Risk Based Automotive Insurance Rating System

• US 20160189303A1
• Filed: 08/15/2014
• Published: 06/30/2016
• Est. Priority Date: 03/21/2014
• Status: Abandoned Application

• Current Assignee: Scope Technologies Holdings Limited
• Original Assignee: Scope Technologies Holdings Limited
• Inventors: Fuchs, Gil Emanuel
• Application Number:
  US14/460,868
• Publication Number:
  US 20160189303A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method and system for determining the risk associated with providing vehicle insurance. A database is compiled that contains historical information pertaining to vehicle and driver activities and risk factors associated with elements of a road network. The historical information may include, for example, accident counts, and weather and road conditions during the accidents. A statistical predictive relationship is developed to estimate insurance risk as a function of the historical information for each road element. During driving, vehicle and driver activity are monitored and subsequently, insurance premiums are calculated based on the developed model and when and where a vehicle and/or driver travel. The model is periodically updated and refined.

Claims

1. A method for determining the risk associated with providing vehicle insurance comprising:

compiling a database of historical information comprising;

a plurality of indications of vehicle and driver activities and risk factors wherein the historical information is geo-referenced to transportation elements, and wherein the historical information may be related to insurance risk;

developing a statistical predictive relationship to estimate insurance risk as a function of the historical information for each transportation element wherein the type of historical information is found to have statistical relevance to insurance risk;

monitoring and recording at least one of the vehicle and specific driver activity including both driving habits and when and how often the at least one of the vehicle and driver traverses individual transportation elements;

determining an insurance premium based on;

determining when and where a vehicle is traveling or a driver is driving, and using this information as input to the statistical predictive relationship;

acquiring additional geo-referenced risk factors from outside sources;

refining the statistical predictive relationship by incorporating both the recorded at least one of the vehicle and specific driver activity and additional geo-referenced risk factors into the database of historical information and re-developing the statistical predictive relationship; and

at least one of adding new risk factors as statistically significant amounts of data becomes available for the new risk factors and removing risk factors from the predictive model as the impact on the predictive relationship goes below a statistical threshold.

2. The method of claim 1 wherein the risk factors for each transportation element comprise at least one of:

accident counts;

traffic density;

number of driving citations, and

number of insurance claims.

3. The method of claim 2 wherein the risk factors are further referenced or indexed by one or more of:

time of day, time of week, and severity of the accident in terms of vehicle damage or passenger injury, type of traffic citation and cost of insurance claims.

4. The method of claim 1 wherein the only risk factor is the number of traffic accidents per transportation segment that is optionally further indexed by one or both of time of day and day of week.

5. The method of claim 1 wherein additional risk factors comprise at least one of the type of vehicle, driver demographics, weather information and pavement conditions.

6. The method of claim 1 wherein the statistical predictive relationship is developed using one of a neural network or machine learning.

7. The method of claim 1 wherein the anticipated accuracy of the predictive function is also presented with a prediction of insurance risk and wherein the anticipated accuracy is based on metadata associated with the historic information for the transportation segments used in the prediction.

8. The method of claim 1 wherein each type of historic information is based on a plurality of disparate sources and wherein the information from the dispirit sources is merged using consistent units of measurement and parameterized into consistent ranges of measure.

9. The method of claims 8 wherein at least one of the disparate sources contains information geo-referenced only to an address and that address is geocoded and snapped to a transportation segment.

10. The method of claim 1 wherein the determined insurance risk associated with transportation segments is productized as attribution associated with a transportation map.

11. The method of claim 1 wherein the insurance risk is collectively determined for a plurality of routes from an origin to a destination and wherein route selection is at least in part based on minimizing the collective risk.

12. The method of claim 11 wherein if a driver follows a determined route that has a minimized collective risk, the driver is provided a discount on insurance premiums.

13. The method of claim 1 wherein additional risk factors comprise at least one of, traffic conditions, accident occurrences, detours, and weather information wherein the additional factors are received in real-time and used to determine immediate risk.

14. The method of claim 13 wherein if the immediate risk of driving exceeds a threshold, and the driver chooses to delay travel until such time as the risk is less, the driver is rewarded with reduced insurance premiums

15. The method of claim 13 wherein the received real-time information is utilized in a route determination wherein route selection is at least in part based on minimizing the collective risk of driving along the route.

16. The method of claim 1 wherein the recorded activity comprises historical routes taken by the specific driver or vehicle and the frequency those routes are taken;

and

determining while the vehicle is in motion if it likely that the vehicle is traveling along a frequented route; and

upon finding that a likely route is being taken, calculating alternate routes to the destination of the currently traveled route in order to determine if the alternate route has a lower risk factor;

upon determining that a lower risk factor route is available, presenting that route to the driver.

17. The method of claim 16 wherein if the driver takes the present lower risk route, the driver receives a discount on the driver'"'"'s insurance premium.

18. The method of claim 1 wherein the insurance premium is periodically adjusted based on the collective exposure to risk for a given period of time.

19. The method of claim 1:

wherein the predictive function varies geographically at least by one of the weighting of risk factors and the risk factors that are actually incorporated into the model.

20. The method of claim 1:

wherein the historical information and the risk factors consist of entirely of sensor output and derivative of the sensor output from sensors contained within and that are part of the vehicle.

21. A computer-implemented system for determining vehicle or specific driver insurance premiums, said computer-implemented system having at least one computer including a processor and associated memory from which computer instructions are executed by said processor, said system comprising:

a database module configured to compile a database of historical information comprising;

a plurality of indications of vehicle and driver activities and risk factors wherein the historical information is geo-referenced to transportation elements, and wherein the historical information may be related to insurance risk;

an insurance risk estimator configured to develop a statistical predictive relationship to estimate insurance risk as a function of the historical information received from the database module for each transportation element wherein the type of historical information is found to have statistical relevance to insurance risk;

a monitoring and recording module configured to monitor and record at least one of the vehicle and specific driver activity including both driving habits and when and how often the at least one of the vehicle and driver traverses individual transportation elements;

a insurance premium generator configured to determine an insurance premium based on when and where a vehicle is traveling or a driver is driving, and using this information as input to the statistical predictive relationship;

a communications module configured to acquire additional geo-referenced risk factors from outside sources; and

the insurance risk estimator further configured to;

refine the statistical predictive relationship by incorporating both the recorded at least one of the vehicle and specific driver activity and additional geo-referenced risk factors into the database of historical information and re-developing the statistical predictive relationship; and

at least one of add new risk factors as statistically significant amounts of data become available for the new risk factors and remove risk factors from the predictive model as the impact on the predictive relationship goes below a statistical threshold.

22. A non-transitory computer readable media containing instructions to implement a system for determining vehicle or specific driver insurance premiums, the system having at least one computer including a processor and associated memory from which the instructions are executed by said processor, said instructions comprising:

compiling a database of historical information comprising;

a plurality of indications of vehicle and driver activities and risk factors wherein the historical information is geo-referenced to transportation elements, and wherein the historical information may be related to insurance risk;

developing a statistical predictive relationship to estimate insurance risk as a function of the historical information for each transportation element wherein the type of historical information is found to have statistical relevance to insurance risk;

monitoring and recording at least one of the vehicle and specific driver activity including both driving habits and when and how often the at least one of the vehicle and driver traverses individual transportation elements;

determining an insurance premium based on;

determining when and where a vehicle is traveling or a driver is driving, and using this information as input to the statistical predictive relationship;

acquiring additional geo-referenced risk factors from outside sources;

refining the statistical predictive relationship by incorporating both the recorded at least one of the vehicle and specific driver activity and additional geo-referenced risk factors into the database of historical information and re-developing the statistical predictive relationship; and

at least one of adding new risk factors as statistically significant amounts of data becomes available for the new risk factors and removing risk factors from the predictive model as the impact on the predictive relationship goes below a statistical threshold.

23. A method for adjusting vehicle or specific driver insurance premiums comprising the steps of:

1) monitoring and recording a vehicle or specific driver activity including when and how often the vehicle or driver traverses individual transportation elements for a first time period;

2) receiving a risk index for each transportation segment traversed during the first time period;

3) calculating an overall risk index for the vehicle or specific driver for the first time period comprising the summation of each risk index for each traversed transportation segment multiplied by the number of traversals for the first time period;

4) repeating steps 1-3 for a second time period; and

5) if the overall risk index for the second time period is different than the first time period, use this information to adjust insurance premiums up or down.

24. A method for adjusting vehicle or specific driver insurance premiums comprising the steps of:

1) receiving a plurality of requests from a specific driver or passenger of the vehicle, using a navigation device located within the vehicle, for route guidance from a start to a destination;

2) for each routing request, determine possible routes;

3) for each possible route, receive real-time hazard information;

4) for each possible route, calculate the relative risk of taking that route;

5) present the driver or passenger of the vehicle with one or more of the safest routes;

6) monitor the vehicle movement and determine if the vehicle has taken one or the safest routes, provided that the vehicle travels to the destination;

7) record over a time period, the amount of safe routes taken and the amount of less safe routes taken; and

8) use the ratio of safe routes taken when compared to less safe routes to adjust insurance premiums up or down.

25. A computer-implemented system for determining a safe route from an origin to a destination, said computer-implemented system having at least one computer including a processor and associated memory from which computer instructions are executed by said processor, said system comprising:

a database module configured to store historical information related to driving risk and that is geo-referenced to transportation elements;

a monitoring system configured to acquire real-time driving risk information along potential routes from the origin to the destination; and

a route calculator configured to determine a safe route from an origin to a destination in part based on the historical driving risk information and the real-time driving risk information.

26. The computer-implemented system of claim 25 wherein the at least one computer is a navigation system located within a vehicle.

27. The computer-implemented system of claim 25 wherein the system is accessible to an end-user via a network and is provided as software as a service.