Usage-based insurance telematics have been used in underwriting and in developing new products, but industry experts say claims can benefit from it too.
In a July 2015 blog, Strategy Meets Action partner, Monique Hesseling, explained that telematics is useful to help determine who is at fault in an accident and urged insurers to consider incorporating telematics in claims handling.
“Right now, particularly in personal lines, telematics is used primarily for market segmentation, product, and underwriting purposes. There is a growing appreciation, though, of the value of telematics in claims handling beyond accident avoidance and driver education,” Hesseling wrote.
A telematics device, according to Allstate’s website, is a system installed in a car that records information about the operator’s driving habits, miles driven, speed and braking habits. Right now, insurers like Allstate and Progressive offer discounts for good driving behavior.
According to the National Association of Insurance Commissioners, by analyzing driving data like hard breaking, speed and time during an accident, telematics can help insurers more accurately estimate damages after an accident and even reduce fraud.
Wunelli, a LexisNexis company and telematics provider for usage-based insurance providers, announced that it had reached its billionth mile of UK driving behavior tracked, analyzed and scored this year. The data is collected via fixed telematics in car devices as well smartphone telematics apps.
“Telematics data have proven to be very powerful rating factors. The one billion miles of data collected includes 450 vehicle makes and models, five million GPS data points per day and collectively 125,000 earned car years,” said Paul Stacy, founding director of Wunelli. “The key to creating real value from the data is in its validation, cleansing and scoring, which leads to better risk assessment, customer engagement and retention and safer driving.”
So far, the data the company has collected has revealed that women are better drivers than men, identified the cars on the road driven by individuals most likely to speed and confirmed that key accident triggers include hard accelerating, braking, night time driving and speeding.
The data derived from telematics is useful in identifying risky drivers. In a study carried out by driver behavior specialist CAS on data provided by Risk Technology, data taken from nearly 1300 drivers insured by a UK carrier found that 104 had claims made against them for crashes where they had been assessed as being at fault.
Using a driving score, the study identified 40 percent of the drivers who had been at fault for a claim. Predictions based on driving score resulted in a large number of false positives – 20 percent of policyholders who had not had an at fault claim should have.
The study considered four types of reasons for crashes:
- Hazard perception;
- Basic steering (mostly concerned with reversing errors);
- Loss of control;
- Maintaining a safety envelope (i.e. driving too close to other road users).
The study revealed that drivers who were involved in loss of control crashes tended to have very poor braking scores. Those involved in crashes where a poor safety envelope or hazard perception were factors scored even worse for braking. Drivers involved in crashes where basic steering errors were the main factor were found to have average braking scores. The study found that braking scores were exceptional predictors of loss of control crashes, but not good in predicting other types of crashes.
A similar study conducted by Progressive of its more than 12 billion miles of Snapshot data confirmed that hard braking was the most accurate indicator in predicting future crashes.
Reviewing the most aggressive one percentile of stops, the data revealed that it took drivers 12 seconds to come to a complete stop when traveling 60 mph. At the other end of the spectrum, the most gradual one percent of stops from 60 mph took a full 40 seconds. Snapshot date found that the average driver falls in the middle at 24 seconds, which equals the distance of 4.2 football fields.
“After analyzing Snapshot driving data, we’ve found hard braking to be one of the most highly predictive variables for predicting future crashes,” said Dave Pratt, general manager of usage-based insurance for Progressive. “We know that one of the main contributors to hard braking is tailgating, so we’re using our data to help drivers be as alert and aware as possible on the road. We’ve gathered billions of miles of driving data and are only just beginning to scratch the surface in terms of the types of predictive behavior our Snapshot analytics can reveal.”
Progressive’s Lead Foot Report also found that the safest male drivers had 77 percent less hard brakes than the most aggressive female drivers. A surprising finding – though teens are often thought of as the riskiest drivers, the study found that the safest quarter of 16 year-olds had fewer hard brakes than the average driver in every other age group.
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