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In this May 23, 2014 file photo, traffic moves on the Interstate 495, the Capital Beltway, in Hyattsville, Maryland, outside Washington. (AP Photo/Carolyn Kaster, File)

The same insured risk characteristics can be viewed differently through the lens of machine learning, where we attempt to create the optimal map to the policyholder's estimated risk rather than taking a series of winding streets. This perspective suggests that since the vehicle is garaged in a traffic-congested area, it's best to drive at night because fewer cars are on the road. The policyholder's youthful inexperience could be viewed favorably with nighttime driving when correlated with improved eyesight and reaction time.

Finally, making sharp right turns might be expected on narrow city streets that intersect at tricky angles — and also far preferable to left turns, which some generally regard as riskier. Looking at a homogenous cohort of policyholders who exhibit these exact same risk factors, it might be observed that this hypothetical policyholder, on average, experiences lower losses than the traditional approach would predict.

Decision tree application

If we were to describe the previous paragraph as an application of machine learning, it would essentially be a decision tree application. A decision tree algorithm generally helps the inquirer to discern which risk factor, such as local traffic density, best explains losses for a particular cohort and then divides the sample into two (or more) groups — such as high versus low.

Within each subgroup, the method similarly identifies the most salient explanatory factor, such as whether the primary operator is young. Then within the youthful subgroups, the method may identify the most salient explanatory factor, such as whether the vehicle is typically operated late at night. Note that the risk factor need not be the same for each subgroup. The automated process continues in this manner until a human intervenes or until the automated process cannot continue splitting information in this manner.

The elegance of this approach is that, in a low-traffic-density subgroup, predictions may hold true about youthful inexperience amplifying risk, with risk increased by late-night driving — corresponding with the predictions of the traditional multivariate regression. But for the higher-traffic-density subgroup, youthful inexperience may prove less of a factor, and (when combined with late-night driving) may significantly reduce loss propensity.

In other words, changing one risk factor, even with other elements being equal, can mean a world of difference. By defining groups based on risk characteristics, decision trees have the potential to save us from making that one “wrong turn” that could conceivably have us traveling quite a distance in the wrong direction.

With respect to UBI, machine learning approaches can reveal perceived drawbacks as potential pluses. For example, a demonstrably risky driving behavior, such as applying intense braking pressure, may occur infrequently and perhaps even be misinterpreted when it does occur. Consider if a vehicle's brakes are applied to avoid a pedestrian collision in an area where there's a high volume of foot traffic. One could argue that defensive braking under such circumstances should not be penalized.

More variables into risk equation

A decision tree may identify braking as less significant in areas with high pedestrian traffic and, in turn, bring other variables into consideration under these circumstances, such as how fast or often one drives during congested times of day or when pedestrians are more likely to enter the road unexpectedly. Taking such an approach has the potential to bring more variables into the risk equation, which, from a UBI perspective, may be one potential benefit.

There will likely always be some people who prefer to use their own feet (and intuition) as opposed to relying on vehicle navigation. Similarly, data scientists may have valid reasons for electing multivariate regression over machine learning — or might opt for some combination of both. In any case, applying machine learning to UBI data demonstrates that, in some contexts, the optimal path to ratemaking is the one less traveled, however the traveler gets there.

Related: Here's what you don't know about today's personal lines business

Jim Weiss is director of analytic solutions for Verisk Insurance Solutions, a Verisk Analytics (Nasdaq:VRSK) business. Udi Makov is head of actuarial research, Verisk Telematics Innovation Center, Tel-Aviv, and professor of statistics, director of the Actuarial Research Center, University of Haifa. Opinions expressed in this article are their own.