Canada, Toronto
Market: Auto Maintenance, Insurance, Transport, Artificial Intelligence
Project stage: Operating business
Also this project:
-
Find investments
- Participate in the Unicorn Battle
Idea or High Level Concept
Commercial auto insurance is expensive & the number of commercial vehicles is on the rise. Foresight gathers and analyses data from vehicles to predict claim costs to be used in pricing models. We leverage data from existing video telematics in fleets, to provide ongoing risk pool management tools to underwriters. We help insurance companies contain losses by identifying high risk drivers and fleets, by using self-driving machine learning models. Foresight is the connecting layer between video-telematics and insurance.
Territory of the product or service implementation
North America
Traction and Current Status
Working with a number of fleet companies who have our solutions deployed. And are on the heels of some pilots with Insurance companies looking to deploy it further for understanding the value add from the technology.
Problem or Opportunity
Commercial Auto Insurance is loosing money every year because of:
Archaic pricing techniques
High overhead and commission fees
High claim and verification adjustment costs
Underwriters require low 90s loss-ratios to reach profitability
To compound the problem, mobility is trending towards Commercial Auto and away from passenger vehicles as sales of each type of vehicle suggest over the past 4 years.
Solution
Intelligent connected auto insurance is a machine learning based cloud run software that taps existing video telematics devices installed in fleets for logistical reasons and provides deep analytics for the purpose of pricing and understanding risk.
Competitors and Existing Alternatives
Swiss Re,
GeoTab
IMS
Otonomo
Netradyne
HDVI
Advantages or differentiators
We are focused on camera based (vision) analysis of driving in order to understand the full context of what happens on the roads.
Further more, we use vehicle self-driving technology and apply it to the video-telematics data and space in order to solve a broader problem around assessing risk, giving us the most comprehensive understanding of the task of driving and hence rating it.
Money will be spent on
Growing our deployments and install base of devices
Increasing number of partner video-telematics providers
Executing on several booked pilots with big insurance companies who are keenly interested in learning more about deep vision based analytics
Big Data infrastructure
Sales & Marketing
Expand Implementation and Software Team
Research and Development on Machine Learning
