Machine learning platform for dynamic device and sensor quality evaluation

1. A computing platform, comprising:

at least one processor;

a communication interface communicatively coupled to the at least one processor; and

memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

receive, from a first computing device, driving data collected by the first computing device during one or more driving trips;

compute, using the driving data, a plurality of driving metrics;

compute a weighted score based on the plurality of driving metrics;

identify, between consecutive driving trips included in the one or more driving trips, a time difference between an end point of a first driving trip and a starting point of a second driving trip;

identify a percentage of the time difference that exceeds a predetermined period of time;

compare the percentage to a predetermined percentage value;

compute a device evaluation score based on subtraction of a fixed value from the weighted score in response to the percentage exceeding the predetermined percentage value, wherein the device evaluation score indicates a quality of the driving data received from the first computing device; and

output a message to a second computing device, the message including the device evaluation score and one or more commands directing the second computing device to indicate the device evaluation score.

2. The computing platform of claim 1 , wherein the plurality of driving metrics includes at least one of a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, or a distance between consecutive trips score.

3. The computing platform of claim 1 , wherein the device evaluation score is computed by applying a machine learning model to the plurality of driving metrics.

4. The computing platform of claim 1 , wherein the first computing device is a mobile computing device.

5. The computing platform of claim 1 , wherein the second computing device is an enterprise user device.

6. The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

identify a device model of the first computing device.

7. A method comprising:

receiving, from a first computing device, telematics data collected by the first computing device during a plurality of driving trips;

computing, using the telematics data, one or more driving metrics;

computing a weighted score based on the one or more driving metrics;

identifying a time difference between an end point of a first driving trip of the plurality of driving trips and a starting point of a second driving trip of the plurality of driving trips;

identifying a percentage of the time difference that exceeds a predetermined period of time;

comparing the percentage to a predetermined percentage value;

computing a device evaluation score by subtracting a fixed value from the weighted score in response to the percentage exceeding the predetermined percentage value, the device evaluation score indicating a quality of the telematics data received from the first computing device;

generating a command directing a second computing device to indicate the device evaluation score; and

transmitting a message to the second computing device, the message including the device evaluation score and the command.

8. The method of claim 7 , wherein the one or more driving metrics includes at least one of a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, or a distance between consecutive trips score.

9. The method of claim 7 , wherein the device evaluation score is computed applying one or more machine learning models to the one or more driving metrics.

10. The method of claim 7 , wherein the first computing device is a mobile computing device.

11. The method of claim 7 , wherein the second computing device is an enterprise user device.

12. The method of claim 7 further comprising:

receiving a device identifier from the first computing device; and

identifying a model of the first computing device using the device identifier.

13. A non-transitory computer readable medium storing instructions that, when read by a processor, cause the processor to:

receive, from a first computing device, data collected by the first computing device during a plurality of driving trips;

compute driving metrics using the data;

compute a weighted score based on the driving metrics;

identify a time difference between an end point of a first driving trip of the plurality of driving trips and a starting point of a second driving trip of the plurality of driving trips;

identify a percentage of the time difference that exceeds a predetermined period of time;

compare the percentage to a predetermined percentage value;

compute a device evaluation score by subtracting a fixed value from the weighted score in response to the percentage exceeding the predetermined percentage value, the device evaluation score indicating a quality of the data received from the first computing device; and

output a message to a second computing device, the message including the device evaluation score and a command directing the second computing device to indicate the device evaluation score.

14. The non-transitory computer readable medium of claim 13 , wherein the driving metrics include at least one of a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, or a distance between consecutive trips score.

15. The non-transitory computer readable medium of claim 13 , wherein the device evaluation score is computed using a machine learning model.

16. The non-transitory computer readable medium of claim 13 , wherein the first computing device is a mobile computing device, and the second computing device is an enterprise user device.

17. The non-transitory computer readable medium of claim 13 , wherein the instructions that, when read by the processor, cause the processor to:

identify a device model of the first computing device using a device identifier received from the first computing device.