabled a user to one or more, and/or with causing
Any equipment that the electronic equipment 500 can be communicated with one or more of the other computing device (such as router, modulation /demodulation
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 550.Also, electronic equipment 500 can be with
By network adapter 560 and one or more network (such as LAN (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.As illustrated, network adapter 560 is communicated by bus 530 with other modules of electronic equipment 500.
It should be understood that although not shown in the drawings, can combine electronic equipment 500 does not use other hardware and/or software module, including but not
It is limited to：Microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can be realized by software, can also be realized by way of software combines necessary hardware.Therefore, according to the disclosure
The technical scheme of embodiment can be embodied in the form of software product, the software product can be stored in one it is non-volatile
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are to cause a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) is performed according to disclosure embodiment
Method.
In an exemplary embodiment of the disclosure, a kind of computer-readable recording medium is additionally provided, is stored thereon with energy
Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product, it includes program code, when described program product is run on the terminal device, institute
State program code be used for make the terminal device perform described in above-mentioned " illustrative methods " part of this specification according to this hair
The step of bright various illustrative embodiments.
With reference to shown in figure 6, the program product for being used to realize the above method according to the embodiment of the present invention is described
600, it can use portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device,
Such as run on PC.However, the program product not limited to this of the present invention, in this document, readable storage medium storing program for executing can be with
Be it is any include or the tangible medium of storage program, the program can be commanded execution system, device either device use or
It is in connection.
Described program product can use any combination of one or more computer-readable recording mediums.Computer-readable recording medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or any combination above.The more specifically example of readable storage medium storing program for executing is (non exhaustive
List) include：It is electrical connection, portable disc, hard disk, random access memory (RAM) with one or more wires, read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media can be including the data-signal in a base band or as carrier wave part propagation, its
In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie beyond readable storage medium storing program for executing
Matter, the computer-readable recording medium can send, propagate either transmit for used by instruction execution system, device or device or and its
The program of combined use.
The program code included on computer-readable recording medium can be transmitted with any appropriate medium, including but not limited to wirelessly, be had
Line, optical cable, RF etc., or above-mentioned any appropriate combination.
Can being combined to write the program operated for performing the present invention with one or more programming languages
Code, described program design language include object oriented program language-Java, C++ etc., include routine
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
Perform on computing device, partly perform on a user device, the software kit independent as one performs, is partly calculated in user
Its upper side point is performed or performed completely in remote computing device or server on a remote computing.It is remote being related to
In the situation of journey computing device, remote computing device can pass through the network of any kind, including LAN (LAN) or wide area network
(WAN) user calculating equipment, is connected to, or, it may be connected to external computing device (such as utilize ISP
To pass through Internet connection).
In addition, above-mentioned accompanying drawing is only the schematic theory of the processing included by method according to an exemplary embodiment of the present invention
It is bright, rather than limitation purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings was not intended that or limited these processing is suitable
Sequence.In addition, being also easy to understand, these processing for example can be performed either synchronously or asynchronously in multiple modules.
Those skilled in the art will readily occur to the disclosure its after considering specification and putting into practice invention disclosed herein
His embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modifications, purposes or
Adaptations follow the general principle of the disclosure and including the undocumented common knowledge in the art of the disclosure or
Conventional techniques.Description and embodiments are considered only as exemplary, and the true scope of the disclosure and spirit are by claim
Point out.
It should be appreciated that the precision architecture that the disclosure is not limited to be described above and is shown in the drawings, and
And various modifications and changes can be being carried out without departing from the scope.The scope of the present disclosure is only limited by appended claim.
Claims (13)
- A kind of 1. methods of risk assessment, it is characterised in that including：The insurance data that user is directed to default insurance kind is passed to air control model, wherein, the air control model is to be based on big data sample This is built-up；The historical data and external data of user is obtained by the air control model；The insurance data, the historical data and the external data are based on using the air control model, calculates the use Family is directed to the probability that is in danger of the default insurance kind.
- 2. methods of risk assessment according to claim 1, it is characterised in that methods described also includes：Risk score according to user for the user described in probability calculation that is in danger of the default insurance kind, and commented according to the risk Point determination is accepted insurance mode.
- 3. methods of risk assessment according to claim 1, it is characterised in that methods described also includes：Based on the big number The air control model is built according to sample；Wherein, it is described to be included based on the big data sample structure air control model：Multiple Variable Factors for the default insurance kind are determined based on the big data sample；Based on the big data sample and the multiple Variable Factors, the weighted value of each Variable Factors is determined；The air control model is built based on the weighted value of the multiple Variable Factors and each Variable Factors.
- 4. methods of risk assessment according to claim 3, it is characterised in that described that pin is determined based on the big data sample To multiple Variable Factors of the default insurance kind, including：The correlation of the unitary variant factor and result of being in danger is calculated based on the big data sample,Using a preset algorithm and the correlation, multiple Variable Factors are filtered out；It is described to be based on the big data sample and the multiple Variable Factors, the weighted value of each Variable Factors is determined, including：To be in danger result and the multiple Variable Factors execution logic regression algorithm filtered out of the big data sample, calculate The weighted value of each Variable Factors filtered out.
- 5. methods of risk assessment according to claim 3, it is characterised in that described to utilize the air control model based on described Insurance data, the historical data and the external data, calculate the probability that is in danger that the user is directed to the default insurance kind Including：Each change is obtained in the air control model according to the insurance data, the historical data and the external data Measure the weighted value of the factor and determine the contribution margin of each Variable Factors；According to the contribution margin of the weighted value of each Variable Factors and each Variable Factors and combine following formula calculating institute State the probability that is in danger that user is directed to the default insurance kind：<mrow> <mi>P</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mrow> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <msup> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>+</mo> </msup> <msub> <mi>&omega;</mi> <mn>0</mn> </msub> </mrow> </mrow> </msup> </mrow> </mfrac> </mrow>Wherein, P represents that user is directed to the probability that is in danger of the default insurance kind, ωiRepresent the weighted value of i-th of Variable Factors, XiTable Show the contribution margin of i-th of Variable Factors, ω0Represent intercept,
- 6. methods of risk assessment according to claim 3, it is characterised in that described based on big data sample structure institute Stating air control model also includes：Checking data sample is obtained, and the AUC of the air control model is calculated based on the checking data sample；Judge whether the AUC meets preparatory condition, and when the AUC is unsatisfactory for default bar, based on the big data Sample rebuilds the air control model, so that the AUC of the air control model rebuild meets preparatory condition.
- 7. methods of risk assessment according to claim 1, it is characterised in that the acquisition external data includes：The air control model includes external interface, and the air control model is obtained and the insurance data pair by the external interface The external data answered.
- 8. the methods of risk assessment according to any one in claim 1~7, it is characterised in that the default insurance kind bag Comprehensive accident insurance is included, the big data sample includes comprehensive accident insurance data sample.
- 9. the methods of risk assessment according to any one in claim 1~7, it is characterised in that the historical data bag Consumer behavior record, membership information, the means of payment and the accumulative risk for including user are insured amount；The external data is commented including credit Divide, blacklist.
- 10. methods of risk assessment according to claim 2, it is characterised in that the mode of accepting insurance includes：Premium is lowered to hold Protect, normal accept insurance and raise premium and accept insurance；It is described to determine that the mode of accepting insurance includes according to the risk score：Judge whether the risk score belongs to low-risk scoring section, risk scoring section or excessive risk scoring section；When judging that the risk score belongs to the low-risk scoring section, it is determined that the mode of accepting insurance is the downward premium Accept insurance；When judging that the risk score belongs to the risk scoring section, it is determined that the mode of accepting insurance normally is held to be described Protect；When judging that the risk score belongs to the excessive risk scoring section, it is determined that the mode of accepting insurance is the up-regulation premium Accept insurance.
- A kind of 11. risk assessment device, it is characterised in that including：Data afferent module, the insurance data for user to be directed to default insurance kind are passed to air control model, wherein, the air control mould Type is built-up based on big data sample；Data acquisition module, for obtaining the historical data and external data of user by the air control model；Probability evaluation entity, for being based on the insurance data, the historical data and described outer using the air control model Portion's data, calculate the probability that is in danger that the user is directed to the default insurance kind.
- 12. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program The methods of risk assessment described in any one in claim 1~10 is realized when being executed by processor.
- 13. a kind of electronic equipment, it is characterised in that including：Processor；AndMemory, for storing the executable instruction of the processor；Wherein, the processor is configured to carry out any one in perform claim requirement 1~10 via the execution executable instruction Described methods of risk assessment.
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| CN201711190314.2A CN107818513A (en) | 2017-11-24 | 2017-11-24 | Methods of risk assessment and device, storage medium, electronic equipment |
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