The telematics golden ticket: Accurate insurance risk data tied to historical claims

Not just any data will work for your telematics program

As top insurers embrace behavior-based auto insurance pricing, it’s clear there are financial payoffs for insurers and their best drivers. However, to guarantee an accurate and profitable telematics program, not just any data will do.  

When it comes to providing the level of insights it takes to succeed in today’s insurance market, telematics models built without actual insurance risk data fall short. So how do we fill existing gaps in assessing the risk profile of drivers? The answer lies in risk data tied to historical claims.

Busy highway with data points over cars.

Identifying the gaps in telematics data 

Although behavior-based pricing is the future of insurance, it’s no secret that gaps in telematics models currently exist. Resulting from missing claims data and an inability to determine which factors should be weighed more heavily, these gaps — and whether or not they are filled — can make or break your telematics insurance program. 

Incorrectly weighted scoring factors and missing claims data often result in models that fail to accurately account for all risk factors. Agents may underprice bad drivers, giving them deals they don’t deserve.  

This scenario is financially harmful to insurers, who have now retained higher-risk drivers. Alternatively, agents might overprice good drivers, resulting in lower-risk drivers leaving their business for better rates elsewhere. 

The bottom line? When insurers fail to accurately price drivers, the result is lost profitability, potential pricing dissatisfaction, and attrition from the best drivers. 

Leveraging claims data and risk factor weights 

Collecting insurance risk data is simple when the most important factors are kept in mind. Here are the three factors insurers need to consider for an accurate telematics program: 

  1. Claims data: Without enough actual claims data, extrapolated insights can negatively impact insurers’ rating plans and ability to price profitably. To avoid inaccurate risk prediction, insights and models should be built on hundreds of thousands of earned exposures and distinct claims, at a minimum. 
  2. Accurate risk factor weight: To guarantee program accuracy, insurers must weigh risk factors correctly. For example, if the number of miles driven is weighted heavier than abrupt stops and speeding, a low-risk driver who accrues more mileage may be inaccurately priced higher than a high-risk driver who accrues fewer miles. 
  3. Correlated factors/double counting risk: Insurers should be cautious to avoid double-counting risk among correlated factors. By taking traditional ratings into account during our modeling process, we avoid double-counting risk factors. As a result, driving behaviors in our score receive appropriate weights, providing the best starting point for true indicated factors. 

The downside? It can take insurers years to collect enough of this data from their own telematics program.  

With a telematics partner like Arity, you can fill the gaps in data and access the insights you need to quote individualized prices on day one, potentially increasing speed to bind for the best drivers. 

The power behind mobility data 

Arity is the only telematics provider that offers both mobility and claims data. Drivesight 3.0, our latest in telematics, is 4x more data-driven than its predecessor in terms of claims-tied data. In fact, the Arity Drivesight score was developed to weigh behaviors drivers have the most control over, like speeding, braking, and distracted driving. 

We’re able to reflect claims from vehicles in the U.S. that have logged billions of miles and trips. As a result, we can accurately anticipate overall insurance risk by: 

  • Including historical claims data 
  • Weighing the right risk factors 
  • Accounting for correlated factors 

Ditch the delay and learn more about closing the gaps in your telematics program.