Systems and methods for modeling telematics, positioning, and environmental data
Provided herein is a modeling computing device including a processor in communication with a memory device. The processor is configured to: (i) retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability amount data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data, (ii) generate a model that relates the historical liability amount data and the historical user data, (iii) store the model in the at least one memory device, (iv) collect current user data associated with a candidate user, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, and (v) analyze the collected current user data using the generated model.
1. A modeling computing device comprising at least one processor in communication with at least one memory device, the at least one processor configured to:
retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data;
create a first plurality of training datasets including the historical liability data and the historical user data for building a model using one or more machine learning programs;
collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle;
create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;
update the model by applying the second plurality of training datasets to the model; and
execute the updated model to determine a current liability level for the candidate user.
2. The modeling computing device of claim 1 , wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
3. The modeling computing device of claim 1 , wherein the at least one processor is further configured to:
build, using the one or more machine learning programs, the model based upon the first plurality of training datasets; and
store the model in the at least one memory device.
4. The modeling computing device of claim 1 , wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the at least one processor is further configured to build the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.
5. The modeling computing device of claim 1 , wherein the current user data further includes current personal information and current environmental data.
6. The modeling computing device of claim 1 , wherein the at least one processor is further configured to:
transmit the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
7. The modeling computing device of claim 1 , wherein the at least one processor is further configured to:
generate an insurance policy for the candidate user based upon the determined current liability level; and
enroll the candidate user with an insurance provider of the insurance policy.
8. A computer-implemented method implemented by a modeling computing device including at least one processor in communication with at least one memory device, the computer-implemented method comprising:
retrieving, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data;
creating a first plurality of training datasets including the historical liability data and the historical user data for building a model using one or more machine learning programs;
collecting current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle;
creating a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;
updating the model by applying the second plurality of training datasets to the model; and
executing the updated model to determine a current liability level for the candidate user.
9. The computer-implemented method of claim 8 , wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
10. The computer-implemented method of claim 8 further comprising:
building, using the one or more machine learning programs, the model based upon the first plurality of training datasets; and
storing the model in the at least one memory device.
11. The computer-implemented method of claim 8 , wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the method further comprises building the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.
12. The computer-implemented method of claim 8 , wherein the current user data further includes current personal information and current environmental data.
13. The computer-implemented method of claim 8 further comprising:
transmitting the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
14. The computer-implemented method of claim 8 further comprising:
generating an insurance policy for the candidate user based upon the determined current liability level; and
enrolling the candidate user with an insurance provider of the insurance policy.
15. At least one non-transitory computer-readable medium having computer-executable instructions embodied thereon, wherein when executed by a modeling computing device including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:
retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability data and historical user data;
create a first plurality of training datasets including the historical liability data and the historical user data for building a model using one or more machine learning programs;
collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current vehicle telematics data associated with the vehicle, wherein the current vehicle telematics data is gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include at least one of a GPS device, an accelerometer, a gyroscope, a camera, or a sensor installed within the vehicle or located remotely from the vehicle;
create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;
update the model by applying the second plurality of training datasets to the model; and
execute the updated model to determine a current liability level for the candidate user.
16. The at least one non-transitory computer-readable medium of claim 15 , wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, or historical environmental data.
17. The at least one non-transitory computer-readable medium of claim 15 , wherein the computer-executable instructions further cause the at least one processor to:
build, using the one or more machine learning programs, the model based upon the first plurality of training datasets; and
store the model in the at least one memory device.
18. The at least one non-transitory computer-readable medium of claim 15 , wherein the current user data further includes current personal information and current environmental data.
19. The at least one non-transitory computer-readable medium of claim 15 , wherein the computer-executable instructions further cause the at least one processor to:
transmit the determined current liability level to at least one third party computing device, wherein the at least one third party computing device includes an insurance computing device.
20. The at least one non-transitory computer-readable medium of claim 15 , wherein the computer-executable instructions further cause the at least one processor to:
generate an insurance policy for the candidate user based upon the determined current liability level; and
enroll the candidate user with an insurance provider of the insurance policy.