Generate more accurate and granular driver risk profiling
Aggregated Usage Based Insurance (A-UBI)
Downtown.AI's aggregated human movement analytics of entire populations, enable us to generate accurate and granular driver risk cohorts for the auto insurance market. These cohorts are based on drivers’ real world behaviors such as yearly mileage (VKT / VMT), speeding over limit, driving hours, roads in use, common weather conditions and more.
Our solution enables insurance companies to optimize pricing and reduce risk with no mobile apps installation and no infringement of client privacy.
You can get our R&D white paper by sending the form bellow.
This visualization of Vancouver, Canada, demonstrates Average VKT per motorist for each FSA (Canadian Postal Code forward sortation area) by height and colour. Key variable such like VMT/VKT can serve to optimize driver risk profiling by identify real world driving patterns and differential exposure to risk.
This visualization demonstrates the non linear correlation between number of crashes occurred within hexagons (low = blue, high = red), to the number of crashes the residents of hexagons are actually involved with (tall bins = high involvement in crashes). The visualization emphasizes how driver risk profiles that based on aggregated driving patterns could better indicate exposure to risk than current methods.
Get the white paper
Send the form bellow to get a copy of the 'Aggregated UBI' white paper
Title: Using Machine Learning and Mobile Location data to Optimize Driver Risk Profiling for the Auto Insurance Industry. An advance toward A-UBI (Aggregated Usage Based Insurance)