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Jason Doring

Recent Posts

Future-Proofing Your Auto Insurance Risk Analysis Modeling - Get the Whitepaper

The automotive industry is running parallel races toward fully electrified and fully autonomous vehicles, and both races have a deceptively close finish line. EVs will become the norm within 10 years due to a combination of government legislation and manufacturer mandates – Progressive estimates that EVs could comprise 40% of car sales by 2030. Vehicle autonomy, meanwhile, remains a top priority for OEMs as well, with the market projected to reach $200 billion by the end of the decade. Mercedes-Benz is already certified for Level 3 autonomy, a standard that encompasses conditional hands-free driving, suggesting that Level 5 (totally automated driving) is a feasible goal for automakers more quickly than initially anticipated.

The technology involved in developing electrified, autonomous vehicles will not be universal; each feature package will have its own unique benefits, drawbacks, and relationship to road conditions and driver capabilities. Consequently, auto insurers cannot treat “ADAS” or “EV” as monolithic or even loosely segmented variables in their risk analysis; all levels of the organization must have a comprehensive understanding of their car’s technology stack to maintain a balanced risk-to-rate ratio. 

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Topics: VIN, Insurance

Pattern-Level vs. Option-Level VIN Decoding: Which Does Your Business Need?

VIN Decoding solutions offer multiple levels of information for vehicle inquiries. Pattern-level VIN decoding provides information from the World Manufacturer Identifier (WMI), Vehicle Descriptor Section (VDS), the vehicle’s model year, and production plant. Option-level VIN decoding includes the serial number of the specific vehicle and often presents “as-built data” from the Original Equipment Manufacturer (OEM), such as installed options and packages, as well as the exact exterior and interior colors.

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Topics: VIN

Clear and Actionable Vehicle Data is Now Essential for Auto Insurers

Traditional risk models in the auto insurance space incorporate multiple data points, including vehicle details, the driver’s safety record, the vehicle’s geographic location, and that location’s associated weather patterns. While these factors may be weighed differently by each insurer’s model, the entire auto industry is now facing the reality that increased vehicle complexity will force a significant change to risk analysis. 

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Topics: Automotive Data, Insurance