LONDON, April 13, 2016 /PRNewswire/ -- Having accumulated billions of miles of driving behaviour data and hundreds of thousands of related claims, Wunelli, a LexisNexis® Risk Solutions company, has developed Driver Signature' analytics for smartphone telematics apps after just 200 miles of journeys captured and without the need for confirmation from the policyholder that s/he was at the wheel. Driver Signature tackles the risk of fraud, using machine learning to identify when a policyholder is driving rather than in the passenger seat of a vehicle, in a taxi or on a bus, with a 15% improvement in accuracy compared to the rules-based approach commonly used. Logo - http://photos.prnewswire.com/prnh/20160411/353751LOGO Driver Signature is identified using a range of metrics based on where, when and how motorists drive - characteristics that are individual to each driver. After collecting a certain number of trips meeting the minimum driving distance, a set of driving pattern attributes will be developed and a Driver Signature analytics model applied to classify the trips as either a driver trip or a passenger trip. This is then used as the basis for scoring all future journeys. There are recommended default settings, but the insurer will be able to choose customised parameters, including the minimum number of trips and minimum distance of trips, as well as the classification threshold. There is no customer interaction needed during this process, although insurers will have the option to manually override the model classification results in the app. Paul Stacy, Founding Director of Wunelli said: "When, where and how motorists drive is entirely individual to them and we call this unique identifier Driver Signature analytics. This key fact has been central to the development of telematics-based insurance and in particular to smartphone telematics apps as a more cost-effective alternative to fixed black boxes. Insurers have needed to trust that the policyholder was the person driving the car and not their partner, friend or parent, and that they were actually in a car, not a taxi, train etc. Identifying them entirely through their driving behaviour data without any reliance on confirmation from the customer, has been key." "The advances we have made in Driver Signature analytics represent a significant step forward for telematics apps. This improves both the customer experience and removes the risk of fraud - that is, policyholders claiming to be driving when they are not and vice versa. This development should bring insurers and brokers a new level of confidence in the use of smartphone apps as a means to collect driving behaviour data to support telematics based insurance." Wunelli Working closely with all major software houses, today, Wunelli provides a complete end-to-end solution to enable insurers and brokers to offer telematics-based motor insurance products, in a fast, efficient and cost-effective manner. Whether it is hard-wired, OBD2, 12V or smartphone solution, Wunelli offers a completely outsourced solution or an integrated package that links telematics to existing internal systems. LexisNexis Risk Solutions LexisNexis Risk Solutions is a leader in providing essential information that helps customers across industries and government predict, assess and manage risk. Combining cutting-edge technology, unique data and advanced analytics, LexisNexis Risk Solutions provides products and services that address evolving client needs in the risk sector while upholding the highest standards of security and privacy. LexisNexis Risk is part of RELX Group, a world-leading provider of information and analytics for professional and business customers across industries. http://www.lexisnexis.com/risk/ To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/wunelli-evolves-driver-signature-to-take-telematics-apps-to-next-stage-of-growth-300250572.html
US - based fraud company Wunelli has developed the Signature app . The smartphone app , which has been developed to help insurers detect fraud , can determine whether an individual was a driver or a passenger on trips .