LexisNexis Risk Solutions Boosts Machine Learning in Driver Signature Telematics Programs

The solution’s sophisticated machine learning techniques and analytics identify an individual’s driving patterns, improving consumer experience and representation.

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LexisNexis Risk Solutions reports that it has improved its machine learning in Driver Signature analytics, the vendor’s proprietary technology for insurance telematics programs, increasing accuracy when identifying the driver for a given trip based on driving style. With the improved capabilities, after collecting a certain number of trips meeting the minimum driving distance, an individual driving pattern can be established, according to LexisNexis Risk Solutions. The model requires nominal future interaction with the policyholder to designate that s/he was at the wheel, rather than in the passenger seat of a vehicle, in a taxi or on a bus.

David Lukens, Director, Telematics, Insurance, LexisNexis Risk Solutions.

David Lukens, Director, Telematics, Insurance, LexisNexis Risk Solutions.

Driver Signature draws from the billions of miles of driving behavior data accumulated since LexisNexis created its telematics platform in 2009.  The telematics platform has the ability to collect, process, normalize and score driving data from smartphone apps, as well as OBDII devices, 12 volt devices and connected cars from auto makers.

“Identifying a driver entirely through his or her driving behavior data with minimal reliance on confirmation from the customer is key to a better customer experience,” comments David Lukens, Director of Telematics, LexisNexis Risk Solutions. “Driver Signature analytics is a unique identifier which identifies drivers based on their individual driving habits and patterns, and continues to learn over time to become more accurate as it analyzes the driver’s behavior.”

Driver Signature uses machine learning and advanced analytics to identify when a policyholder is driving, which enables insurance carriers to offer telematics programs without the need for an additional aftermarket device to be installed in the vehicle. Once a set of driving pattern attributes are developed, a Driver Signature analytics model is applied to classify the trips as either a driver trip or a passenger trip. The model is then used as the basis for scoring all future journeys.

Establishing Driver Identity

“Up until now, U.S. insurers offering usage-based insurance (UBI) policies needed to trust that the policyholder was the person driving the car and not a partner, friend, young driver or parent,” a Lexis Nexis statement notes. “Driver Signature analytics can reduce fraud risks from policyholders claiming to be driving when they are not and vice versa, and offer insurers a new level of confidence in the use of smartphone apps.”

Driver Signature comes with recommended default settings, and insurers will be able to choose customized parameters, including the minimum number of trips and minimum distance of trips, as well as the classification threshold. The process requires limited customer interaction, which makes it a critical enabler of continued consumer adoption of UBI, Lexis Nexis Risk Solutions says.

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Anthony R. O’Donnell // Anthony O'Donnell is Executive Editor of Insurance Innovation Reporter. For nearly two decades, he has been an observer and commentator on the use of information technology in the insurance industry, following industry trends and writing about the use of IT across all sectors of the insurance industry. He can be reached at AnthODonnell@IIReporter.com or (503) 936-2803.

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