How mobility data helps reduce distracted driving
The automotive insurance industry is heading towards dramatic change. Telematics is enabling both increased accuracy of existing business as well as the transition to new forms of mobility. By strategically leveraging the right data, insurers can go on the offense and not be victims of it. Insurers must become increasingly responsive to the changing needs of their customers and create insurance solutions that take the changing landscape into account, enabling customers to experience mobility options seamlessly and efficiently.
At Arity, we believe that capitalizing on this opportunity today starts with the mobile device. It’s a catalyst for many of the changes happening in transportation – most notably the gig economy and the increase in distracted driving. But it’s also a relevant and reliable tool that can offer insights to help understand true driving risk and enable a better transportation system for everyone.
At the Auto Insurance Report National Conference last month, our President Gary Hallgren shared the importance of having an accurate understanding of distracted driving, and how telematics with associated loss data must be used to quantify and help reduce this risky behavior.
Didn’t get a chance to attend? No worries, we’ve packaged up some of our favorite knowledge nuggets from Gary’s presentation below. Check out these key takeaways.
Telematics with associated loss data must be used to quantify and help reduce this risky behavior.
Use the right data from the right sources to build the best models
Auto insurers that adopted credit score data to price premiums gained significant competitive advantages. Combining credit data with insurance claim data, they created very predictive models that allowed them to out segment the competition. Today, a similar opportunity is presenting itself for companies to crest the next wave of competitiveness: mobility data. For the past decade or so, we’ve learned that using driving behavior data-based models are an even better predictor of loss, but only when the right data elements are applied correctly. So, what does that mean exactly?
Telematics data from onboard diagnostic (OBD-II) devices launched the revolution of usage-based insurance (UBI). This type of data has proven to be the source of truth for verifying mileage, detecting speeding, hard braking, and the time of day that a vehicle is driven. While useful, device-based data doesn’t provide the full picture of who is driving the vehicle and what they’re doing behind the wheel, preventing insurers from truly understanding how risky a driver is.
With distracted driving data from mobile phones, we can identify if a driver is sending texts at 65 mph or checking a navigation app at a red light.
Data from consumers’ mobile devices offers predictive insights and allows you to contextualize detailed behaviors from a trip. For example, with distracted driving data from mobile phones, we can identify if a driver is sending texts at 65 mph or checking a navigation app at a red light. We’ve determined to incorporate these insights into models are more predictive than verified mileage or credit history – but only when matched with claims data. For any model to accurately predict losses, you need to know if an accident occurred, if a claim was filed, and the cost of the claim.
Relying primarily on OBD data to build a mobile score can result in significant mispricing. Even good normalization can’t correct for differences. For a driving risk score to be most predictive, the modeled data should be based on the type of sensor in use. And finally, any model built on an analysis of driving behaviors must account for traditional rating factors that are already in play – such as age, geography, years of driving experience, and type of car – to avoid double counting the impact of these correlated variables. Too often, we see insurers investing a lot to collect the right data, but end up pricing less accurately because the right measures to avoid double counting have not been taken.
It’s time to get smarter about distracted driving
Distracted driving is a widely known issue, but just how much this behavior impacts an insurer’s bottom line has historically been unknown. At Arity, we analyze distracted driving behaviors by implementing naturalistic test settings and leveraging video data to pinpoint dangerous driving habits. Insights from this distracted driving data can help insurers accurately account for risky behaviors in their pricing.
It’s estimated that since 2011, distracted driving has cost insurers $9 billion and Arity research correlating mobile sensor data with associated insurance claims data determined the most distracted drivers have a loss cost more than 1.5 times higher that of the least distracted drivers, on average.
While we all agree that any phone usage or distraction when behind the wheel is dangerous, when trying to predict for insurance losses, our analysis shows that not all distracted driving behaviors are equally risky. For example, we can now distinguish opening GPS at a red light from texting while driving and texting while driving will likely yield a different insurance loss than checking directions at a red light.
Mobile telematics pricing, especially that accounts for distracted driving, can not only save money but also save lives.
And finally, mobile telematics pricing, especially that accounts for distracted driving, can not only save money but also save lives. The mobile phone is the one tool that not only captures this risky behavior but gives a direct feedback channel to empower consumers to control their pricing and develop safer driving habits for the benefit of themselves and everyone around them.
The distracted driving data you need to activate change already exists
So, if you’re serious about pricing more accurately and tackling distracted driving, then it’s time to get serious about incorporating the right data into your models. Adding the mobile phone to your toolkit is the key to determining a better prediction of insurance loss that you can act on strategically and smartly. The mobile phone is the first step in accumulating the distracted driving data that helps account for driver risk and encourages safer habits.
But accumulating and analyzing this level of high frequency, granular data is challenging and takes time. If you haven’t already, there are opportunities to quickly catch up and gain a complete understanding of the risk on your book. So where to start?
Over the past decade, Arity has made significant investments in accumulating data (as of May 1, nearly 130 billion miles of driving data and 16 million active connections) and data science expertise to help insurers with the heavy lifting. In addition to the insurance programs we’ve powered over the past decade – such as Drivewise and DriveSense – we’re partnering with third-party applications to leverage the driving behavior data your customers are already sharing to deliver insights and data models you can use immediately to account for distracted driving and loss.
Want to learn more about preventing distracted driving? Check out our blog on steps insurers can take to solve for distracted driving and read our distracted driving insights.