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.

Sophisticated data science

Now consider applying an alternative approach to usage-based insurance (UBI) programs, which typically offer discounts for safe driving behaviors confirmed by vehicle-generated data. UBI programs can be fertile ground to test various data science techniques that constitute the field of advanced pattern recognition, commonly known as machine learning. By using an alternative approach that relies on sophisticated data science techniques and considers potentially more powerful variable combinations, one could conceivably arrive at similar, if not better, results — and avoid unnecessary twists and turns in the process. Think of the swift-footed traveler.

This approach applies such sophisticated decision-making tools as decision trees, random forests, and neural networks. These tools can be applied to help the inquirer quickly discern the set of variables that most closely "maps" to each policyholder's estimated loss propensity. Such an application would reduce the need to use every significant variable that could potentially apply to each policyholder — which occurs with traditional approaches even though some variables might not always be relevant for all cases.