CN109409648A - Claims Resolution air control method, apparatus, computer equipment and storage medium - Google Patents
Claims Resolution air control method, apparatus, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of Claims Resolution air control method, apparatus, computer equipment and storage mediums.The present invention applies the field in big data in conjunction with artificial intelligence, is the intelligent predicting based on big data analysis.The described method includes: obtaining the risks and assumptions of Claims Resolution case；The evaluation information of the risks and assumptions is determined according to preset vulnerability database；It is normalized to obtain basic probability assignment using the subordinating degree function that fuzzy theory obtains the evaluation information；And the basic probability assignment is carried out to obtain the air control conclusion of the risks and assumptions by fusion according to rule of combination.The air control accuracy of Claims Resolution case can be improved in method by implementing the embodiment of the present invention, improves the controllability of air control cost, improves insurance service quality.
Description
Technical field
The present invention relates to settlement of insurance claim air control technical fields more particularly to a kind of Claims Resolution air control method, apparatus, computer to set
Standby and storage medium.
Background technique
With science and expanding economy, insurance has become very important a kind of safeguard way in people's daily life.
However, groups of people gain insurance money by cheating to speculate through the false accident illusion of manufacture, insurance company needs to spend big
The manpower and material resources of amount go to distinguish this kind of settlement of insurance claim case that there is fraud.Existing settlement of insurance claim air control measure is usually by protecting
Dangerous expert sets rule to carry out risk control to Claims Resolution case, therefore existing settlement of insurance claim air control measure is to insurance expert
Professional knowledge ability, risk judgment ability and settlement of insurance claim experience the very high and different insurance expert of requirement for
The risk control that the same Claims Resolution case is made is all different.For insurance company, the risk that expert is made is insured
Control can largely effect on air control cost, and it is uncontrollable that the risk control of low accuracy will lead to air control cost, reduce insurance service
Quality.
Summary of the invention
The embodiment of the invention provides a kind of Claims Resolution air control method, apparatus, computer equipment and storage mediums, it is intended to improve
To the risk control accuracy of Claims Resolution case.
In a first aspect, the embodiment of the invention provides a kind of Claims Resolution air control methods comprising: obtain the risk of Claims Resolution case
The factor, the risks and assumptions are for judging the Claims Resolution case with the presence or absence of the Claims Resolution information of fraud possibility；According to default
Vulnerability database determine the evaluation informations of the risks and assumptions；The degree of membership letter of the evaluation information is obtained using fuzzy theory
Number is normalized to obtain basic probability assignment；And the basic probability assignment is melted according to rule of combination
It closes to obtain the air control conclusion of the risks and assumptions.
Second aspect, the embodiment of the invention also provides a kind of Claims Resolution wind-controlling devices comprising: acquiring unit, for obtaining
The risks and assumptions of Claims Resolution case are taken, the risks and assumptions are for judging the Claims Resolution case with the presence or absence of the reason of fraud possibility
Pay for information；Determination unit, for determining the evaluation information of the risks and assumptions according to preset vulnerability database；Normalization is single
Member is normalized to obtain elementary probability point using the subordinating degree function that fuzzy theory obtains the evaluation information
Match；And integrated unit, for according to rule of combination by the basic probability assignment carry out fusion to obtain the risk because
The air control conclusion of son.
The third aspect, the embodiment of the invention also provides a kind of computer equipments comprising memory and processor, it is described
Computer program is stored on memory, the processor realizes the above method when executing the computer program.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage medium, the storage medium storage
There is computer program, the computer program can realize the above method when being executed by a processor.
The embodiment of the invention provides a kind of Claims Resolution air control method, apparatus, computer equipment and storage mediums.Wherein, institute
The method of stating includes: the risks and assumptions for obtaining Claims Resolution case；The evaluation of the risks and assumptions is determined according to preset vulnerability database
Information；It is normalized to obtain elementary probability point using the subordinating degree function that fuzzy theory obtains the evaluation information
Match；And the basic probability assignment is carried out to obtain the air control conclusion of risks and assumptions by fusion according to rule of combination.This hair
For bright embodiment due to being merged basic probability assignment by rule of combination, the air control that each risks and assumptions can be improved is accurate
Degree, and then the air control accuracy of Claims Resolution case is improved, the controllability of air control cost is improved, insurance service quality is improved.
Detailed description of the invention
Technical solution in order to illustrate the embodiments of the present invention more clearly, below will be to needed in embodiment description
Attached drawing is briefly described, it should be apparent that, drawings in the following description are some embodiments of the invention, general for this field
For logical technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is the application scenarios schematic diagram of Claims Resolution air control method provided in an embodiment of the present invention；
Fig. 2 is the flow diagram of Claims Resolution air control method provided in an embodiment of the present invention；
Fig. 3 is the sub-process schematic diagram of Claims Resolution air control method provided in an embodiment of the present invention；
Fig. 4 is the sub-process schematic diagram of Claims Resolution air control method provided in an embodiment of the present invention；
Fig. 5 is the sub-process schematic diagram of Claims Resolution air control method provided in an embodiment of the present invention；
Fig. 6 is the schematic block diagram of Claims Resolution wind-controlling device provided in an embodiment of the present invention；
Fig. 7 is the schematic block diagram of the normalization unit of Claims Resolution wind-controlling device provided in an embodiment of the present invention；
Fig. 8 is the schematic block diagram of the integrated unit of Claims Resolution wind-controlling device provided in an embodiment of the present invention；And
Fig. 9 is the schematic block diagram of computer equipment provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, the presence or addition of element, component and/or its set.
It is also understood that mesh of the term used in this description of the invention merely for the sake of description specific embodiment
And be not intended to limit the present invention.As description of the invention and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in description of the invention and the appended claims is
Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.
Fig. 1 and Fig. 2 are please referred to, Fig. 1 is the application scenarios schematic diagram of Claims Resolution air control method provided in an embodiment of the present invention.Figure
2 be the schematic flow chart of Claims Resolution air control method provided in an embodiment of the present invention.The Claims Resolution air control method is applied in terminal 10.
The terminal 10 is connect with server 20, to receive all information of the related Claims Resolution case sent by server 20.The Claims Resolution wind
Prosecutor method is suitable for settlement of insurance claim case, and settlement of insurance claim case can be the cases such as car insurance, medical insurance and property insurance
Part.
Fig. 2 is the flow diagram of Claims Resolution air control method provided in an embodiment of the present invention.As shown in Fig. 2, this method includes
Following steps S110-S150.
S110, the risks and assumptions for obtaining Claims Resolution case, the risks and assumptions are for judging whether the Claims Resolution case deposits
In the Claims Resolution information of fraud possibility.
In one embodiment, Claims Resolution case is that insurer insurance risk occurs in scope of insurance coverage asks for guarantor to insurance company
The event of danger gold.For example, the event that car accident asks for insurance money to insurance company occurs for insurer；Or insurer's burst disease
Disease asks for the event of insurance money to insurance company.Insurance company first has to related to Claims Resolution case in processing Claims Resolution case
All Claims Resolution information are collected.In medical treatment Claims Resolution case, Claims Resolution information is for example including insurance policy, case history and medical invoice
Deng；For example including insurance policy, setting loss list, confirmation of responsibility book etc. in vehicle insurance Claims Resolution case.After obtaining Claims Resolution information, from Claims Resolution
It is obtained in information for judging to settle a claim case the risks and assumptions with the presence or absence of fraud, in medical treatment Claims Resolution case, risks and assumptions example
It such as include the drug inventory in idagnostic logout, inspection item and the medical invoice in case history；In vehicle insurance Claims Resolution case, wind
The dangerous factor be, for example, the vehicle model in insurance policy, the part of damage in setting loss list, in confirmation of responsibility book be in danger the time and
It is in danger place etc..
In one embodiment, as shown in figure 3, the step S110 may include step S111-S112.
Claims Resolution information in S111, case of settling a claim from server acquisition.
In one embodiment, Claims Resolution information be by insurer either insure staff be uploaded to server, for example,
Insure staff and the insurance policy, setting loss list and confirmation of responsibility book that insurer provides are uploaded to server.Terminal passes through again
Server receives the Claims Resolution information of vehicle insurance Claims Resolution case.
S112, risks and assumptions are extracted from the Claims Resolution information according to preset vulnerability database.
In one embodiment, due to including that can be used for judging whether there is the possible Claims Resolution letter of fraud in Claims Resolution information
Breath and other Claims Resolution information unrelated with fraud is judged whether there is, it is therefore desirable to which extract from Claims Resolution information is for judgement
It is no to there is the possible Claims Resolution information of fraud as risks and assumptions.Wherein, vulnerability database be insurance expert preset risk because
The data acquisition system of son matches the risks and assumptions in vulnerability database with Claims Resolution information, after obtaining Claims Resolution information if reason
The risks and assumptions for existing in information and matching with the preset risks and assumptions of vulnerability database are paid for, then are extracted in the Claims Resolution information
Risks and assumptions.For example, including the Claims Resolutions such as name, age, household register, vehicle model, the vehicle age of insurer letter in insurance policy
Breath, wherein vehicle model and vehicle age are preset risks and assumptions in vulnerability database, and include in information of settling a claim
The Claims Resolution information of vehicle model and vehicle age, then extracting vehicle model in Claims Resolution information is B board as a risks and assumptions, vehicle
Age is to be used as another risks and assumptions in 6 years.
S120, the evaluation information that the risks and assumptions are determined according to preset vulnerability database.
It in one embodiment, further include having the insurance preset risks and assumptions of expert and commenting in preset vulnerability database
The data acquisition system of corresponding relationship between valence information.After obtaining risks and assumptions, the risks and assumptions pair are searched from vulnerability database
The evaluation information answered so that it is determined that the risks and assumptions evaluation information.In the present embodiment, it there are three types of evaluation informations, respectively deposits
In fraud, there is no cheat and do not know with the presence or absence of fraud.For example, for vehicle model be B board risks and assumptions according to
Preset rules show that there is no the evaluation informations of fraud；For the time of being in danger be morning any risks and assumptions according to preset rules
Obtain the evaluation information in the presence of fraud；For be in danger risks and assumptions that place is national highway according to preset rules obtain it is uncertain whether
There are the evaluation informations of fraud.
S130, it is normalized to obtain base using the subordinating degree function that fuzzy theory obtains the evaluation information
This probability assignments.
In one embodiment, the value quantified due to evaluation information not instead of one, a fuzzy evaluation are taken advantage of as existed
Swindleness, there is no cheat and do not know not to be able to satisfy design conditions, it is therefore desirable to evaluation with the presence or absence of fuzzy evaluations such as frauds
Information is normalized to obtain quantized value.
In one embodiment, as shown in figure 4, the step S130 may include step S131-S133.
S131, the subordinating degree function that the evaluation information is constructed using fuzzy theory.
In one embodiment, fuzzy theory refers to the reason of the basic conception for having used fuzzy set or continuous subordinating degree function
By.Subordinating degree function is the mathematical tool for characterizing fuzzy set, in order to describe element to a fuzzy set in subset
Membership, due to the fuzzy of this relationship, it will replace 0,1 this two value with the numerical value taken from section [0,1]
It describes, indicates that element belongs to " really degree " of certain fuzzy set.In the present embodiment, if Θ={ θ1、θ2、θ3…θn, Θ
For domain, evaluation information is indicated with the presence or absence of all possibilities of fraud, θ is fuzzy subset, θnFor n-th of fuzzy subset, and
Meet following condition: θ ∈ Θ, P (θ): → [0,1], then P (θ) is referred to as the subordinating degree function of evaluation information.Wherein domain Θ includes
θ1、θ2、θ3Three fuzzy subsets, θ1To there is the fuzzy subset of fraud, θ2For there is no the fuzzy subset of fraud, θ3It is uncertain
With the presence or absence of the fuzzy subset of fraud.So θ1、θ2、θ3Corresponding subordinating degree function is respectively as follows: P (θ1)、P(θ2)、P(θ3).Its
In, the specific formula of subordinating degree function is determined by method of expertise.Method of expertise is the practical experience according to expert
The processing formula or corresponding weight coefficient value for providing fuzzy message determine a kind of method of membership function.I.e. subordinating degree function be by
What insurance expert set up according to certain rules.
S132, the evaluation information obtain corresponding degree of membership by the subordinating degree function.
In one embodiment, after determining subordinating degree function, evaluation information is input in subordinating degree function to obtain
To the degree of membership of the evaluation information.Degree of membership is the output valve of membership function, indicates that evaluation information belongs to some fuzzy subset's
Subjection degree.For example, the evaluation information that vehicle model is B board is input in subordinating degree function, P (θ is obtained1)=0.2, P (θ2)
=0.7, P (θ3)=0.1.Wherein, P (θ1)=0.2 indicates that the evaluation information that vehicle model is B board has the degree of membership cheated and is
0.2, P (θ2It is 0.7, P (θ that)=0.7, which indicates that the degree of membership of fraud is not present in the evaluation information that vehicle model is B board,3The table of)=0.1
Show that the uncertain degree of membership with the presence or absence of fraud of evaluation information that vehicle model is B board is 0.1.
S133, using the degree of membership as basic probability assignment.
In one embodiment, Basic probability assignment function is constructed first, and Basic probability assignment function is each for distributing to
The function of proposition trusting degree, basic probability assignment (Basic Probability Assignment, abbreviation BPA) are substantially general
The output valve of rate partition function indicates the trusting degree to proposition.If identification framework is Θ, Θ={ θ1、θ2、θ3…θn, identification
Set of the frame representation to all possible answers of proposition, the one such possible answer of θ expression.For example, Θ is Claims Resolution case
The evaluation information set of the part risk factor, including there are three types of may θ1、θ2、θ3, θ1Indicate there is fraud, θ2Expression is not deposited
It is cheating, θ3Indicate uncertain with the presence or absence of fraud.Define m:2Θ→ [0,1], and meet following condition:
Wherein, 2ΘIndicate the set that all subsets of identification framework Θ collectively constitute, the referred to as power set of Θ, formula (1) table
The basic probability assignment for showing empty set is 0, and formula (2) indicates 2ΘThe sum of basic probability assignment of upper all elements is 1.It is above-mentioned when meeting
After condition, then claiming m is 2ΘOn Basic probability assignment function, be basic probability assignment that m (θ) is θ, indicate to the letter of proposition θ
Appoint degree.
It builds and obtained degree of membership is assigned to Basic probability assignment function after Basic probability assignment function obtains base
This probability assignments.
P (θ)=m (θ) (3)
For example, being to be assigned to basic probability assignment letter in the presence of the degree of membership cheated by the evaluation information that vehicle model is B board
It is 0.2 that number, which obtains basic probability assignment, obtains m (θ by formula (3)1)=P (θ1)=0.2, m (θ1) expression vehicle model be B board
It is 0.2 there are the trusting degree of fraud；It is that there is no the degrees of membership of fraud to be assigned to base by the evaluation information that vehicle model is B board
It is 0.7 that this probability distribution function, which obtains basic probability assignment, obtains m (θ by formula (3)2)=P (θ2)=0.7, m (θ2) indicate vehicle
Model B board is 0.7 there is no the trusting degree of fraud；It is uncertain whether deposit by the evaluation information that vehicle model is B board
Being assigned to Basic probability assignment function to obtain basic probability assignment in the degree of membership of fraud is 0.1, obtains m (θ by formula (3)3)
=P (θ3)=0.1, m (θ3) indicate that vehicle model is that B board does not know with the presence or absence of the trusting degree cheated to be 0.1.
S140, the basic probability assignment is carried out to obtain the air control of the risks and assumptions by fusion according to rule of combination
Conclusion.
In one embodiment, rule of combination is the compositional rule that can represent the synergy between evidence.At this
In embodiment, Basic probability assignment function is the evidence in rule of combination, and basic probability assignment is expressed as the trust journey to evidence
Degree.For example, risks and assumptions are that vehicle model is B board in vehicle insurance Claims Resolution case, according to insurance expert 1 and insure expert's 2
The basic probability assignment m that the preset rules being set separately obtain evaluation information and obtain after normalized1(θ1)、m1
(θ2)、m1(θ3) and m2(θ1)、m2(θ2)、m2(θ3), m1(θ1) indicate insurance expert 1 to the letter that vehicle model is that B board has fraud
Appoint degree, m1(θ2) indicate that insurance expert 1 is that there is no the trusting degree of fraud, m for B board to vehicle model1(θ3) indicate that insurance is special
1 pair of vehicle model of family is the uncertain trusting degree with the presence or absence of fraud of B board；m2(θ1) indicate that insurance expert 2 is to vehicle model
There is the trusting degree of fraud, m in B board2(θ2) indicate that insurance expert 2 is that the trusting degree cheated is not present in B board to vehicle model,
m2(θ3) indicate that insurance expert 2 is the uncertain trusting degree with the presence or absence of fraud of B board to vehicle model.It will according to rule of combination
m1(θ1) and m2(θ1) merged, by m1(θ2) and m2(θ2) merged, by m1(θ3) and m2(θ3) merged, after fusion
To corresponding joint basic probability assignment, wherein fusion refers to obtaining using two evidences as being input in rule of combination
One process of joint basic probability assignment.Maximum joint basic probability assignment indicates maximum to the trusting degree of evidence, because
This is using maximum joint basic probability assignment as the air control conclusion of risks and assumptions, for example, m1(θ2) and m2(θ2) fused
Elementary probability maximum is closed, then the air control conclusion that vehicle model is the risks and assumptions of B board is that there is no frauds.
In one embodiment, as shown in figure 5, the step S140 may include step S141-S143.
S141, the basic probability assignment is modified by modifying factor.
In one embodiment, since the Knowledge Capability of each insurance expert is different, the significance level of each evidence is different, from
And evidence is made to possess different confidence level and weight.Evidence is modified to obtain by introducing modifying factor and is more accurately demonstrate,proved
According to.
Wherein, crw,iFor modifying factor, wiIndicate weight, riIndicate confidence level, i indicates i-th of evidence.mθ,iIndicate amendment
Preceding basic probability assignment,Indicate that revised basic probability assignment, P (Θ) are expressed as the superset of θ, formula (5) and formula (6)
Indicate the specific formula of amendment basic probability assignment.The range of weight and confidence level is [0,1], and weight is the weight according to evidence
Degree is wanted to give in value range, confidence level is given in value range according to the Knowledge Capability of expert.
S142, revised basic probability assignment is merged according to rule of combination to obtain joint basic probability assignment.
In one embodiment, revised basic probability assignment is updated in rule of combination to obtain combining substantially general
Rate distribution.
Wherein,To combine basic probability assignment, formula (7) and formula (8) are rule of combination.
S143, using the maximum joint basic probability assignment as the air control conclusion of the risks and assumptions.
In one embodiment, joint basic probability assignment, the distribution of maximum joint probability are obtained according to rule of combination
Corresponding evaluation information is then final conclusion.For example, the basic probability assignment in table 1 is merged according to rule of combination.
Table 1:
Joint basic probability assignment is obtained according to rule of combinationAndWhereinNamelyValue it is maximum,Corresponding evaluation information is vehicle
There is no frauds for model B board, to it is concluded that be that vehicle model is B board there is no cheat.
Different evidences is merged through the above steps, different insurance experts is solved and same Claims Resolution case is done
Out the problem of different risk judgments.Each risks and assumptions obtains more accurate air control conclusion by air control method of settling a claim,
So that policymaker can carry out risk control to Claims Resolution case according to the risks and assumptions of all pinpoint accuracy, to improve
To the risk control accuracy of Claims Resolution case.
S150, the joint basic probability assignment is obtained using evidence theory according to the joint basic probability assignment
Section is trusted to characterize the confirmation degree of the corresponding evaluation information of the joint basic probability assignment.
In one embodiment, the belief function of joint basic probability assignment is found out according to evidence theory and likelihood function obtains
Trust section.Evidence theory is a kind of inference method of ability with processing uncertain information.Belief function indicates that evidence is
Genuine trusting degree, likelihood function indicate that evidence is the trusting degree of non-vacation, therefore be made of belief function and likelihood function
Trusting section can be used to indicate the confirmation degree to evidence.
Wherein, Bel (θ) is belief function, and Pl (θ) is likelihood function, is made of Bel (θ) and Pl (θ) and trusts section [Bel
(θ),Pl(θ)].For example, calculating joint basic probability assignment according to formula (9) and formula (10)Trust section be [0.55,
0.85], it is genuine trusting degree that 0.55 expression vehicle model, which is B board there is no fraud, and 0.85 expression vehicle model is that B board is not deposited
In the trusting degree that fraud is non-vacation, and 0.85-0.55=0.3 indicates that vehicle model be B board there is no cheating is uncertain
Trusting degree, 1-0.85=0.15 indicate that vehicle model is that there is no the trusting degrees that fraud is vacation for B board.
The embodiment of the present invention illustrates a kind of Claims Resolution air control method, by the risks and assumptions for obtaining Claims Resolution case；According to pre-
If vulnerability database determine the evaluation informations of the risks and assumptions；The degree of membership of the evaluation information is obtained using fuzzy theory
Function is normalized to obtain basic probability assignment；And the basic probability assignment is carried out according to rule of combination
The air control accuracy of each risks and assumptions can be improved to obtain the air control conclusions of risks and assumptions in fusion, and then improves Claims Resolution
The air control accuracy of case improves the controllability of air control cost, improves insurance service quality.
Fig. 6 is a kind of schematic block diagram of wind-controlling device of settling a claim provided in an embodiment of the present invention.As shown in fig. 6, corresponding to
The above Claims Resolution air control method, the present invention also provides a kind of Claims Resolution wind-controlling devices 200.The Claims Resolution wind-controlling device includes for executing
State the unit of Claims Resolution air control method, the device can be configured in desktop computer, tablet computer, laptop computer, etc. in terminals.Tool
Body, referring to Fig. 6, the Claims Resolution wind-controlling device 200 include acquiring unit 210, determination unit 220, normalization unit 230 and
Integrated unit 240.
Acquiring unit 210, for obtaining the risks and assumptions of Claims Resolution case, the risks and assumptions are for judging the Claims Resolution
Claims Resolution information of the case with the presence or absence of fraud possibility.
In one embodiment, Claims Resolution case is that insurer insurance risk occurs in scope of insurance coverage asks for guarantor to insurance company
The event of danger gold.For example, the event that car accident asks for insurance money to insurance company occurs for insurer；Or insurer's burst disease
Disease asks for the event of insurance money to insurance company.Insurance company first has to related to Claims Resolution case in processing Claims Resolution case
All Claims Resolution information are collected.In medical treatment Claims Resolution case, Claims Resolution information is for example including insurance policy, case history and medical invoice
Deng；For example including insurance policy, setting loss list, confirmation of responsibility book etc. in vehicle insurance Claims Resolution case.After obtaining Claims Resolution information, from Claims Resolution
It is obtained in information for judging to settle a claim case the risks and assumptions with the presence or absence of fraud, in medical treatment Claims Resolution case, risks and assumptions example
It such as include the drug inventory in idagnostic logout, inspection item and the medical invoice in case history；In vehicle insurance Claims Resolution case, wind
The dangerous factor be, for example, the vehicle model in insurance policy, the part of damage in setting loss list, in confirmation of responsibility book be in danger the time and
It is in danger place etc..
Determination unit 220, for determining the evaluation information of the risks and assumptions according to preset vulnerability database.
It in one embodiment, further include having the insurance preset risks and assumptions of expert and commenting in preset vulnerability database
The data acquisition system of corresponding relationship between valence information.After obtaining risks and assumptions, the risks and assumptions pair are searched from vulnerability database
The evaluation information answered so that it is determined that the risks and assumptions evaluation information.In the present embodiment, it there are three types of evaluation informations, respectively deposits
In fraud, there is no cheat and do not know with the presence or absence of fraud.For example, for vehicle model be B board risks and assumptions according to
Preset rules show that there is no the evaluation informations of fraud；For the time of being in danger be morning any risks and assumptions according to preset rules
Obtain the evaluation information in the presence of fraud；For be in danger risks and assumptions that place is national highway according to preset rules obtain it is uncertain whether
There are the evaluation informations of fraud.
Normalization unit 230, the subordinating degree function for obtaining the evaluation information using fuzzy theory are normalized
Processing is to obtain basic probability assignment.
In one embodiment, the value quantified due to evaluation information not instead of one, a fuzzy evaluation are taken advantage of as existed
Swindleness, there is no cheat and do not know not to be able to satisfy design conditions, it is therefore desirable to evaluation with the presence or absence of fuzzy evaluations such as frauds
Information is normalized to obtain quantized value.
In one embodiment, as shown in fig. 7, the normalization unit 230 includes subelement: construction unit 231, degree of membership
Unit 232 and basic probability assignment unit 233.
Construction unit 231, for constructing the subordinating degree function of the evaluation information using fuzzy theory.
In one embodiment, fuzzy theory refers to the reason of the basic conception for having used fuzzy set or continuous subordinating degree function
By.Subordinating degree function is the mathematical tool for characterizing fuzzy set, in order to describe element to a fuzzy set in subset
Membership, due to the fuzzy of this relationship, it will replace 0,1 this two value with the numerical value taken from section [0,1]
It describes, indicates that element belongs to " really degree " of certain fuzzy set.In the present embodiment, if Θ={ θ1、θ2、θ3…θn, Θ
For domain, evaluation information is indicated with the presence or absence of all possibilities of fraud, θ is fuzzy subset, θnFor n-th of fuzzy subset, and
Meet following condition: θ ∈ Θ, P (θ): → [0,1], then P (θ) is referred to as the subordinating degree function of evaluation information.Wherein domain Θ includes
θ1、θ2、θ3Three fuzzy subsets, θ1To there is the fuzzy subset of fraud, θ2For there is no the fuzzy subset of fraud, θ3It is uncertain
With the presence or absence of the fuzzy subset of fraud.So θ1、θ2、θ3Corresponding subordinating degree function is respectively as follows: P (θ1)、P(θ2)、P(θ3).Its
In, the specific formula of subordinating degree function is determined by method of expertise.Method of expertise is the practical experience according to expert
The processing formula or corresponding weight coefficient value for providing fuzzy message determine a kind of method of membership function.I.e. subordinating degree function be by
What insurance expert set up according to certain rules.
Degree of membership unit 232 obtains corresponding degree of membership by the subordinating degree function for the evaluation information.
In one embodiment, after determining subordinating degree function, evaluation information is input in subordinating degree function to obtain
To the degree of membership of the evaluation information.Degree of membership is the output valve of membership function, indicates that evaluation information belongs to some fuzzy subset's
Subjection degree.For example, the evaluation information that vehicle model is B board is input in subordinating degree function, P (θ is obtained1)=0.2, P (θ2)
=0.7, P (θ3)=0.1.Wherein, P (θ1)=0.2 indicates that the evaluation information that vehicle model is B board has the degree of membership cheated and is
0.2, P (θ2It is 0.7, P (θ that)=0.7, which indicates that the degree of membership of fraud is not present in the evaluation information that vehicle model is B board,3The table of)=0.1
Show that the uncertain degree of membership with the presence or absence of fraud of evaluation information that vehicle model is B board is 0.1.
Basic probability assignment unit 233, for using the degree of membership as basic probability assignment.
In one embodiment, Basic probability assignment function is constructed first, and Basic probability assignment function is each for distributing to
The function of proposition trusting degree, basic probability assignment (Basic Probability Assignment, abbreviation BPA) are substantially general
The output valve of rate partition function indicates the trusting degree to proposition.If identification framework is Θ, Θ={ θ1、θ2、θ3…θn, identification
Set of the frame representation to all possible answers of proposition, the one such possible answer of θ expression.For example, Θ is Claims Resolution case
The evaluation information set of the part risk factor, including there are three types of may θ1、θ2、θ3, θ1Indicate there is fraud, θ2Expression is not deposited
It is cheating, θ3Indicate uncertain with the presence or absence of fraud.Define m:2Θ→ [0,1], and meet following condition:
Wherein, 2ΘIndicate the set that all subsets of identification framework Θ collectively constitute, the referred to as power set of Θ, formula (1) table
The basic probability assignment for showing empty set is 0, and formula (2) indicates 2ΘThe sum of basic probability assignment of upper all elements is 1.It is above-mentioned when meeting
After condition, then claiming m is 2ΘOn Basic probability assignment function, be basic probability assignment that m (θ) is θ, indicate to the letter of proposition θ
Appoint degree.
It builds and obtained degree of membership is assigned to Basic probability assignment function after Basic probability assignment function obtains base
This probability assignments.
P (θ)=m (θ) (3)
For example, being to be assigned to basic probability assignment letter in the presence of the degree of membership cheated by the evaluation information that vehicle model is B board
It is 0.2 that number, which obtains basic probability assignment, obtains m (θ by formula (3)1)=P (θ1)=0.2, m (θ1) expression vehicle model be B board
It is 0.2 there are the trusting degree of fraud；It is that there is no the degrees of membership of fraud to be assigned to base by the evaluation information that vehicle model is B board
It is 0.7 that this probability distribution function, which obtains basic probability assignment, obtains m (θ by formula (3)2)=P (θ2)=0.7, m (θ2) indicate vehicle
Model B board is 0.7 there is no the trusting degree of fraud；It is uncertain whether deposit by the evaluation information that vehicle model is B board
Being assigned to Basic probability assignment function to obtain basic probability assignment in the degree of membership of fraud is 0.1, obtains m (θ by formula (3)3)
=P (θ3)=0.1, m (θ3) indicate that vehicle model is that B board does not know with the presence or absence of the trusting degree cheated to be 0.1.
Integrated unit 240, for according to rule of combination by the basic probability assignment carry out fusion to obtain risk because
The air control conclusion of son.
In one embodiment, rule of combination is the compositional rule that can represent the synergy between evidence.At this
In embodiment, Basic probability assignment function is the evidence in rule of combination, and basic probability assignment is expressed as the trust journey to evidence
Degree.For example, risks and assumptions are that vehicle model is B board in vehicle insurance Claims Resolution case, according to insurance expert 1 and insure expert's 2
The basic probability assignment m that the preset rules being set separately obtain evaluation information and obtain after normalized1(θ1)、m1
(θ2)、m1(θ3) and m2(θ1)、m2(θ2)、m2(θ3), m1(θ1) indicate insurance expert 1 to the letter that vehicle model is that B board has fraud
Appoint degree, m1(θ2) indicate that insurance expert 1 is that there is no the trusting degree of fraud, m for B board to vehicle model1(θ3) indicate that insurance is special
1 pair of vehicle model of family is the uncertain trusting degree with the presence or absence of fraud of B board；m2(θ1) indicate that insurance expert 2 is to vehicle model
There is the trusting degree of fraud, m in B board2(θ2) indicate that insurance expert 2 is that the trusting degree cheated is not present in B board to vehicle model,
m2(θ3) indicate that insurance expert 2 is the uncertain trusting degree with the presence or absence of fraud of B board to vehicle model.It will according to rule of combination
m1(θ1) and m2(θ1) merged, by m1(θ2) and m2(θ2) merged, by m1(θ3) and m2(θ3) merged, after fusion
To corresponding joint basic probability assignment, wherein fusion refers to obtaining using two evidences as being input in rule of combination
One process of joint basic probability assignment.The maximum basic allocation probability of joint indicates maximum to the trusting degree of evidence, because
This is using maximum joint basic probability assignment as the air control conclusion of risks and assumptions, for example, m1(θ2) and m2(θ2) fused
Elementary probability maximum is closed, then the air control conclusion that vehicle model is the risks and assumptions of B board is that there is no frauds.
In one embodiment, as shown in figure 8, the integrated unit 240 includes subelement: amending unit 241, assembled unit
242 and conclusion unit 243.
Amending unit 241, for being modified by modifying factor to the basic probability assignment.
In one embodiment, since the Knowledge Capability of each insurance expert is different, the significance level of each evidence is different, from
And evidence is made to possess different confidence level and weight.Evidence is modified to obtain by introducing modifying factor and is more accurately demonstrate,proved
According to.
Wherein, crw,iFor modifying factor, wiIndicate weight, riIndicate confidence level, i indicates i-th of evidence.mθ,iIndicate amendment
Preceding basic probability assignment,Indicate that revised basic probability assignment, P (Θ) are expressed as the superset of θ, formula (5) and formula (6)
Indicate the specific formula of amendment basic probability assignment.The range of weight and confidence level is [0,1], and weight is according to the important of evidence
Degree gives in value range, and confidence level gives in value range according to the Knowledge Capability of expert.
Assembled unit 242 obtains joint base for being merged revised basic probability assignment according to rule of combination
This probability assignments.
In one embodiment, revised basic probability assignment is updated in rule of combination to obtain combining substantially general
Rate distribution.
Wherein,To combine basic probability assignment, formula (7) and formula (8) are rule of combination.
Conclusion unit 243, for using the maximum joint basic probability assignment as the air control conclusion of the risks and assumptions.
In one embodiment, joint basic probability assignment, the distribution of maximum joint probability are obtained according to rule of combination
Corresponding evaluation information is then final conclusion.For example, the basic probability assignment in table 1 is merged according to rule of combination.
Table 1:
Joint basic probability assignment is obtained according to rule of combinationAndWhereinNamelyValue it is maximum,Corresponding evaluation information is vehicle
There is fraud in model B board, to it is concluded that be vehicle model be that there is no cheat for B board.
Different evidences is merged through the above steps, different insurance experts is solved and same Claims Resolution case is done
Out the problem of different risk judgments.Each risks and assumptions obtains more accurate air control conclusion by air control method of settling a claim,
So that policymaker can carry out risk control to Claims Resolution case according to the risks and assumptions of all pinpoint accuracy, to improve
To the risk control accuracy of Claims Resolution case.
The embodiment of the present invention illustrates a kind of Claims Resolution wind-controlling device, by the risks and assumptions for obtaining Claims Resolution case；According to pre-
If vulnerability database determine the evaluation informations of the risks and assumptions；The degree of membership of the evaluation information is obtained using fuzzy theory
Function is normalized to obtain basic probability assignment；And the basic probability assignment is carried out according to rule of combination
The air control accuracy of each risks and assumptions can be improved to obtain the air control conclusions of the risks and assumptions in fusion, and then improves
The air control accuracy for case of settling a claim improves the controllability of air control cost, improves insurance service quality.
Above-mentioned Claims Resolution wind-controlling device can be implemented as a kind of form of computer program, which can such as scheme
It is run in computer equipment shown in 9.
Referring to Fig. 9, Fig. 9 is a kind of schematic block diagram of computer equipment provided by the embodiments of the present application.The computer
Equipment 500 can be terminal, such as can be smart phone, tablet computer, laptop, desktop computer, individual digital and help
The electronic equipment with communication function such as reason and wearable device.
Refering to Fig. 9, which includes processor 502, memory and the net connected by system bus 501
Network interface 505, wherein memory may include non-volatile memory medium 503 and built-in storage 504.
The non-volatile memory medium 503 can storage program area 5031 and computer program 5032.The computer program
5032 are performed, and processor 502 may make to execute a kind of Claims Resolution air control method.
The processor 502 is for providing calculating and control ability, to support the operation of entire computer equipment 500.
The built-in storage 504 provides environment for the operation of the computer program 5032 in non-volatile memory medium 503, should
When computer program 5032 is executed by processor 502, processor 502 may make to execute a kind of Claims Resolution air control method.
The network interface 505 is used to carry out network communication with other equipment.It will be understood by those skilled in the art that in Fig. 9
The structure shown, only the block diagram of part-structure relevant to application scheme, does not constitute and is applied to application scheme
The restriction of computer equipment 500 thereon, specific computer equipment 500 may include more more or fewer than as shown in the figure
Component perhaps combines certain components or with different component layouts.
Wherein, the processor 502 is for running computer program 5032 stored in memory, to realize following step
It is rapid: to obtain the risks and assumptions of Claims Resolution case；The evaluation information of the risks and assumptions is determined according to preset vulnerability database；It utilizes
The subordinating degree function that fuzzy theory obtains the evaluation information is normalized to obtain basic probability assignment；And root
The basic probability assignment is subjected to fusion to obtain the air control conclusion of the air control factor according to rule of combination.
In one embodiment, processor 502 is when realizing the risks and assumptions step received in Claims Resolution case, tool
Body realizes following steps: obtaining the Claims Resolution information in Claims Resolution case from server；According to preset vulnerability database from the reason
It pays in information and extracts risks and assumptions.
In one embodiment, processor 502 described obtains the degree of membership of the evaluation information using fuzzy theory realizing
When function is normalized to obtain basic probability assignment step, following steps are also realized: being constructed using fuzzy theory
The subordinating degree function of the evaluation information；The evaluation information obtains corresponding degree of membership by the subordinating degree function；
Using the degree of membership as basic probability assignment.
In one embodiment, processor 502 described melts the basic probability assignment according to rule of combination realizing
When closing to obtain the air control conclusion step of risks and assumptions, following steps are also realized: by modifying factor to the elementary probability
Distribution is modified；Revised basic probability assignment is merged according to rule of combination to obtain joint basic probability assignment；
Using the maximum joint basic probability assignment as the air control conclusion of the risks and assumptions.
In one embodiment, processor 502 described melts the basic probability assignment according to rule of combination realizing
After air control conclusion step of the conjunction to obtain the risks and assumptions, following steps are also realized: according to the joint elementary probability
Distribution obtains the trust section of the joint basic probability assignment using evidence theory to characterize the joint elementary probability point
Confirmation degree with the corresponding evaluation information.
It should be appreciated that in the embodiment of the present application, processor 502 can be central processing unit (Central
Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic
Device, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor or
Person's processor is also possible to any conventional processor etc..
Those of ordinary skill in the art will appreciate that be realize above-described embodiment method in all or part of the process,
It is that relevant hardware can be instructed to complete by computer program.The computer program includes program instruction, computer journey
Sequence can be stored in a storage medium, which is computer readable storage medium.The program instruction is by the department of computer science
At least one processor in system executes, to realize the process step of the embodiment of the above method.
Therefore, the present invention also provides a kind of storage mediums.The storage medium can be computer readable storage medium.This is deposited
Storage media is stored with computer program, and wherein computer program includes program instruction.The program instruction makes when being executed by processor
Processor executes following steps: obtaining the risks and assumptions of Claims Resolution case；According to preset vulnerability database determine the risk because
The evaluation information of son；It is normalized to obtain base using the subordinating degree function that fuzzy theory obtains the evaluation information
This probability assignments；And the basic probability assignment is carried out to obtain the wind of the risks and assumptions by fusion according to rule of combination
Control conclusion.
In one embodiment, the processor realizes described receive in Claims Resolution case executing described program instruction
Risks and assumptions step when, be implemented as follows step: from server obtain Claims Resolution case in Claims Resolution information；According to preset
Vulnerability database extracts risks and assumptions from the Claims Resolution information.
In one embodiment, the processor realizes that the utilization fuzzy theory obtains institute in the instruction of execution described program
The subordinating degree function for stating evaluation information is normalized to be implemented as follows step thus when obtaining basic probability assignment step
It is rapid: the subordinating degree function of the evaluation information is constructed using fuzzy theory；The evaluation information is obtained by the subordinating degree function
To corresponding degree of membership；Using the degree of membership as basic probability assignment.
In one embodiment, the processor execute described program instruction and realize it is described will be described according to rule of combination
When basic probability assignment carries out air control conclusion step of the fusion to obtain the risks and assumptions, it is implemented as follows step: logical
Modifying factor is crossed to be modified the basic probability assignment；Revised basic probability assignment is melted according to rule of combination
Conjunction obtains joint basic probability assignment；Using the maximum joint basic probability assignment as the air control conclusion of the risks and assumptions.
In one embodiment, the processor execute described program instruction and realize it is described will be described according to rule of combination
After basic probability assignment carries out air control conclusion step of the fusion to obtain the risks and assumptions, following steps: root are also realized
The trust section of the joint basic probability assignment is obtained to characterize using evidence theory according to the joint basic probability assignment
The confirmation degree for combining the corresponding evaluation information of basic probability assignment.
The storage medium can be USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), magnetic disk
Or the various computer readable storage mediums that can store program code such as CD.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
In several embodiments provided by the present invention, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary.For example, the division of each unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation.Such as multiple units or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.
The steps in the embodiment of the present invention can be sequentially adjusted, merged and deleted according to actual needs.This hair
Unit in bright embodiment device can be combined, divided and deleted according to actual needs.In addition, in each implementation of the present invention
Each functional unit in example can integrate in one processing unit, is also possible to each unit and physically exists alone, can also be with
It is that two or more units are integrated in one unit.
If the integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product,
It can store in one storage medium.Based on this understanding, technical solution of the present invention is substantially in other words to existing skill
The all or part of part or the technical solution that art contributes can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, terminal or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (10)
1. a kind of Claims Resolution air control method characterized by comprising
The risks and assumptions of Claims Resolution case are obtained, the risks and assumptions are for judging that the Claims Resolution case may with the presence or absence of fraud
The Claims Resolution information of property；
The evaluation information of the risks and assumptions is determined according to preset vulnerability database, wherein being preset in the vulnerability database
Corresponding relationship between the risks and assumptions and evaluation information；
It is normalized to obtain elementary probability point using the subordinating degree function that fuzzy theory obtains the evaluation information
Match；And
The basic probability assignment is subjected to fusion to obtain the air control conclusion of the risks and assumptions according to rule of combination.
2. Claims Resolution air control method according to claim 1, which is characterized in that the risks and assumptions for obtaining Claims Resolution case,
Include:
The Claims Resolution information in Claims Resolution case is obtained from server；
Risks and assumptions are extracted from the Claims Resolution information according to preset vulnerability database.
3. Claims Resolution air control method according to claim 1, which is characterized in that described to obtain the evaluation using fuzzy theory
The subordinating degree function of information is normalized to obtain basic probability assignment, comprising:
The subordinating degree function of the evaluation information is constructed using fuzzy theory；
The evaluation information obtains corresponding degree of membership by the subordinating degree function；
Using the degree of membership as basic probability assignment.
4. Claims Resolution air control method according to claim 1, which is characterized in that it is described will be described substantially general according to rule of combination
Rate distribution carries out fusion to obtain the air control conclusion of the risks and assumptions, comprising:
The basic probability assignment is modified by modifying factor；
Revised basic probability assignment is merged according to rule of combination to obtain joint basic probability assignment；
Using the maximum joint basic probability assignment as the air control conclusion of the risks and assumptions.
5. Claims Resolution air control method according to claim 4, which is characterized in that further include:
The trust section of the joint basic probability assignment is obtained using evidence theory according to the joint basic probability assignment
To characterize the confirmation degree of the corresponding evaluation information of the joint basic probability assignment.
6. a kind of Claims Resolution wind-controlling device characterized by comprising
Acquiring unit, for obtaining the risks and assumptions of Claims Resolution case, the risks and assumptions are for judging that the Claims Resolution case is
The no Claims Resolution information with fraud；
Determination unit, for determining the evaluation information of the risks and assumptions according to preset vulnerability database, wherein the risk
The corresponding relationship being preset in database between the risks and assumptions and evaluation information；
Normalization unit is normalized to obtain using the subordinating degree function that fuzzy theory obtains the evaluation information
Basic probability assignment；And
Integrated unit, for the basic probability assignment to be carried out fusion to obtain the risks and assumptions according to rule of combination
Air control conclusion.
7. Claims Resolution wind-controlling device according to claim 6 characterized by comprising
Construction unit, for constructing the subordinating degree function of the evaluation information using fuzzy theory；
Degree of membership unit obtains corresponding degree of membership by the subordinating degree function for the evaluation information；
Basic probability assignment unit, for using the degree of membership as basic probability assignment.
8. Claims Resolution wind-controlling device according to claim 6 characterized by comprising
Amending unit, for being modified by modifying factor to the basic probability assignment；
Assembled unit obtains joint elementary probability point for being merged revised basic probability assignment according to rule of combination
Match；
Conclusion unit, for using the maximum joint elementary probability as the air control conclusion of the risks and assumptions.
9. a kind of computer equipment, which is characterized in that the computer equipment includes memory and processor, on the memory
It is stored with computer program, the processor is realized as described in any one of claim 1-5 when executing the computer program
Method.
10. a kind of storage medium, which is characterized in that the storage medium is stored with computer program, and the computer program is worked as
Method according to any one of claims 1 to 5 can be realized when being executed by processor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348669A (en) * | 2019-05-23 | 2019-10-18 | 中国平安财产保险股份有限公司 | Intelligent rules generation method, device, computer equipment and storage medium |
| CN110610431A (en) * | 2019-08-15 | 2019-12-24 | 中国平安人寿保险股份有限公司 | Intelligent claim settlement method and intelligent claim settlement system based on big data |
| CN112561713A (en) * | 2020-12-15 | 2021-03-26 | 中国人寿保险股份有限公司 | Method and device for anti-fraud recognition of claim settlement in insurance industry |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104125217A (en) * | 2014-06-30 | 2014-10-29 | 复旦大学 | Cloud data center real-time risk assessment method based on mainframe log analysis |
| CN105868912A (en) * | 2016-04-06 | 2016-08-17 | 清华大学 | Power transformer state evaluate method and apparatus based on data fusion |
| CN106022480A (en) * | 2016-05-13 | 2016-10-12 | 北京工业大学 | Robot functional module granularity division evaluating method based on D-S evidence theory |
| CN107240024A (en) * | 2017-05-22 | 2017-10-10 | 中国平安人寿保险股份有限公司 | The anti-fraud recognition methods of settlement of insurance claim and device |
| CN107871285A (en) * | 2017-12-06 | 2018-04-03 | 和金在线（北京）科技有限公司 | A kind of health insurance pays for the method for detecting and system of fraud and abuse |
| CN108256720A (en) * | 2017-11-07 | 2018-07-06 | 中国平安财产保险股份有限公司 | A kind of settlement of insurance claim methods of risk assessment and terminal device |
-
2018
- 2018-09-10 CN CN201811052447.8A patent/CN109409648A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104125217A (en) * | 2014-06-30 | 2014-10-29 | 复旦大学 | Cloud data center real-time risk assessment method based on mainframe log analysis |
| CN105868912A (en) * | 2016-04-06 | 2016-08-17 | 清华大学 | Power transformer state evaluate method and apparatus based on data fusion |
| CN106022480A (en) * | 2016-05-13 | 2016-10-12 | 北京工业大学 | Robot functional module granularity division evaluating method based on D-S evidence theory |
| CN107240024A (en) * | 2017-05-22 | 2017-10-10 | 中国平安人寿保险股份有限公司 | The anti-fraud recognition methods of settlement of insurance claim and device |
| CN108256720A (en) * | 2017-11-07 | 2018-07-06 | 中国平安财产保险股份有限公司 | A kind of settlement of insurance claim methods of risk assessment and terminal device |
| CN107871285A (en) * | 2017-12-06 | 2018-04-03 | 和金在线（北京）科技有限公司 | A kind of health insurance pays for the method for detecting and system of fraud and abuse |
Non-Patent Citations (3)
| Title |
|---|
| 梁静国 等: "基于证据理论的风险投资项目风险评价", 科技进步与对策, no. 12, 31 December 2005 (2005-12-31), pages 104 - 107 * |
| 闫丽颖 等: "基于改进D-S证据理论的工程项目风险评价模型", 建筑技术开发, vol. 40, no. 10, 31 October 2013 (2013-10-31), pages 69 - 72 * |
| 韩芬: "多传感器信息融合目标识别***设计", 电子制作, no. 16, 31 August 2013 (2013-08-31), pages 125 - 126 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348669A (en) * | 2019-05-23 | 2019-10-18 | 中国平安财产保险股份有限公司 | Intelligent rules generation method, device, computer equipment and storage medium |
| CN110348669B (en) * | 2019-05-23 | 2023-08-22 | 中国平安财产保险股份有限公司 | Intelligent rule generation method, intelligent rule generation device, computer equipment and storage medium |
| CN110610431A (en) * | 2019-08-15 | 2019-12-24 | 中国平安人寿保险股份有限公司 | Intelligent claim settlement method and intelligent claim settlement system based on big data |
| CN110610431B (en) * | 2019-08-15 | 2023-10-27 | 中国平安人寿保险股份有限公司 | Intelligent claim settlement method and intelligent claim settlement system based on big data |
| CN112561713A (en) * | 2020-12-15 | 2021-03-26 | 中国人寿保险股份有限公司 | Method and device for anti-fraud recognition of claim settlement in insurance industry |
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