Methods and systems of predicting total loss events

A system comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the one or more processors, configure the system to:
• receive sensor measurements from one or more sensors of a mobile device while the mobile device is positioned within a vehicle;
• detect, using the sensor measurements, a crash event indicating an occurrence of a vehicle collision;
• identify a first set of data from the sensor measurements, wherein the first set of data is associated with the crash event;
• generate a first feature vector using the first set of data and vehicle data, wherein the vehicle data includes an identifier of the vehicle;
• generate a second feature vector using the first set of data and one or more additional data types;
• predict a confidence of a total loss event by:
• generating a first confidence value using a first machine-learning model and the first feature vector;
• and
• generating a second confidence value using a second machine-learning model and the second feature vector;
• and
• transmit a notification that includes an indication of the confidence of the total loss event.

A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving sensor measurements from one or more sensors of a mobile device while the mobile device is positioned within a vehicle;
• detecting, using the sensor measurements, a crash event indicating an occurrence of a vehicle collision;
• identifying a first set of data from the sensor measurements, wherein the first set of data is associated with the crash event;
• generating a first feature vector using the first set of data and vehicle data, wherein the vehicle data includes an identifier of the vehicle;
• generating a second feature vector using the first set of data and one or more additional data types;
• predicting a confidence of a total loss event by:
• generating a first confidence value using a first machine-learning model and the first feature vector;
• and
• generating a second confidence value using a second machine-learning model and the second feature vector;
• and
• transmitting a notification that includes an indication of the confidence of the total loss event.