CMT’s Model Offers the Most Reliable Way to Assess Driving Risk & Accurately Price Policies
CAMBRIDGE, Mass.–(BUSINESS WIRE)–Cambridge Mobile Telematics (CMT) announced today that Kansas is the 35th state to approve its latest telematics-based insurance scoring model – the most accurate and reliable smartphone solution on the market to assess driving risks. This approval ratifies that insurers offering policies in Kansas – as well as 34 other U.S. states – can work with CMT to use this scoring model to price policies in telematics programs, including those that use CMT’s DriveWell platform.
To accurately estimate driving risk, the scoring model uses driving behaviors such as phone distraction (both talking and texting) and at-risk speeding, in addition to traditional telematics factors like hard braking.
According to the National Association of Insurance Commissioners, linking insurance premiums more closely to actual individual vehicle or fleet performance allows insurers to more accurately price premiums, increasing affordability for lower-risk drivers. Further, a performance-based insurance policy gives these drivers the ability to control their premium costs by providing feedback and incentivizing them to adopt safer driving habits.
“Many insurers don’t have a sufficient volume of telematics data to develop their own model,” said Nick Paventi, Director of Insurance and Regulatory Affairs at CMT. “We’ve worked across the country to get CMT’s model to serve as a starting point that streamlines the process for insurers and provides a fast implementation path. We are delighted with the fast approval rates and believe it is a testament to the quality of analysis and the predictive capabilities of the models.”
CMT’s scoring model also ensures policyholders are being offered policies based on their actual driving behavior, helping them improve behavior behind the wheel while saving money in the process.
“With 6.5 million drivers on the road and millions of new trips analyzed weekly, CMT is constantly evolving and improving our ability to score and reduce risk,” said Lakshmi Shalini, CMT’s VP of Risk and Insurance Analytics. “We’re excited to take a leadership role in working with regulators around the country to make these analytic tools accessible to insurers, and through them, help policyholders improve their driving.”
The scoring models were developed by CMT’s data scientists and actuaries using telematics data from hundreds of thousands of drivers covering billions of miles. Working with insurance regulators to obtain approvals, Arkansas became the first state to adopt CMT’s latest insurance scoring model in January of 2020. Since that time, 34 more states have approved the model to date, including Arizona, Colorado, Connecticut, Delaware, Illinois, Indiana, Iowa, Kansas, Louisiana, Maine, Maryland, Michigan, Minnesota, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Vermont, Washington, West Virginia, Wisconsin, and Wyoming.
About Cambridge Mobile Telematics
CMT’s mission is to make the world’s roads and drivers safer. Since its first product launch in 2012 that pioneered mobile usage-based insurance, CMT has become the world’s leading telematics and analytics provider for insurers, rideshares, and fleets. CMT’s DriveWell platform uses mobile sensing and behavioral science to measure driving risk and incentivize safer driving, while its Claim Studio reduces the claims cycle time with real-time crash detection, crash reconstruction, and damage assessment using telematics and artificial intelligence. CMT has over 50 active programs with insurers and other partners, improving safety for millions of drivers every day around the world. Started based on research at MIT and backed by the SoftBank Vision Fund to fuel its rapid growth, CMT is headquartered in Cambridge MA. To learn more, visit www.cmtelematics.com and follow CMT on Twitter @cmtelematics.
Contacts
Cori Cagide
cmt@sourcecodecomms.com
