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Auto Insurance

Generate more accurate and granular driver risk profiling

Aggregated Usage Based Insurance (A-UBI)

Within the insurance industry, driver risk profiles are used to assign rates such that risk exposure is minimized, profitability is increased and costs to drivers are further reduced.


Downtown.AI uses machine learning and anonymized mobile location data to improve driver risk profiles. By analyzing the aggregated and anonymized road behavior of a large sample of the population, we can generate differential risk profiles for very granular geographic areas.    


We propose the term “A - UBI” for Aggregated Usage Based Insurance. The A-UBI method enables better understanding of driver behavior and exposure to risk - with no apps or sensors to install, and no infringement of client privacy.  


Our solution allows insurers to set rates in a way that is not only more profitable, but also more fair to their customers.

Download the full A - UBI white paper or ask for a demo below. 

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. 

Downtown AI A-UBI 2

Download the A - 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)

Authors: Jordie Fulton, Dr. Ofer Amram, Yaron Bazaz

Downtown AI whitepaper A-UBI copy.png

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