CN112001658A - Method and device for generating vehicle insurance quotation - Google Patents
Method and device for generating vehicle insurance quotation Download PDFInfo
- Publication number
- CN112001658A CN112001658A CN202010911072.7A CN202010911072A CN112001658A CN 112001658 A CN112001658 A CN 112001658A CN 202010911072 A CN202010911072 A CN 202010911072A CN 112001658 A CN112001658 A CN 112001658A
- Authority
- CN
- China
- Prior art keywords
- risk
- coefficient
- target information
- value
- insurance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012545 processing Methods 0.000 claims abstract description 37
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 238000004590 computer program Methods 0.000 claims description 16
- 238000012502 risk assessment Methods 0.000 abstract description 7
- 238000007405 data analysis Methods 0.000 abstract 1
- 230000000875 corresponding effect Effects 0.000 description 89
- 230000006870 function Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 9
- 230000006854 communication Effects 0.000 description 8
- 239000000047 product Substances 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 7
- 208000031968 Cadaver Diseases 0.000 description 6
- 238000004891 communication Methods 0.000 description 6
- 230000005856 abnormality Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 230000007423 decrease Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000010485 coping Effects 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000012552 review Methods 0.000 description 2
- 239000013589 supplement Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Technology Law (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The embodiment of the invention provides a vehicle insurance quotation generation method relating to the technical field of insurance policy data processing, aiming at the insurance industry, and the method comprises the following steps: acquiring target information; analyzing the risk degree of the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient; and generating the vehicle insurance quotation according to the insurance coefficient. According to the invention, the risk factor and the risk degree analysis rule are set to carry out online risk analysis on the target information, so that the vehicle insurance quotation can be rapidly carried out, and the insurance policy business transaction efficiency is improved; and the data analysis is carried out on the service data through the set sensitive risk value section, the low risk compensation value section and the high risk compensation value section, so that the car insurance quotation can be accurately carried out.
Description
Technical Field
The invention relates to the technical field of insurance policy data processing, in particular to a vehicle insurance quotation generation method and a vehicle insurance quotation generation device.
Background
Automobile insurance is one of property insurance, which is produced and developed along with the appearance and popularity of automobiles. To date, purchasing automobile insurance for automobiles has become a standard for automobile owners, and the standard brings a huge insurance market. In a huge insurance market, the working efficiency of the business model and the accuracy of the risk assessment influence the share of the insurance market. Therefore, the efficiency of the business model and the accuracy of the risk assessment are much more of a concern.
The original motorcade business mode is mainly based on offline communication, and communication or bidding is carried out by a salesman and an enterprise. In the negotiation process of the salesman and the client, the underwriter needs to repeatedly confirm to give the quotation and discount, so that a large amount of time communication cost is wasted, certain influence is caused on the aspect of the client, and the ticket of the company is easily taken. Moreover, the target information obtained by the service staff is relatively fuzzy, and only the overall summary information can be obtained, so that each actual target is lack of understanding. In the case of lack of information, the risk assessment for the entire fleet may be inaccurate, ultimately resulting in discounts and costs not being justified.
Therefore, the original business mode completely depends on the industry experience of the underwriter, and the quotation efficiency is not high; and the staff is also difficult to obtain complete target information, which easily causes inaccurate risk assessment.
Disclosure of Invention
In view of the above problems, embodiments of the present invention are provided to provide a vehicle insurance quotation generating method and a corresponding vehicle insurance quotation generating device that overcome or at least partially solve the above problems.
In order to solve the above problems, an embodiment of the present invention discloses a method for generating car insurance quotation, including:
acquiring target information, wherein the target information comprises target information with unique identity attribute;
analyzing the risk degree of the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient;
and generating the vehicle insurance quotation according to the insurance coefficient.
Optionally, the obtaining of the target information, where the target information includes the target information with a unique identity attribute, includes:
acquiring a correlation instruction;
and associating the target information with the unique identity attribute with the corresponding other target information.
Optionally, the performing risk degree analysis on the risk value and the risk coefficient of the target information according to a preset rule to obtain a corresponding risk factor includes:
determining a risk factor and a risk value corresponding to the risk factor according to the target information;
obtaining a corresponding risk coefficient according to the corresponding relation between the type of the preset risk factor and the risk coefficient;
processing the risk value and the risk coefficient according to a preset rule to generate a risk degree value;
and obtaining a corresponding risk coefficient according to the risk degree value.
Optionally, after determining the risk factor and the risk value corresponding to the risk factor according to the target information, the method includes:
and presetting a sensitive risk value segment, and performing offline wind for the current targeted information when the risk value is in the preset sensitive risk value segment.
Optionally, after obtaining the corresponding risk factor according to the risk degree value, the method includes:
performing corresponding processing according to the value section of the risk coefficient in a preset risk coefficient table;
if the risk coefficient is located in a preset high risk compensation value section, carrying out no-price quotation or high-price quotation processing;
automatically discounting or/and providing a gift if the risk-offering coefficient is located in a preset low-risk compensation value section;
and if the risk factor is positioned between the high risk compensation value section and the low risk compensation value section, normally quoting.
Optionally, the performing risk degree analysis on the risk value and the risk coefficient of the target information according to a preset rule to obtain a corresponding risk factor includes:
and when the risk degree analysis is not clear, performing offline wind compensation on the current targeted information.
Optionally, the generating of the vehicle insurance offer according to the insurance coefficient includes:
obtaining an initial price according to the corresponding relation between the preset insurance type and the initial price;
and generating the vehicle insurance quotation according to the insurance coefficient and the initial price.
The embodiment of the invention also discloses a device for generating the car insurance quotation, which comprises the following steps:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring target information which comprises target information with unique identity attribute;
the analysis module is used for carrying out risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule so as to obtain a corresponding risk coefficient;
and the generating module is used for generating the vehicle insurance quotation according to the insurance coefficient.
The embodiment of the invention also discloses electronic equipment which comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein the computer program realizes the steps of the car insurance quotation generating method when being executed by the processor.
The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the steps of the vehicle insurance quotation generating method when being executed by a processor.
The embodiment of the invention has the following advantages: risk factors and risk degree analysis rules are set to carry out online risk analysis on the target information, so that the car insurance quotation can be carried out quickly; the set sensitive risk value section can greatly reduce the insurance policy reimbursement quantity, and the set low risk compensation value section and the set high risk compensation value section respectively promote the entry and exit of the insurance policy and reduce the reimbursement rate; and the target business with indefinite risk identification or higher risk is subjected to offline wind, so that the policy can be subjected to targeted processing; and a counter-offer coping mechanism is also established so as to cope with clients with low intention, clients accustomed to counter-offer or clients with large single quantity, thereby increasing the success probability of the service.
Drawings
FIG. 1 is a flow chart of steps of a method for generating a vehicle insurance quote in accordance with the present invention;
FIG. 2 is a flow chart of the steps of information association of a vehicle insurance quote generation method object of the present invention;
FIG. 3 is a flow chart of steps for obtaining an insurance coefficient according to an insurance quote generation method of the present invention;
FIG. 4 is a flowchart of the steps of a method for generating a car insurance quote to determine a risk value according to the present invention;
FIG. 5 is a flowchart of the steps of determining an insurance coefficient for a vehicle insurance quote generation method of the present invention;
FIG. 6 is a flow chart of the steps of determining wind speed according to the present invention;
FIG. 7 is a flowchart illustrating steps of a method for generating an insurance quote according to the present invention;
fig. 8 is a block diagram showing the construction of an insurance quotation generating apparatus according to the present invention;
fig. 9 is an electronic device implementing the car insurance quote generating method of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
One of the core ideas of the embodiment of the invention is that the risk factor and the risk degree analysis rule are set to carry out online risk analysis on the target information, so that the vehicle insurance quotation can be rapidly carried out; the set sensitive risk value section can greatly reduce the insurance policy reimbursement quantity, and the set low risk compensation value section and the set high risk compensation value section respectively promote the entry and exit of the insurance policy and reduce the reimbursement rate; and the target business with indefinite risk identification or higher risk is subjected to offline wind, so that the policy can be subjected to targeted processing; and a counter-offer coping mechanism is also established so as to cope with clients with low intention, clients accustomed to counter-offer or clients with large single quantity, thereby increasing the success probability of the service.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a method for generating a car insurance quotation according to the present invention is shown, and specifically, the method may include the following steps:
s100, obtaining target information, wherein the target information comprises target information with unique identity attribute;
s200, carrying out risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient;
and S300, generating the vehicle insurance quotation according to the insurance coefficient.
S100, obtaining target information, wherein the target information comprises target information with unique identity attribute; the target information refers to the information of vehicles to be guaranteed by customers, and comprises information of automobile price, automobile type, brand, driving age, previous insurance policy information, corresponding target frame number, license plate number and the like. The target information can be collected by a service staff in advance and acquired by knowing the general view of a client fleet from multiple aspects in the communication process with the client; after the target information is obtained, the staff inputs the target information into the system, and the system stores the target information. Or the data can be input into the system on site in the process of communication between the service personnel and the client.
S200, carrying out risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient; the method comprises the steps of establishing a corresponding relation between the risk degree of the risk factor and a risk value through presetting the risk factor, and generating a corresponding risk coefficient according to the risk value through obtaining a specific risk value.
S300, generating an automobile insurance quotation according to the insurance coefficient; and calculating the risk coefficient and the corresponding insurance policy initial price to obtain a calculation result which is the current vehicle insurance quotation.
Referring to fig. 2, the acquiring target information, where the target information includes target information with unique identity attribute, includes:
s110, acquiring a correlation instruction;
and S120, associating the target information with the unique identity attribute with other corresponding target information.
The method comprises the steps of obtaining an instruction for associating the target information with the unique identity attribute with other corresponding target information, and associating the information with the unique identity attribute with other information with non-unique identity attribute in each item of target information stored in a system to establish a target library. By associating the target information with the unique identity attribute with the corresponding other target information, the associated other target information can be inquired by inputting the target information with the unique identity attribute into the system. For example: the information such as the frame number or the license plate number of each vehicle is correlated with the information such as the price, the vehicle type, the brand, the driving age, the past warranty information and the like of each vehicle, and a service person can inquire other information under the information such as the frame number or the license plate number by inputting the information such as the frame number or the license plate number and the like so as to check and update the target information at the later stage.
Referring to fig. 3, the performing risk degree analysis on the risk value and the risk coefficient of the target information according to the preset rule to obtain a corresponding risk factor includes:
s210, determining a risk factor and a risk value corresponding to the risk factor according to the target information;
s220, obtaining a corresponding risk coefficient according to the corresponding relation between the type of the preset risk factor and the risk coefficient;
s230, processing the risk value and the risk coefficient according to a preset rule to generate a risk degree value;
and S240, obtaining a corresponding risk coefficient according to the risk degree value.
And determining the risk factors after matching in the preset risk factors according to the target information. The risk factor is a factor that measures the degree of risk of a subject. The risk factor is target information with non-unique identity, such as: the price of the automobile, the type of the automobile, the brand, the driving age, the information of the previous insurance policy and other target information. And acquiring the risk values of the risk factors according to the corresponding relationship between the risk degrees of the risk factors and the risk values, wherein the corresponding relationship between the risk degrees of different risk factors and the risk values is different. For example: the higher the car price, the smaller the risk value, and the lower the car price, the larger the risk value; the longer the driving life, the greater the risk value, and the shorter the driving life, the smaller the risk value.
And acquiring the risk coefficients of the different types of risk factors according to the corresponding relation between the types of the risk factors and the risk coefficients so as to improve the evaluation precision of the risk degree. This is because the different risk factors have different degrees of risk, and the greater the importance of the risk factor, the greater the degree of risk. For example: the influence of the price and the driving age of the vehicle on the risk degree is large, and the risk coefficient values of the price and the driving age of the vehicle can be set to be large; the influence of the type and the brand of the vehicle on the risk degree is small, and the risk coefficient value of the type and the brand can be set to be small.
And (4) performing product calculation on the risk values and the corresponding risk coefficients, and then summing products of the risk values and the corresponding risk coefficients to obtain a risk degree value. And matching the calculated risk coefficient in the corresponding relation between the risk degree value and the risk coefficient to obtain the risk coefficient according to the corresponding relation between the risk degree value and the risk coefficient.
Referring to fig. 4, after determining the risk factor and the risk value corresponding to the risk factor according to the target information, the method includes:
s211, presetting a sensitive risk value segment, and performing offline wind compensation on the current target information when the risk value is in the preset sensitive risk value segment.
The preset sensitive risk value segment is located in the area with the maximum risk value in the corresponding relation between the risk degree of the risk factor and the risk value, and the range of the sensitive risk value segment can be set according to the actual situation, wherein the actual situation comprises the risk loss rate, the importance degree of the risk factor and the type of insurance. The risk factors with sensitive risk value segments preset in the corresponding relation between the risk degree of the risk factors and the risk values comprise driving age and historical policy information. And if the risk value of at least one risk factor of the target information is located in the sensitive risk value section, performing offline wind compensation on the target information. This is because the risk value of a single risk factor is too large, which will greatly increase the risk loss rate of the subject service, or the risk factor necessarily leads to the risk loss; for example, when the driving age of the automobile insured by the customer is too high or is about to exceed the scrapped age, the risk value of the driving age is definitely large, and the occurrence probability of an accident is also inevitably increased greatly; for another example: if a plurality of insurance policy pay records exist on the past insurance policy information of the client, the driving behavior of the client is expected to have great abnormality or the possibility of cheating insurance; therefore, this step can avoid bearing a large risk of meaningless reimbursement, greatly reducing the amount of policy reimbursement.
Referring to fig. 5, after obtaining the corresponding risk factor according to the risk degree value, the method includes:
s241, performing corresponding processing according to the risk coefficient in a numerical value section of a preset risk coefficient table;
if the risk coefficient is located in a preset high risk compensation value section, carrying out no-price quotation or high-price quotation processing;
automatically discounting or/and providing a gift if the risk-offering coefficient is located in a preset low-risk compensation value section;
and if the risk factor is positioned between the high risk compensation value section and the low risk compensation value section, normally quoting.
In order to improve the list rate and reduce the odds ratio, corresponding processing is carried out according to the position of the calculated risk coefficient in a preset risk coefficient table. And positioning the risk coefficient in the target information of the preset low-risk compensation value segment, and giving a discount or providing a gift at the same time of quotation. Specifically, when the risk coefficient is positioned in the upper half of the preset low risk compensation value section, giving a plurality of gifts for selection; when the risk factor is positioned in the lower half section of the preset low risk compensation value section, giving a discount; and when the risk giving coefficient is the lowest value of the preset low risk compensation value section, giving out which discount is offered as a gift. And performing non-quotation or high-quotation processing on the target information of which the risk-leaving coefficient is positioned in the preset high-risk compensation numerical value section. Specifically, when the risk factor is located in the upper half of the preset high risk compensation value section, no quotation processing is carried out; and when the risk coefficient is positioned in the lower half section of the preset high risk compensation value section, performing high quotation processing. And when the risk-taking coefficient is positioned between the preset high-risk compensation value section and the preset low-risk compensation value section, normally quoting. The preset low-risk compensation value section is located in the lowest risk coefficient value section in the preset risk coefficient table, and the preset high-risk compensation value section is located in the highest risk coefficient value section in the preset risk coefficient table. The ranges of the preset low-risk compensation value segment and the preset high-risk compensation value segment can be set according to actual conditions, wherein the actual conditions comprise the risk loss rate, the current traffic and the dangerous seeds.
Referring to fig. 6, the performing risk degree analysis on the risk value and the risk coefficient of the target information according to the preset rule to obtain a corresponding risk factor includes:
and S250, performing offline wind compensation on the current target information when the risk degree analysis is not clear.
When risk degree analysis is carried out, if target information of which the risk degree value is not obtained exists, the risk degree analysis of the current target information is ambiguous, and offline wind compensation is carried out on the current target information. The cases where risk identification is ambiguous include target information insufficiency and risk identification abnormality. When the risk degree analysis of the existing target information is not clear, the system dispatches wind-corps to the current target information, and the wind-corps immediately perform off-line wind-corps after receiving the dispatch from the system. When the wind can be off-line, the wind can review the target information manually or/and supplement the target information to analyze the risk degree again. Therefore, the target information with unclear risk identification can be processed in time, the condition that no price is quoted when the business staff negotiates with the client is avoided, and the whole price quotation process is more complete.
Referring to fig. 7, the generating of the car insurance offer according to the risk factor includes:
s310, obtaining an initial price according to the corresponding relation between the preset insurance type and the initial price;
and S320, generating the vehicle insurance quotation according to the insurance coefficient and the initial price.
And obtaining an initial price corresponding to the current insurance category according to the corresponding relation between the preset insurance category and the initial price of the insurance, and multiplying the obtained insurance coefficient and the initial price to obtain a product, namely the current vehicle insurance quoted price.
In one embodiment, after the generating the vehicle insurance quote according to the insurance coefficient, the method comprises the following steps: acquiring a counter-offer instruction; and performing automatic discount according to the risk degree value of the target information or/and the automatic discount times or/and the target amount value. In order to deal with clients with little intention, clients accustomed to counter-offer or clients with a large number of single-volume, so as to increase the service success probability, after the counter-offer instruction is obtained, the price needing counter-offer is automatically discounted according to the risk degree value or/and the automatic discount times or/and the price of the price. Matching a risk degree coefficient in a corresponding relation between the risk degree coefficient and the risk degree coefficient according to the calculated risk degree value, matching an automatic discount time coefficient in a corresponding relation between the automatic discount time and the automatic discount time coefficient according to the automatic discount time, matching an objective volume discount price in a corresponding relation between the objective volume value and the objective volume discount price according to the objective volume value, multiplying the risk degree coefficient and the objective volume value to obtain a risk degree price, and multiplying the automatic discount time coefficient and the objective volume value to obtain an automatic discount time price; and finally, the sum of the previous quote value and the risk degree price, the automatic discount time price and the bid amount discount price is subtracted, so that the current quote is obtained.
Wherein, the automatically discounting according to the risk degree value or/and the automatically discounting times or/and the target amount value of the target information specifically comprises: obtaining a risk degree coefficient according to the risk degree value, and generating a risk degree price according to the risk degree coefficient and a nominal amount value; obtaining an automatic discount time coefficient according to the automatic discount time, and generating an automatic discount time price according to the automatic discount time coefficient and a target volume value; acquiring the price of the target amount discount according to the value of the target amount; and obtaining automatic discount according to the risk degree price, the automatic discount time price and the target amount discount price.
Specifically, a risk degree coefficient is matched in the corresponding relation between the risk degree value and the risk degree coefficient according to the calculated risk degree value, and a product is calculated according to the risk degree coefficient and the target quota value to generate a risk degree price. And matching an automatic discount time coefficient in the corresponding relation between the automatic discount time and the automatic discount time coefficient according to the automatic discount time, and performing quadrature generation according to the automatic discount time coefficient and the target volume value to generate an automatic discount time price. And matching the target amount discount price in the corresponding relation between the target amount value and the target amount discount price according to the target amount value. And finally, the sum of the previous quote value and the risk degree price, the automatic discount time price and the bid amount discount price is subtracted, so that the current quote is obtained.
The risk degree coefficient is set according to the risk degree; specifically, the greater the risk level, the smaller the risk level coefficient. The risk degree and the risk degree coefficient can be in various linear correlations and can be specifically set according to actual conditions; in one embodiment, the risk degree is linearly related to the risk degree coefficient in a binary function, namely: the risk degree coefficient decreases faster as the risk degree increases. The automatic discount time coefficient is set according to the automatic discount time; specifically, the more the number of automatic discounts, the smaller the coefficient of the number of automatic discounts. The automatic discount times and the automatic discount time coefficient can be in various linear correlations and can be specifically set according to actual conditions; in one embodiment, the auto-discount time and the auto-discount time coefficient are linearly related by a binary function, that is: the more rapidly the auto-discount number coefficient decreases as the auto-discount number increases. The target amount value and the target amount discount price are correspondingly set according to the target amount value; specifically, the larger the amount value of the target, the larger the amount offer price of the target. The amount value of the target and the discount price of the target amount can be in various linear correlations and can be set according to actual conditions; in one embodiment, the target amount value and the target amount offer are linearly related by a binary function, namely: the bid amount offer price increases faster as the bid amount value increases.
In another embodiment, the generating the vehicle insurance quote according to the insurance coefficient further comprises: and when the quotation times exceed a preset value, performing offline wind for the current targeted information. In order to avoid unlimited price reduction for business personnel who do not know quotations to promote the order and improve the order probability under the targeted assistance and suggestion of the wind exploration personnel, when the number of repeated quotations exceeds the preset number, the targeted assignment wind exploration personnel who currently conduct quotations is manually checked, namely: the wind surveying staff gives final quotation according to the current actual conditions, and the actual conditions comprise: the importance of the customer, the current overall performance, the profitability of the current business and the intention of the customer. It can be seen that not only can the bottom line of the price be preserved but also the exo-assistance can be obtained immediately.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 8, a block diagram of a structure of an embodiment of the vehicle insurance quotation generating device of the present invention is shown, and may specifically include the following modules:
an obtaining module 100, configured to obtain target information, where the target information includes target information with a unique identity attribute;
the analysis module 200 is configured to perform risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk factor;
and the generating module 300 is configured to generate the car insurance quotation according to the insurance coefficient.
An obtaining module 100, configured to obtain target information, where the target information includes target information with a unique identity attribute; the target information refers to the information of vehicles to be guaranteed by customers, and comprises information of automobile price, automobile type, brand, driving age, previous insurance policy information, corresponding target frame number, license plate number and the like. The target information can be collected by a service staff in advance and acquired by knowing the general view of a client fleet from multiple aspects in the communication process with the client; after the target information is obtained, the staff inputs the target information into the system, and the system stores the target information. Or the data can be input into the system on site in the process of communication between the service personnel and the client.
The analysis module 200 is configured to perform risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk factor; the method comprises the steps of establishing a corresponding relation between the risk degree of the risk factor and a risk value through presetting the risk factor, and generating a corresponding risk coefficient according to the risk value through obtaining a specific risk value.
The generating module 300 is used for generating the vehicle insurance quotation according to the insurance coefficient; and calculating the risk coefficient and the corresponding insurance policy initial price to obtain a calculation result which is the current vehicle insurance quotation.
The acquisition module 100 includes:
the instruction acquisition module is used for acquiring the association instruction;
and the association module is used for associating the target information with the unique identity attribute with the corresponding other target information.
The method comprises the steps of obtaining an instruction for associating the target information with the unique identity attribute with other corresponding target information, and associating the information with the unique identity attribute with other information with non-unique identity attribute in each item of target information stored in a system to establish a target library. By associating the target information with the unique identity attribute with the corresponding other target information, the associated other target information can be inquired by inputting the target information with the unique identity attribute into the system. For example: the information such as the frame number or the license plate number of each vehicle is correlated with the information such as the price, the vehicle type, the brand, the driving age, the past warranty information and the like of each vehicle, and a service person can inquire other information under the information such as the frame number or the license plate number by inputting the information such as the frame number or the license plate number and the like so as to check and update the target information at the later stage.
The generating module 300 includes:
the determining module is used for determining a risk factor and a risk value corresponding to the risk factor according to the target information;
the first corresponding module is used for obtaining a corresponding risk coefficient according to the corresponding relation between the type of the preset risk factor and the risk coefficient;
the processing module is used for processing the risk value and the risk coefficient according to a preset rule to generate a risk degree value;
and the second corresponding module is used for obtaining a corresponding risk coefficient according to the risk degree value.
And determining the risk factors after matching in the preset risk factors according to the target information. The risk factor is a factor that measures the degree of risk of a subject. The risk factor is target information with non-unique identity, such as: the price of the automobile, the type of the automobile, the brand, the driving age, the information of the previous insurance policy and other target information. And acquiring the risk values of the risk factors according to the corresponding relationship between the risk degrees of the risk factors and the risk values, wherein the corresponding relationship between the risk degrees of different risk factors and the risk values is different. For example: the higher the car price, the smaller the risk value, and the lower the car price, the larger the risk value; the longer the driving life, the greater the risk value, and the shorter the driving life, the smaller the risk value.
And acquiring the risk coefficients of the different types of risk factors according to the corresponding relation between the types of the risk factors and the risk coefficients so as to improve the evaluation precision of the risk degree. This is because the different risk factors have different degrees of risk, and the greater the importance of the risk factor, the greater the degree of risk. For example: the influence of the price and the driving age of the vehicle on the risk degree is large, and the risk coefficient values of the price and the driving age of the vehicle can be set to be large; the influence of the type and the brand of the vehicle on the risk degree is small, and the risk coefficient value of the type and the brand can be set to be small.
And (4) performing product calculation on the risk values and the corresponding risk coefficients, and then summing products of the risk values and the corresponding risk coefficients to obtain a risk degree value. And matching the calculated risk coefficient in the corresponding relation between the risk degree value and the risk coefficient to obtain the risk coefficient according to the corresponding relation between the risk degree value and the risk coefficient.
The determining module comprises:
and the first judgment module is used for presetting the sensitive risk value section, and performing offline wind compensation on the current targeted information when the risk value is in the preset sensitive risk value section.
The preset sensitive risk value segment is located in the area with the maximum risk value in the corresponding relation between the risk degree of the risk factor and the risk value, and the range of the sensitive risk value segment can be set according to the actual situation, wherein the actual situation comprises the risk loss rate, the importance degree of the risk factor and the type of insurance. The risk factors with sensitive risk value segments preset in the corresponding relation between the risk degree of the risk factors and the risk values comprise driving age and historical policy information. And if the risk value of at least one risk factor of the target information is located in the sensitive risk value section, performing offline wind compensation on the target information. This is because the risk value of a single risk factor is too large, which will greatly increase the risk loss rate of the subject service, or the risk factor necessarily leads to the risk loss; for example, when the driving age of the automobile insured by the customer is too high or is about to exceed the scrapped age, the risk value of the driving age is definitely large, and the occurrence probability of an accident is also inevitably increased greatly; for another example: if a plurality of insurance policy pay records exist on the past insurance policy information of the client, the driving behavior of the client is expected to have great abnormality or the possibility of cheating insurance; therefore, this step can avoid bearing a large risk of meaningless reimbursement, greatly reducing the amount of policy reimbursement.
The second corresponding module comprises:
the second judgment module is used for performing corresponding processing on the numerical value section of the risk coefficient in a preset risk coefficient table according to the risk coefficient;
if the risk coefficient is located in a preset high risk compensation value section, carrying out no-price quotation or high-price quotation processing;
automatically discounting or/and providing a gift if the risk-offering coefficient is located in a preset low-risk compensation value section;
and if the risk factor is positioned between the high risk compensation value section and the low risk compensation value section, normally quoting.
In order to improve the list rate and reduce the odds ratio, corresponding processing is carried out according to the position of the calculated risk coefficient in a preset risk coefficient table. And positioning the risk coefficient in the target information of the preset low-risk compensation value segment, and giving a discount or providing a gift at the same time of quotation. Specifically, when the risk coefficient is positioned in the upper half of the preset low risk compensation value section, giving a plurality of gifts for selection; when the risk factor is positioned in the lower half section of the preset low risk compensation value section, giving a discount; and when the risk giving coefficient is the lowest value of the preset low risk compensation value section, giving out which discount is offered as a gift. And performing non-quotation or high-quotation processing on the target information of which the risk-leaving coefficient is positioned in the preset high-risk compensation numerical value section. Specifically, when the risk factor is located in the upper half of the preset high risk compensation value section, no quotation processing is carried out; and when the risk coefficient is positioned in the lower half section of the preset high risk compensation value section, performing high quotation processing. And when the risk-taking coefficient is positioned between the preset high-risk compensation value section and the preset low-risk compensation value section, normally quoting. The preset low-risk compensation value section is located in the lowest risk coefficient value section in the preset risk coefficient table, and the preset high-risk compensation value section is located in the highest risk coefficient value section in the preset risk coefficient table. The ranges of the preset low-risk compensation value segment and the preset high-risk compensation value segment can be set according to actual conditions, wherein the actual conditions comprise the risk loss rate, the current traffic and the dangerous seeds.
The analysis module 200 includes:
and the judging module is used for performing offline wind compensation on the current targeted information when the risk degree analysis is ambiguous.
When risk degree analysis is carried out, if target information of which the risk degree value is not obtained exists, the risk degree analysis of the current target information is ambiguous, and offline wind compensation is carried out on the current target information. The cases where risk identification is ambiguous include target information insufficiency and risk identification abnormality. When the risk degree analysis of the existing target information is not clear, the system dispatches wind-corps to the current target information, and the wind-corps immediately perform off-line wind-corps after receiving the dispatch from the system. When the wind can be off-line, the wind can review the target information manually or/and supplement the target information to analyze the risk degree again. Therefore, the target information with unclear risk identification can be processed in time, the condition that no price is quoted when the business staff negotiates with the client is avoided, and the whole price quotation process is more complete.
The generating module 300 includes:
the price acquisition module is used for acquiring an initial price according to the corresponding relation between the preset insurance type and the initial price;
and the quotation generation module is used for generating the vehicle insurance quotation according to the insurance coefficient and the initial price.
And obtaining an initial price corresponding to the current insurance category according to the corresponding relation between the preset insurance category and the initial price of the insurance, and multiplying the obtained insurance coefficient and the initial price to obtain a product, namely the current vehicle insurance quoted price.
In one embodiment, after the generating the vehicle insurance quote according to the insurance coefficient, the method comprises the following steps: acquiring a counter-offer instruction; and performing automatic discount according to the risk degree value of the target information or/and the automatic discount times or/and the target amount value. In order to deal with clients with little intention, clients accustomed to counter-offer or clients with a large number of single-volume, so as to increase the service success probability, after the counter-offer instruction is obtained, the price needing counter-offer is automatically discounted according to the risk degree value or/and the automatic discount times or/and the price of the price. Matching a risk degree coefficient in a corresponding relation between the risk degree coefficient and the risk degree coefficient according to the calculated risk degree value, matching an automatic discount time coefficient in a corresponding relation between the automatic discount time and the automatic discount time coefficient according to the automatic discount time, matching an objective volume discount price in a corresponding relation between the objective volume value and the objective volume discount price according to the objective volume value, multiplying the risk degree coefficient and the objective volume value to obtain a risk degree price, and multiplying the automatic discount time coefficient and the objective volume value to obtain an automatic discount time price; and finally, the sum of the previous quote value and the risk degree price, the automatic discount time price and the bid amount discount price is subtracted, so that the current quote is obtained.
Wherein, the automatically discounting according to the risk degree value or/and the automatically discounting times or/and the target amount value of the target information specifically comprises: obtaining a risk degree coefficient according to the risk degree value, and generating a risk degree price according to the risk degree coefficient and a nominal amount value; obtaining an automatic discount time coefficient according to the automatic discount time, and generating an automatic discount time price according to the automatic discount time coefficient and a target volume value; acquiring the price of the target amount discount according to the value of the target amount; and obtaining automatic discount according to the risk degree price, the automatic discount time price and the target amount discount price.
Specifically, a risk degree coefficient is matched in the corresponding relation between the risk degree value and the risk degree coefficient according to the calculated risk degree value, and a product is calculated according to the risk degree coefficient and the target quota value to generate a risk degree price. And matching an automatic discount time coefficient in the corresponding relation between the automatic discount time and the automatic discount time coefficient according to the automatic discount time, and performing quadrature generation according to the automatic discount time coefficient and the target volume value to generate an automatic discount time price. And matching the target amount discount price in the corresponding relation between the target amount value and the target amount discount price according to the target amount value. And finally, the sum of the previous quote value and the risk degree price, the automatic discount time price and the bid amount discount price is subtracted, so that the current quote is obtained.
The risk degree coefficient is set according to the risk degree; specifically, the greater the risk level, the smaller the risk level coefficient. The risk degree and the risk degree coefficient can be in various linear correlations and can be specifically set according to actual conditions; in one embodiment, the risk degree is linearly related to the risk degree coefficient in a binary function, namely: the risk degree coefficient decreases faster as the risk degree increases. The automatic discount time coefficient is set according to the automatic discount time; specifically, the more the number of automatic discounts, the smaller the coefficient of the number of automatic discounts. The automatic discount times and the automatic discount time coefficient can be in various linear correlations and can be specifically set according to actual conditions; in one embodiment, the auto-discount time and the auto-discount time coefficient are linearly related by a binary function, that is: the more rapidly the auto-discount number coefficient decreases as the auto-discount number increases. The target amount value and the target amount discount price are correspondingly set according to the target amount value; specifically, the larger the amount value of the target, the larger the amount offer price of the target. The amount value of the target and the discount price of the target amount can be in various linear correlations and can be set according to actual conditions; in one embodiment, the target amount value and the target amount offer are linearly related by a binary function, namely: the bid amount offer price increases faster as the bid amount value increases.
In another embodiment, the generating the vehicle insurance quote according to the insurance coefficient further comprises: and when the quotation times exceed a preset value, performing offline wind for the current targeted information. In order to avoid unlimited price reduction for business personnel who do not know quotations to promote the order and improve the order probability under the targeted assistance and suggestion of the wind exploration personnel, when the number of repeated quotations exceeds the preset number, the targeted assignment wind exploration personnel who currently conduct quotations is manually checked, namely: the wind surveying staff gives final quotation according to the current actual conditions, and the actual conditions comprise: the importance of the customer, the current overall performance, the profitability of the current business and the intention of the customer. It can be seen that not only can the bottom line of the price be preserved but also the exo-assistance can be obtained immediately.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
Referring to fig. 9, in an embodiment of the present invention, the present invention further provides a computer device, where the computer device 12 is represented in a form of a general-purpose computing device, and components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)31 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard drives"). Although not shown in FIG. 9, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, with the program modules 42 configured to carry out the functions of embodiments of the invention.
A program/utility 41 having a set (at least one) of program modules 42 may be stored, for example, in memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by running a program stored in the system memory 28, for example, implementing the insurance quote generation method provided by the embodiment of the present invention.
That is, the processing unit 16 implements, when executing the program: acquiring target information; analyzing the risk degree of the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient; and generating the vehicle insurance quotation according to the insurance coefficient.
In an embodiment of the present invention, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the vehicle insurance quotation generating method as provided in all embodiments of the present application.
That is, the program when executed by the processor implements: acquiring target information; analyzing the risk degree of the risk value and the risk coefficient of the determined target information according to a preset rule to obtain a corresponding risk coefficient; and generating the vehicle insurance quotation according to the insurance coefficient.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer-readable storage medium or a computer-readable signal medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPOM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The present invention provides a method and a device for generating car insurance quotations, which are introduced in detail, and the present invention has been explained in detail by applying specific examples to explain the principle and implementation manner of the present invention, and the descriptions of the above examples are only used to help understanding the method and the core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for generating a vehicle insurance quote, comprising:
acquiring target information, wherein the target information comprises target information with unique identity attribute;
determining a risk value and a risk coefficient of the target information according to a preset rule to obtain a corresponding risk coefficient;
and generating the vehicle insurance quotation according to the insurance coefficient.
2. The method of claim 1, wherein the obtaining of the target information, the target information including the target information unique to the identity attribute, comprises:
acquiring a correlation instruction;
and associating the target information with the unique identity attribute with the corresponding other target information.
3. The method of claim 1, wherein the performing risk degree analysis on the risk value and the risk coefficient for determining the target information according to a preset rule to obtain a corresponding risk factor comprises:
determining a risk factor and a risk value corresponding to the risk factor according to the target information;
obtaining a corresponding risk coefficient according to the corresponding relation between the type of the preset risk factor and the risk coefficient;
processing the risk value and the risk coefficient according to a preset rule to generate a risk degree value;
and obtaining a corresponding risk coefficient according to the risk degree value.
4. The method of claim 3, wherein determining the risk factor and the risk value corresponding to the risk factor according to the target information comprises:
and presetting a sensitive risk value segment, and performing offline wind for the current targeted information when the risk value is in the preset sensitive risk value segment.
5. The method according to claim 3 or 4, wherein the step of obtaining the corresponding risk factor according to the risk degree value comprises:
performing corresponding processing according to the value section of the risk coefficient in a preset risk coefficient table;
if the risk coefficient is located in a preset high risk compensation value section, carrying out no-price quotation or high-price quotation processing;
automatically discounting or/and providing a gift if the risk-offering coefficient is located in a preset low-risk compensation value section;
and if the risk factor is positioned between the high risk compensation value section and the low risk compensation value section, normally quoting.
6. The method of claim 1, wherein the performing risk degree analysis on the risk value and the risk coefficient for determining the target information according to a preset rule to obtain a corresponding risk factor comprises:
and when the risk degree analysis is not clear, performing offline wind compensation on the current targeted information.
7. The method of claim 1, wherein generating an automobile insurance offer according to the risk factor comprises:
obtaining an initial price according to the corresponding relation between the preset insurance type and the initial price;
and generating the vehicle insurance quotation according to the insurance coefficient and the initial price.
8. An insurance quote generating apparatus, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring target information which comprises target information with unique identity attribute;
the analysis module is used for carrying out risk degree analysis on the risk value and the risk coefficient of the determined target information according to a preset rule so as to obtain a corresponding risk coefficient;
and the generating module is used for generating the vehicle insurance quotation according to the insurance coefficient.
9. Electronic device, characterized in that it comprises a processor, a memory and a computer program stored on said memory and capable of running on said processor, said computer program, when executed by said processor, implementing the steps of the car insurance offer generation method according to any of claims 1 to 7.
10. Computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the vehicle insurance offer generating method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010911072.7A CN112001658A (en) | 2020-09-02 | 2020-09-02 | Method and device for generating vehicle insurance quotation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010911072.7A CN112001658A (en) | 2020-09-02 | 2020-09-02 | Method and device for generating vehicle insurance quotation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112001658A true CN112001658A (en) | 2020-11-27 |
Family
ID=73464472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010911072.7A Pending CN112001658A (en) | 2020-09-02 | 2020-09-02 | Method and device for generating vehicle insurance quotation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112001658A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177087A (en) * | 2021-05-12 | 2021-07-27 | 微民保险代理有限公司 | Information prompting method, device, equipment and computer readable medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106980911A (en) * | 2017-04-05 | 2017-07-25 | 南京人人保网络技术有限公司 | Driving methods of risk assessment and device based on the static factor |
| CN107818513A (en) * | 2017-11-24 | 2018-03-20 | 泰康保险集团股份有限公司 | Methods of risk assessment and device, storage medium, electronic equipment |
| CN109460964A (en) * | 2018-09-29 | 2019-03-12 | 中国平安财产保险股份有限公司 | Method, apparatus and computer equipment based on the more newly-generated vehicle insurance price list of data |
| CN110163759A (en) * | 2018-02-12 | 2019-08-23 | 北京直通万连科技有限公司 | A kind of vehicle insurance scheme generation method |
| CN111127228A (en) * | 2019-12-30 | 2020-05-08 | 山东坤达诚经济咨询有限公司 | Risk factor identification method, system and terminal |
-
2020
- 2020-09-02 CN CN202010911072.7A patent/CN112001658A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106980911A (en) * | 2017-04-05 | 2017-07-25 | 南京人人保网络技术有限公司 | Driving methods of risk assessment and device based on the static factor |
| CN107818513A (en) * | 2017-11-24 | 2018-03-20 | 泰康保险集团股份有限公司 | Methods of risk assessment and device, storage medium, electronic equipment |
| CN110163759A (en) * | 2018-02-12 | 2019-08-23 | 北京直通万连科技有限公司 | A kind of vehicle insurance scheme generation method |
| CN109460964A (en) * | 2018-09-29 | 2019-03-12 | 中国平安财产保险股份有限公司 | Method, apparatus and computer equipment based on the more newly-generated vehicle insurance price list of data |
| CN111127228A (en) * | 2019-12-30 | 2020-05-08 | 山东坤达诚经济咨询有限公司 | Risk factor identification method, system and terminal |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177087A (en) * | 2021-05-12 | 2021-07-27 | 微民保险代理有限公司 | Information prompting method, device, equipment and computer readable medium |
| CN113177087B (en) * | 2021-05-12 | 2024-05-24 | 微民保险代理有限公司 | Information prompting method, device, equipment and computer readable medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10796362B2 (en) | Used automobile transaction facilitation for a specific used automobile | |
| US11836785B1 (en) | System and method for providing comprehensive vehicle information | |
| US9626704B2 (en) | Automobile transaction facilitation based on a customer selection of a specific automobile | |
| US10223720B2 (en) | Automobile transaction facilitation using a manufacturer response | |
| CA2844768C (en) | Systems and methods for generating vehicle insurance premium quotes based on a vehicle history | |
| US9147217B1 (en) | Systems and methods for analyzing lender risk using vehicle historical data | |
| US20160042450A1 (en) | Methods and systems for deal structuring for automobile dealers | |
| US20150178829A1 (en) | System and method for generating a virtual credit score and a respective virtual credit line | |
| US20120130844A1 (en) | Automotive diagnostic and estimate system and method | |
| US20110082759A1 (en) | System and method for the analysis of pricing data including dealer costs for vehicles and other commodities | |
| US9727905B2 (en) | Systems and methods for determining cost of vehicle ownership | |
| CN105431878A (en) | System and method for automatically providing a/r-based lines of credit to businesses | |
| US20200410465A1 (en) | Payment-driven sourcing | |
| US20230281722A1 (en) | Smart estimatics methods and systems | |
| US20090276290A1 (en) | System and method of optimizing commercial real estate transactions | |
| US11508007B2 (en) | System and method for identifying vehicles for a purchaser from vehicle inventories | |
| US20210118016A1 (en) | Net valuation guarantee for vehicles | |
| CN112001658A (en) | Method and device for generating vehicle insurance quotation | |
| CN111861757A (en) | Financing matching method, system, equipment and storage medium | |
| JP2018205876A (en) | Damaged car assessment system | |
| JP7031838B2 (en) | Information processing equipment | |
| CN111079991A (en) | Service index prediction method, device, equipment and storage medium | |
| CN110955837A (en) | Product data checking method, device, equipment and storage medium | |
| WO2009023711A2 (en) | System and method for automating dealership transactions | |
| JP2008299547A (en) | Device and method for estimating repair or maintenance cost of vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |