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LexisNexis Solution Predicts Commercial Auto Scores

Staff Writer | May 06, 2014

LexisNexis Risk Solutions announces the availability of LexisNexis Attract for commercial auto underwriting, a proprietary predictive analytics model. Attract can provide insurers with higher match rates and more accurate loss prediction of commercial auto risks than traditional commercial auto underwriting methods, which can represent up to a nearly six to one return on investment for a typical insurer.

“LexisNexis analysis shows that Attract for commercial auto underwriting can reduce an insurer’s combined ratio by up to four points,” says Ernie Feirer, vice president and general manager, commercial insurance, LexisNexis. “The level of fleet risk, for example, can be revealed at point of submission, enabling the insurer to select and price driver risk very early in the underwriting process.”

This analysis based on over five years of LexisNexis research on historical loss and policy data from commercial auto fleets.

Sustaining profitability in commercial auto insurance can be challenging. LexisNexis research has shown that the lowest scoring driver is the highest predictor of loss frequency within a policy. A 2014 First Advantage Commercial Driver Safety Report found a flurry of macro trends about fleet driver safety that make the ability for insurers to accurately predict risk of commercial fleets more important than ever.

According to the report, employment projections estimate that between 2010 and 2020, the transportation and material moving industry will experience 14.8 percent job growth, with nearly all the new jobs being motor vehicle operators and material moving workers.

 LexisNexis Attract ranks commercial insurance fleet driver risks using a non-credit based predictive loss model to calculate loss ratio and/or loss frequency. Leveraging individual and fleet-level commercial driver scores, insurers can improve pricing segmentation and make better underwriting decisions at points of risk selection, quote and renewal.


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