LexisNexis Risk Solutions

August 1, 2017

In the past, commercial carriers have relied heavily on motor vehicle records (MVR) as the sole source of individual driver information for predicting commercial auto risk. However, MVRs alone cannot provide the insights carriers need for effective risk assessment. Now, carriers have at their fingertips a robust set of new data and analytics capabilities to layer on top of MVRs to get to the bottom of risk propensity at the individual driver level.

LexisNexis® C.L.U.E®. Commercial and LexisNexis® AttractSM for Commercial Auto Underwriting, in combination with telematics analytics solutions, show great promise in helping carriers get control over escalating loss ratios and drive greater profitability. It’s all about knowing your drivers.

In my previous post, I detailed how each of these three tools can help you better understand risk at the individual level. Now I’d like to share some scenarios so you can visualize the difference these tools could make to your business.

Putting it all together

Imagine one of your commercial auto clients has two drivers—Joe A and Joe B. You run MVR reports on both drivers. Joe A comes back clean. Joe B has a couple of moving violations. Based on this information, you assign Joe A a $500 premium and Joe B a $1,000 premium. If this is all the information you have at hand, the process is over.

But say you’ve decided to get to know your drivers, and you apply the tools we’ve talked about earlier. You run a C.L.U.E. Commercial driver search on both drivers. It turns out Joe A has a $70,000 loss with a prior employer. Joe B has a clean record. You make a premium adjustment for Joe A, as he’s starting to look a little risky, but you leave Joe B as he is.

Next, you run both drivers through the Attract for Commercial Auto Underwriting model and receive driver scores for each of them. Based on the expanded data the model has access to, Joe A turns in a low score—indicating a high propensity for loss. Joe B turns in a high score, putting him low on the loss propensity scale. Even though he has a couple of dings on his MVR, all other indications are that he is now the less risky of the two, with a low likelihood of precipitating a claim.

Based on these deeper insights, you decide to restructure the premiums—assigning a $1,500 premium to Joe A and sticking with the $800 premium for Joe B. Quite a different picture from your original one.

Telematics would add additional real-time information about ongoing driver behavior, allowing you to keep a pulse on these drivers and make premium adjustments accordingly.

Next steps

Now that you can see how these data assets provide a much more insightful view into individual drivers, you’re free to identify where the gaps are in your book of business and determine how you can best integrate one or more of these capabilities into your product framework to deliver improved business results. Knowing your drivers is your first step to reducing loss ratio and boosting your bottom line.