Risk Insider: Ernie Feirer

Solving Commercial Auto’s Loss Problem

By: | January 9, 2017 • 2 min read
Ernie Feirer, CPCU, is Vice President and General Manager, Commercial Insurance, at LexisNexis Risk Solutions, where he is responsible for developing a suite of solutions for the commercial insurance market. He can be reached at [email protected]

Commercial auto has a problem. The combined loss ratio for commercial auto has increased 17 percent over the past 10 years, and more than 10 percent in four of the past five years.[1]

Yet these chronic underwriting losses are taking place in an insurance market that has experienced very strong growth for five years in a row, including $31 billion in direct written premium in 2015 (a 7.35 percent increase).[2] Clearly, rate increases are being offset by losses.

Why? A combination of factors, but the common denominator is the driver. The good news is, by understanding data around how drivers and their past behavior correlates with future losses, carriers can decrease loss frequency profitability.

Carriers have traditionally utilized motor vehicle records (MVRs) to understand specific driver behavior, focusing on violations and driving eligibility. While those records are helpful, they don’t tell the whole story. Not everything on the MVR translates to losses, and there are losses that are not visible in MVRs.

The unsustainable pattern of year after year loss ratios can be reversed with simply the right investment in data analytics.

Luckily, there are solutions that carriers can leverage that go well beyond the MVR and can help gain a better understanding of commercial drivers and, in turn, help close the gap in risk assessment.

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For example, contributory databases that provide automated loss runs based on searches not only by business but also by specific drivers can provide exactly the kind of deeper insight carriers need to underwrite more precisely. These contributory databases can help carriers research prior loss histories, identify claim patterns, identify carrier information and improve claim outcomes.

There are also driver-specific commercial driver models that use alternative data sources as a surrogate for consumer credit, giving carriers information about a commercial driver that has never before been available. These models provide a score for each driver on a policy/quote that allows the carrier to rank order individual drivers in terms of loss propensity. It opens up multiple options for incorporating the score into the carrier’s own proprietary model and rating plan.

These tools can provide insights on loss propensity by driver, enabling carriers to better understand your driving population and better segment your risk. This understanding enhances a carrier’s tiering factors, or at the very least provides more rigor around discretionary pricing.

Both approaches can complement the use of traditional MVRs to help carriers gain deeper insights and better evaluate risk. The unsustainable pattern of year after year loss ratios can be reversed with simply the right investment in data analytics.

[1] Source: Insurance Information Institute, based off of A.M. Best Data

[2] Source: Auto Insurance Report, June 2016

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