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Machine learning for UBI: An optimal path to insurance ratemaking?

Learn how insured risk characteristics can be viewed differently through the lens of machine learning.

By Jim Weiss, Udi Makov | Updated on June 17, 2016

Anyone who relies on automated vehicle navigation systems while driving in congested traffic knows to disregard the principle that the shortest distance between two points is a straight line. One-way streets, missed turns, and dubious directions often make reaching a destination by car seem to take far longer than by foot.

The same could be said of traditional insurance ratemaking approaches. Like a car navigating heavy traffic, the traditional ratemaking approach, otherwise known as multivariate regression, considers numerous rating variables to estimate risk. Sure, they may accurately reflect a policyholder’s loss propensity. But at times, it seems the path is more circuitous than necessary.

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