Systems and methods for generating mobility insurance products using ride-sharing telematics data

A computer-implemented method for determining an optimal usage-based insurance (“UBI”) product in real-time for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services using a personalized insurance (“PI”) computing device, the PI computing device having at least one processor in communication with at least one memory, the method comprising:

• generating, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver;
• receiving from the TNC in real-time supply and demand data associated with a plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for the transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services;
• retrieving driver data for the driver, wherein the driver data includes at least driver history associated with the driver;
• updating, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model;
• determining, in real-time, the optimal UBI product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with the increased demand for the transportation services and a risk profile associated with the driver;
• and
• transmitting, in real-time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.

One or more non-transitory computer-readable storage media having computer-executable instructions embodied thereon, when executed by a personalized insurance (“PI”) computing device having at least one processor in communication with at least one memory, the computer-executable instructions causing the at least one processor to:

• generate, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver;
• receive from a transportation network company (“TNC”) in real-time supply and demand data associated with a plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services;
• retrieve driver data for a driver operating a vehicle for the TNC, wherein the driver data includes at least driver history associated with the driver;
• update, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model;
• determine, in real-time, an optimal usage-based insurance (“UBI”) product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with an increased demand for the transportation services and a risk profile associated with the driver;
• and
• transmit, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.