CN112668956B - Intelligent verification method for logistics insurance data - Google Patents
Intelligent verification method for logistics insurance data Download PDFInfo
- Publication number
- CN112668956B CN112668956B CN202010919418.8A CN202010919418A CN112668956B CN 112668956 B CN112668956 B CN 112668956B CN 202010919418 A CN202010919418 A CN 202010919418A CN 112668956 B CN112668956 B CN 112668956B
- Authority
- CN
- China
- Prior art keywords
- risk
- risk target
- marked
- target
- transportation
- 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.)
- Active
Links
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to an intelligent nuclear insurance method for logistics insurance data, which is characterized in that first nuclear insurance coefficients are obtained by automatically acquiring information of a risk target in a target logistics insurance policy and based on the information of the risk target, second nuclear insurance coefficients are obtained according to formed logistics company driving transportation information aiming at carrying the transportation business of the risk target, and then the obtained first nuclear insurance coefficients and second nuclear insurance coefficients are input into an intelligent nuclear insurance early warning model by pre-constructing the intelligent nuclear insurance early warning model, so that a nuclear insurance result aiming at the risk target and a premium corresponding to the risk target are intelligently obtained, thereby replacing the traditional mode of artificial nuclear insurance, avoiding the nuclear insurance influence of artificial factors on the logistics insurance business and improving the nuclear insurance efficiency and accuracy.
Description
Technical Field
The invention relates to the field of logistics, in particular to an intelligent verification method for logistics insurance data.
Background
With the development of electronic commerce industry, the logistics industry becomes more developed, and logistics companies play a significant role in the logistics industry.
In order to reduce adverse effects of logistics risks on enterprises, more and more enterprises start to apply to related logistics business of the enterprises so as to avoid the influence of logistics transportation risks on development of the enterprises. Of course, it is particularly important for insurance companies how to efficiently and accurately assess the risk of such logistic traffic.
In the conventional insurance checking process of the insurance company aiming at the logistics business, the insurance company mainly carries out risk assessment and quotation aiming at premium by arranging insurance checking personnel on a insurance policy of the logistics insurance, so that the insurance checking result given by the insurance checking personnel can directly influence quotation of the insurance policy risk target and the wind control management condition of the insurance company.
However, because the traditional verification method for the logistics business is based on manual verification, the verification result is affected by the professional level, the practical experience and even the personal preference of the verification personnel to a great extent, the accurate and objective fair verification of the risk targets for the logistics business is affected, the cost of manual verification is increased, the verification efficiency of an insurance company is reduced, and the integral development of the insurance business of the logistics business is not facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an intelligent verification method for logistics insurance data aiming at the prior art.
The technical scheme adopted for solving the technical problems is as follows: the intelligent verification method for the logistics insurance data is characterized by comprising the following steps of:
step 1, acquiring a target logistics insurance policy, and extracting risk target information in the target logistics insurance policy;
the risk target is an article to be sent, and the risk target information comprises a name of the risk target, a logistics company for carrying the risk target, a total transportation distance of the risk target, cities through which the risk target needs to be transported and predicted weather when the risk target passes through each city, wherein the total transportation distance of the risk target is a preset planned path distance from a sending place to a destination of the risk target;
the risk target is marked as Q, the total transportation distance of the risk target Q is marked as D, the total number of cities which need to be passed through by the transportation risk target is marked as M, and the M-th City which needs to be passed through by the transportation risk target Q is marked as City m ，1≤m≤M；
The risk target Q passes through City m The predicted weather is marked asPredicting weatherThe corresponding risk is denoted as a j ，j＝1,2,3,4,5；
Step 2, obtaining a first underwriting coefficient according to the extracted risk mark information; wherein the first kernel coefficient is marked as tau 1 ；
Step 3, acquiring historical driving data information sets of all drivers of a logistics company carrying risk targets, and forming logistics company driving transportation information containing the historical driving data information sets;
the historical driving data information of the driver comprises a driver driving license number, the driving age of the driver, the years of the driver in logistics transportation, the total number of times of danger of the driver driving the logistics vehicle and the amount of claims corresponding to each time of danger; the total number of all drivers of the logistics company is marked as N, and the nth Driver is marked as Driver n Driver n Is marked as the driving age of (2)Driver n The year of the physical distribution is marked by +.>Driver n The total number of times of driving the logistic vehicle is marked +.>Driver n The claim amount corresponding to the ith risk of driving the logistic vehicle is marked +.>
Step 4, obtaining a second nuclear protection coefficient according to the formed driving and transportation information of the logistics company; wherein the second kernel coefficient is marked as tau 2 ；
Step 5, pre-constructing an intelligent nuclear protection early warning model; the input variables of the intelligent nuclear protection early-warning model are two nuclear protection coefficients, and the output value of the intelligent nuclear protection early-warning model is a result of whether a risk target passes through nuclear protection or not; wherein the intelligent nuclear insurance early warning model is marked as y=f (x 1 ,x 2 )，x 1 A nuclear protection coefficient, x, of an intelligent nuclear protection early warning model 2 The method is characterized in that the method is another insurance coefficient of the intelligent insurance early-warning model, and y is an output value of the intelligent insurance early-warning model; y=1 when the risk target passes through the underwriting, and y= -1 when the risk target does not pass through the underwriting;
step 6, the first and second underwriting coefficients are used as input variables to be input to an intelligent underwriting early warning model at the same time, and underwriting results aiming at the risk targets are obtained; wherein the nuclear retention result for the risk target is labeled y',
and 7, obtaining the premium for the risk target according to the underwriting result, the second underwriting coefficient and the market total value of the risk target.
In the intelligent verification method of the logistics insurance data, the first verification coefficient tau is improved 1 The method is as follows:
further, in the intelligent verification method of the logistics insurance data, the second verification coefficient τ 2 The method is as follows:
still further, in the method for intelligently verifying and protecting logistics insurance data, in step 7, a security fee for the risk target is marked as K, and a market total value of the risk target is marked as P:
still further improved, the intelligent verification method of logistics insurance data further comprises a process of adjusting the premium of the risk target according to the planned path condition of the transportation risk target. Specifically, in the present invention, the premium of the risk target is adjusted by the following steps S1 to S4:
step S1, calculating a first adjustment factor of the Q aiming at a risk target according to the total transportation distance of a planned path of the Q aiming at the transportation risk target and a preset standard mileage;
wherein the first adjustment factor for the risk target Q is marked as phi 1 The total transportation distance of the planned path of the Q aiming at the transportation risk target is marked as D, and the preset standard mileage is marked as L 0 ：
L 0 Is a preset fixed value;
step S2, calculating a second adjustment factor for the transportation risk target Q according to the current driver information of the logistics company carrying the transportation risk target Q and the road information of the planned path for the transportation risk target Q;
wherein the second adjustment factor for the transportation risk target Q is marked as phi 2 The driving behavior safety index of the current driver of the logistics company is marked as kappa, kappa epsilon (0, 1); the smooth traffic index for the planned path of the transportation risk target Q is marked as
V is the total quantity of recorded information of violations of current drivers of logistics companies in the last two years;
step S3, calculating and obtaining a premium adjustment factor of the risk target Q according to the first adjustment factor and the second adjustment factor; wherein the premium adjustment factor for the risk target Q is labeled γ, γ=Φ 1 ·φ 2 ；
Step S4, obtaining the adjusted premium for the risk target Q according to the obtained premium adjustment factor gamma for the risk target Q; wherein, the premium after adjustment for the risk mark Q is marked as P 1 ，P 1 ＝(1+γ)·P。
Compared with the prior art, the invention has the advantages that:
firstly, the intelligent nuclear insurance method automatically acquires information of a risk target in a target logistics insurance policy, obtains a first nuclear insurance coefficient based on the information of the risk target, obtains a second nuclear insurance coefficient according to formed logistics company driving transportation information for carrying the transportation business of the risk target, and intelligently obtains a nuclear insurance result for the risk target and a premium corresponding to the risk target by constructing an intelligent nuclear insurance early warning model in advance and inputting the obtained first nuclear insurance coefficient and the second nuclear insurance coefficient into the intelligent nuclear insurance early warning model, thereby replacing the traditional mode of artificial nuclear insurance, avoiding nuclear insurance influence of the artificial factor on the logistics insurance business and improving nuclear insurance efficiency and accuracy.
Secondly, the intelligent verification method of the invention can adjust the premium of the risk targets according to the planned path conditions of the transportation risk targets, so as to obtain the premium which is convenient for the insurance company to accurately evaluate and underwire the risk targets.
Drawings
Fig. 1 is a schematic flow chart of an intelligent verification method for logistics insurance data in an embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
As shown in fig. 1, the intelligent verification method for logistics insurance data in this embodiment includes the following steps:
step 1, acquiring a target logistics insurance policy, and extracting risk target information in the target logistics insurance policy;
wherein, the risk is marked as an object to be sent, namely, an object to be applied by the applicant; the risk target information comprises names of risk targets, logistics companies carrying the risk targets, total transportation distance of the risk targets, cities through which the risk targets need to pass, and predicted weather when the risk targets pass through each city, wherein the total transportation distance of the risk targets is a preset planned path distance from a mailplace to a destination;
the risk target is marked as Q, the total transportation distance of the risk target Q is marked as D, the total number of cities which the risk target needs to pass through is marked as M, and the mth City which the risk target Q needs to pass through is marked as City m ，1≤m≤M；
Risk subject Q passes through City m The predicted weather is marked asPredictive weather->The corresponding risk is denoted as a j ，j＝1,2,3,4,5；
Step 2, according to the extracted risk target informationObtaining a first nuclear protection coefficient; wherein the first kernel coefficient is marked as tau 1 First kernel protection coefficient tau 1 The method is as follows:
step 3, acquiring historical driving data information sets of all drivers of the logistics company carrying the risk targets Q, and forming logistics company driving transportation information containing the historical driving data information sets;
the historical driving data information of the driver comprises a driver driving license number, the driving age of the driver, the years of the driver in logistics transportation, the total number of times of danger of the driver driving the logistics vehicle and the amount of claims corresponding to each time of danger;
the total number of all drivers of the logistics company is marked as N, and the nth Driver is marked as Driver n Driver n Is marked as the driving age of (2)Driver n The years of the physical distribution transportation are marked asDriver n The total number of times of driving the logistic vehicle is marked +.>Driver n The claim amount corresponding to the ith risk of driving the logistic vehicle is marked +.>
Step 4, obtaining a second underwriting system according to the formed driving and transporting information of the logistics companyA number; wherein the second kernel coefficient is marked as tau 2 Second kernel protection coefficient tau 2 The method is as follows:
step 5, pre-constructing an intelligent nuclear protection early warning model; the input variables of the intelligent nuclear protection early-warning model are two nuclear protection coefficients, and the output value of the intelligent nuclear protection early-warning model is a result of whether a risk target passes through nuclear protection or not;
the intelligent nuclear security early warning model constructed in this embodiment is marked as y=f (x 1 ,x 2 )，x 1 A nuclear protection coefficient, x, of an intelligent nuclear protection early warning model 2 The method is characterized in that the method is another insurance coefficient of the intelligent insurance early-warning model, and y is an output value of the intelligent insurance early-warning model; y=1 when Q of the risk target passes the underwriting, and y= -1 when Q of the risk target does not pass the underwriting;
step 6, the first kernel protection coefficient tau is calculated 1 And a second kernel protection coefficient tau 2 As input variables, the input variables are simultaneously input into the intelligent nuclear insurance early-warning model y=f (x) 1 ,x 2 ) Obtaining a underwriting result aiming at the risk targets; wherein, the nuclear guarantee result aiming at the risk target is marked as y',
step 7, according to the verification result y' of the Q aiming at the risk target and the second verification coefficient tau 2 And the market total value of the risk target Q, obtaining the premium of the risk target Q; wherein, the premium for risk subject Q is labeled K, and the market total value of risk subject Q is labeled P:
of course, considering the actual transportation situation of the risk targets, the intelligent verification method of the logistics insurance data of the embodiment also adjusts the premium of the risk targets according to the planned path situation of the transportation risk targets. Specifically, this embodiment adjusts the premium of the risk target by the following steps S1 to S4:
step S1, calculating a first adjustment factor of the Q aiming at a risk target according to the total transportation distance of a planned path of the Q aiming at the transportation risk target and a preset standard mileage; wherein the first adjustment factor for the risk target Q is marked as phi 1 The total transportation distance of the planned path of the Q aiming at the transportation risk target is marked as D, and the preset standard mileage is marked as L 0 ：
L 0 Is a preset fixed value;
step S2, calculating a second adjustment factor for the transportation risk target Q according to the current driver information of the logistics company carrying the transportation risk target Q and the road information of the planned path for the transportation risk target Q; wherein the second adjustment factor for the transportation risk target Q is marked as phi 2 The driving behavior safety index of the current driver of the logistics company is marked as kappa, kappa epsilon (0, 1); the smooth traffic index for the planned path of the transportation risk target Q is marked as
V is the total quantity of recorded information of violations of current drivers of logistics companies in the last two years;
step S3, according to the first adjustment factor phi 1 And a second adjustment factor phi 2 Calculating to obtain the premium adjustment factor of the Q aiming at the risk targetA seed; wherein the premium adjustment factor for the risk target Q is labeled γ, γ=Φ 1 ·φ 2 ；
Step S4, obtaining the adjusted premium for the risk target Q according to the obtained premium adjustment factor gamma for the risk target Q; wherein, the premium after adjustment for the risk mark Q is marked as P 1 ，P 1 ＝(1+γ)·P。
While the preferred embodiments of the present invention have been described in detail, it is to be clearly understood that the same may be varied in many ways by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The intelligent verification method for the logistics insurance data is characterized by comprising the following steps of:
step 1, acquiring a target logistics insurance policy, and extracting risk target information in the target logistics insurance policy;
the risk target is an article to be sent, and the risk target information comprises a name of the risk target, a logistics company for carrying the risk target, a total transportation distance of the risk target, cities through which the risk target needs to be transported and predicted weather when the risk target passes through each city, wherein the total transportation distance of the risk target is a preset planned path distance from a sending place to a destination of the risk target;
the risk target is marked as Q, the total transportation distance of the risk target Q is marked as D, the total number of cities which need to be passed through by the transportation risk target is marked as M, and the M-th City which needs to be passed through by the transportation risk target Q is marked as City m ，1≤m≤M；
The risk target Q passes through City m The predicted weather is marked asPredictive weather->The corresponding risk is denoted as a j ，j＝1,2,3,4,5；
Step 2, obtaining a first underwriting coefficient according to the extracted risk mark information; wherein the first kernel coefficient is marked as tau 1 The method comprises the steps of carrying out a first treatment on the surface of the First kernel protection coefficient tau 1 The method is as follows:
step 3, acquiring historical driving data information sets of all drivers of a logistics company carrying risk targets, and forming logistics company driving transportation information containing the historical driving data information sets;
the historical driving data information of the driver comprises a driver driving license number, the driving age of the driver, the years of the driver in logistics transportation, the total number of times of danger of the driver driving the logistics vehicle and the amount of claims corresponding to each time of danger;
the total number of all drivers of the logistics company is marked as N, and the nth Driver is marked as Driver n Driver n Is marked as the driving age of (2)Driver n The years of the physical distribution transportation are marked asDriver n The total number of times of driving the logistic vehicle is marked +.>Driver n The claim amount corresponding to the ith risk of driving the logistic vehicle is marked +.>
Step 4, obtaining a second nuclear protection coefficient according to the formed driving and transportation information of the logistics company; wherein the second kernel coefficient is marked as tau 2 The method comprises the steps of carrying out a first treatment on the surface of the Second kernel protection coefficient tau 2 The method is as follows:
step 5, pre-constructing an intelligent nuclear protection early warning model; the input variables of the intelligent nuclear protection early-warning model are two nuclear protection coefficients, and the output value of the intelligent nuclear protection early-warning model is a result of whether a risk target passes through nuclear protection or not; wherein the intelligent nuclear insurance early warning model is marked as y=f (x 1 ,x 2 )，x 1 A nuclear protection coefficient, x, of an intelligent nuclear protection early warning model 2 The method is characterized in that the method is another insurance coefficient of the intelligent insurance early-warning model, and y is an output value of the intelligent insurance early-warning model; y=1 when the risk target passes through the underwriting, and y= -1 when the risk target does not pass through the underwriting;
step 6, the first and second underwriting coefficients are used as input variables to be input to an intelligent underwriting early warning model at the same time, and underwriting results aiming at the risk targets are obtained; wherein the nuclear retention result for the risk target is labeled y',
and 7, obtaining the premium for the risk target according to the underwriting result, the second underwriting coefficient and the market total value of the risk target.
2. The method of claim 1, wherein in step 7, the risk-targeted premium is marked with K, and the risk-targeted market total value is marked with P:
3. the method of claim 2, further comprising the step of adjusting the premium of the risk target based on the planned path of the transportation risk target.
4. The intelligent verification method for logistics insurance data according to claim 3, wherein the risk-targeted premium is adjusted by the following steps S1 to S4:
step S1, calculating a first adjustment factor of the Q aiming at a risk target according to the total transportation distance of a planned path of the Q aiming at the transportation risk target and a preset standard mileage;
wherein the first adjustment factor for the risk target Q is marked as phi 1 The total transportation distance of the planned path of the Q aiming at the transportation risk target is marked as D, and the preset standard mileage is marked as L 0 ：
L 0 Is a preset fixed value;
step S2, calculating a second adjustment factor for the transportation risk target Q according to the current driver information of the logistics company carrying the transportation risk target Q and the road information of the planned path for the transportation risk target Q;
wherein the second adjustment factor for the transportation risk target Q is marked as phi 2 The driving behavior safety index of the current driver of the logistics company is marked as kappa, kappa epsilon (0, 1); needleThe smooth index of passage of the planned path of the transportation risk target Q is marked as
V is the total quantity of recorded information of violations of current drivers of logistics companies in the last two years;
step S3, calculating and obtaining a premium adjustment factor of the risk target Q according to the first adjustment factor and the second adjustment factor; wherein the premium adjustment factor for the risk target Q is labeled γ, γ=Φ 1 ·φ 2 ；
Step S4, obtaining the adjusted premium for the risk target Q according to the obtained premium adjustment factor gamma for the risk target Q; wherein, the premium after adjustment for the risk mark Q is marked as P 1 ，P 1 ＝(1+γ)·P。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010919418.8A CN112668956B (en) | 2020-09-04 | 2020-09-04 | Intelligent verification method for logistics insurance data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010919418.8A CN112668956B (en) | 2020-09-04 | 2020-09-04 | Intelligent verification method for logistics insurance data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112668956A CN112668956A (en) | 2021-04-16 |
| CN112668956B true CN112668956B (en) | 2023-09-22 |
Family
ID=75403183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010919418.8A Active CN112668956B (en) | 2020-09-04 | 2020-09-04 | Intelligent verification method for logistics insurance data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112668956B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103038795A (en) * | 2009-12-24 | 2013-04-10 | 旅行者保险公司 | Risk assessment and control, insurance premium determinations, and other applications using busyness |
| CN106600417A (en) * | 2016-11-09 | 2017-04-26 | 前海企保科技（深圳）有限公司 | Underwriting method and device of property insurance policies |
| CN106934720A (en) * | 2017-01-24 | 2017-07-07 | 久隆财产保险有限公司 | Equipment insurance intelligent pricing method and system based on Internet of Things |
| CN107203943A (en) * | 2017-04-06 | 2017-09-26 | 北京保程保险公估有限公司 | Motor vehicle business insurance pricing system |
| CN108564421A (en) * | 2018-05-07 | 2018-09-21 | 中国平安财产保险股份有限公司 | Car insurance pricing method, device, equipment and computer storage media |
| CN109377398A (en) * | 2018-11-19 | 2019-02-22 | 北京金州世纪信息技术有限公司 | The classification risk method and device of UBI insurance |
| CN109416873A (en) * | 2016-06-24 | 2019-03-01 | 瑞士再保险有限公司 | The autonomous motor vehicles in autonomous or part and its correlation method with automation risk control system |
| CN109791678A (en) * | 2016-07-25 | 2019-05-21 | 瑞士再保险有限公司 | It is measured for the dynamic risk based on score and polymerize the intelligent adaptive automotive fittings and its correlation method that have telematics connection search engine |
| CN110009517A (en) * | 2019-04-03 | 2019-07-12 | 爱保科技（横琴）有限公司 | A kind of cargo transportation insurance pricing method |
| CN110071953A (en) * | 2018-01-23 | 2019-07-30 | 株式会社日立制作所 | The method that vehicle operating for executing driver is analyzed |
| CN110175923A (en) * | 2019-04-09 | 2019-08-27 | 平安国际智慧城市科技股份有限公司 | A kind of Cold Chain Logistics are insured method, apparatus, computer equipment and storage medium |
| CN110276953A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Rule-breaking vehicle travel risk analysis method based on BEI-DOU position system |
| CN110276954A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Vehicle driving behavior integration methods of marking based on BEI-DOU position system |
| 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 |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070016542A1 (en) * | 2005-07-01 | 2007-01-18 | Matt Rosauer | Risk modeling system |
| US20090177569A1 (en) * | 2008-01-03 | 2009-07-09 | Bowers Jeffrey A | Water alteration structure risk management or ecological alteration management systems and methods |
| US20140278574A1 (en) * | 2013-03-14 | 2014-09-18 | Ernest W. BARBER | System and method for developing a driver safety rating |
| US10942038B2 (en) * | 2016-02-15 | 2021-03-09 | Allstate Insurance Company | Real time risk assessment and operational changes with semi-autonomous vehicles |
-
2020
- 2020-09-04 CN CN202010919418.8A patent/CN112668956B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103038795A (en) * | 2009-12-24 | 2013-04-10 | 旅行者保险公司 | Risk assessment and control, insurance premium determinations, and other applications using busyness |
| CN109416873A (en) * | 2016-06-24 | 2019-03-01 | 瑞士再保险有限公司 | The autonomous motor vehicles in autonomous or part and its correlation method with automation risk control system |
| CN109791678A (en) * | 2016-07-25 | 2019-05-21 | 瑞士再保险有限公司 | It is measured for the dynamic risk based on score and polymerize the intelligent adaptive automotive fittings and its correlation method that have telematics connection search engine |
| CN106600417A (en) * | 2016-11-09 | 2017-04-26 | 前海企保科技（深圳）有限公司 | Underwriting method and device of property insurance policies |
| CN106934720A (en) * | 2017-01-24 | 2017-07-07 | 久隆财产保险有限公司 | Equipment insurance intelligent pricing method and system based on Internet of Things |
| CN107203943A (en) * | 2017-04-06 | 2017-09-26 | 北京保程保险公估有限公司 | Motor vehicle business insurance pricing system |
| CN110071953A (en) * | 2018-01-23 | 2019-07-30 | 株式会社日立制作所 | The method that vehicle operating for executing driver is analyzed |
| CN108564421A (en) * | 2018-05-07 | 2018-09-21 | 中国平安财产保险股份有限公司 | Car insurance pricing method, device, equipment and computer storage media |
| CN109377398A (en) * | 2018-11-19 | 2019-02-22 | 北京金州世纪信息技术有限公司 | The classification risk method and device of UBI insurance |
| CN110009517A (en) * | 2019-04-03 | 2019-07-12 | 爱保科技（横琴）有限公司 | A kind of cargo transportation insurance pricing method |
| CN110175923A (en) * | 2019-04-09 | 2019-08-27 | 平安国际智慧城市科技股份有限公司 | A kind of Cold Chain Logistics are insured method, apparatus, computer equipment and storage medium |
| CN110276953A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Rule-breaking vehicle travel risk analysis method based on BEI-DOU position system |
| CN110276954A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Vehicle driving behavior integration methods of marking based on BEI-DOU position system |
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| A保险公司物流责任险费率厘定方案设计;李婉;《中国优秀硕士学位论文全文数据库经济与管理科学辑》(第02期);第J161-29页 * |
| 物流企业责任风险分析及承保模式研究;唐艳艳;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》(第04期);第J145-257页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112668956A (en) | 2021-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ayuso et al. | Improving automobile insurance ratemaking using telematics: Incorporating mileage and driver behaviour data | |
| US20210312567A1 (en) | Automobile Monitoring Systems and Methods for Loss Reserving and Financial Reporting | |
| US11836785B1 (en) | System and method for providing comprehensive vehicle information | |
| Bordat et al. | An analysis of cost overruns and time delays of INDOT projects | |
| CN107679996A (en) | Vehicle insurance Claims Resolution method and system | |
| CN108846547A (en) | A kind of Enterprise Credit Risk Evaluation method of dynamic adjustment | |
| US8463699B2 (en) | Method and system of determining and applying insurance profit scores | |
| Welde et al. | The efficiency of Norwegian road toll companies | |
| CN110009517A (en) | A kind of cargo transportation insurance pricing method | |
| CN110955866A (en) | Cross-border transaction risk assessment device and method and readable storage medium | |
| CN106022600A (en) | Method for determining cash supplement strategy of multiple cash flow stations in specific period | |
| CN112668956B (en) | Intelligent verification method for logistics insurance data | |
| US11816641B2 (en) | Systems and methods for task distribution and tracking | |
| US20150324807A1 (en) | Carbon emissions credits system | |
| CN113077146A (en) | Multi-element coupled project performance comprehensive evaluation method | |
| CN116542466A (en) | Asset full period management system based on data analysis | |
| Constantinescu et al. | Impact study of telematics auto insurance | |
| CN114240676A (en) | Vehicle insurance cost strategy making method and device | |
| Oppegaard | Platformization as subsumption? A case study of taxi platforms in Oslo, Norway | |
| CN110210984A (en) | Serious disease medical insurance policies adjust the quantization method influenced on fund expenditure and system | |
| Liang et al. | A proposed GST compliance model of GST registered person in Malaysia | |
| CN114201530B (en) | Early screening and preventive supervision method for suspected abnormal operation passenger car | |
| CN116502863B (en) | Balanced scheduling method and system applied to intelligent bus line | |
| CN113034089B (en) | Logistics information integration service management system based on big data | |
| Aboelmagd | Simulating Cash Flow Model for Mega Construction Projects in Egypt |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |