Insurance company turns to location data to determine your risk score

Insurance companies are slowly but steadily migrating to next-gen approaches to more accurately determine a client’s risk score, and LOOP recently embraced an idea that’s supposed to kick off a new model that many are likely to embrace.

LOOP’s new system is based on an advanced system powered by TomTom maps, the purpose of which is to determine whether a customer is generally driving in an area where the probability of accidents is increased or not.

Called the road risk factor, this idea uses a personalized map that highlights the historical flow and density of traffic, with all the information provided by TomTom. Using an advanced system that also uses machine learning, LOOP then examines crash data to determine unsafe roads and determine if customers are driving there.

Drivers’ routes are entered into its machine learning model and are benchmarked against the risk factor of the routes taken. The resulting output from the model is an individual’s risk score, which is calculated for each month of the year. This risk score translates into the probability that an individual driver will be involved in an accident in the next 30 days, based on their last 30 days of driving,», Explains TomTom.

But that’s not all. LOOP also uses a second concept, this time based on an idea already adopted by others and which uses the mobile device that everyone already has in their pocket.

Using an AI-powered app, LOOP tries to monitor drivers’ behavior behind the wheel, thus trying to ensure that they are focused on a safe experience. If not, the app suggests all kinds of ways to encourage safer driving, while also recommending routes that avoid roads with increased risk of accidents.

Much like the rest of the drive monitoring apps, the LOOP implementation examines things like speed and hard braking, ultimately generating a score that helps refine the pricing model for each customer.

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Justin D. O'Neill