How Auto Insurance is Modernizing Using "As Built" Information for Vehicles

3 min read
Aug 29, 2023 4:00:00 PM

Quick Quiz: Test how well you “Know Your Vehicle”

Which of the following makes knowing your vehicle easier, faster, less expensive, and more accurate:

  1. Asking a customer for detailed technical items about a car they purchased a few years ago
  2. Looking up a vague description of that car from sales brochures
  3. Talking with a colleague, supervisor, or agent about their automotive knowledge
  4. Examining fine grained factory instructions from the manufacturer on how it was built
  5. Using the vehicle identification number to pre-fill the "as built" details you need to know about it

Did you answer e?

Automatic form filling processes remove the confusion, doubt, costs, and errors of manual processes. When a single source of accurate information is available, multiple guesses, opinions, and duplicate or triplicate efforts seem obviously tedious and wasteful.  

Why would anyone NOT want the right data, the first time, right away?

Auto insurance companies that process policies and claims every day for the current 250+Million fleet of U.S. vehicles in operation will particularly value the speed and accuracy that VIN pre-fill data provides. Thankfully, the accessibility of this intelligence has caught up with the need. While there have been VINs commonly in place since 1981, recent advancements and OEM participation have made these data assets available at scale. As of late 2019, dedicated teams have created a detailed, insurance-specific semantic taxonomy that makes these underlying data easy to use in everyday applications.

How to Gage if Your Historical Processes Need a VIN Pre-Fill Upgrade

You can quickly assess the value of a VIN pre-fill operation by looking at historical policies and claims that were filled in by customers, agents, adjusters, or employees and doing a Fact-to-Impact analysis:

  • What fields historically were either blank or wrong?
  • Which of them influenced a premium discount?
  • Which were needed for a claim assessment?

From here you will be able to identify evaded premiums, missed discounts, and time spent chasing details in a claim. If you have any or all of these costs, they are directly related to bad or missing data which could be fixed with accurate, pre-filled "as built" information.

The repeatability of insurance processes means that all the holes you identified in a retroactive study still exist going forward, so you can clearly map your ROI for places you know you have issues. Once existing data holes are filled, you can start to create new value. Any innovations or projects that were stalled or never got off the ground due to insufficient or unavailable data can now be revisited.  

While several companies have started to integrate “as built” data into their processes, don’t be frustrated if you either haven’t heard of this before or if you’ve tried and gotten weak results. As we mentioned previously, capabilities have evolved over the last five years and have more recently been adapted at internet scale with common feature mapping across manufacturers and expanded brand participation to now capture most of fleet. Now that you know about the availability of this intelligence, however, it’s time to get moving. The amount and type of technologies, materials, power systems, and optionally available and installed features on vehicles have mushroomed in the last five years as well. As vehicles continue to become more complex, it’s critical to have a clear understanding of exactly what exists on the specific vehicle you insure in order to provide accurate coverage for both your customers and your brand.

Quick Test: Looking for a way to visualize the pre-fill advantage?

We set up the quick time-in-motion study below to demonstrate the cost of data capture vs. the cost of data pre-fill on a like-for-like basis.

Get out a stopwatch and run this quick drill: see how long it takes you to type in the five VIN examples below (these are made-up VINs for this test case). We did not quality check the results, but just typing these side-by-side example took us a little over 2 minutes.

Example Typed String
1C6SRFFT8LN12XX84 (Chevrolet) 1c6srfft8ln12xx84  
2T3R1RFV8LC12XX84 (Toyota) 2t3r1rfv8lc12xx84
5UXCR6C07N9N2XX84 (BMW)   5uxcr6c07n9n2xx84
3C6MR4AJ0NG12XX84 (Chrysler) 3c6mr4aj0ng12xx84
1FMCU0G65NUB2XX84 (Ford)   1fmcu0g65nub2xx84

So the time in motion math of 5 VINs every 2.2 minutes (if that was all we did all day error free and with no setup expense) would work out to about 15 cents per VIN for every place in every system it is manually entered now (example: $20/hour divided by 60 minutes/hour is $0.333/minute, times 2.2 minutes is $0.73  per 5 VINs works out to about 15 cents a VIN every time it is typed manually).

Much more important than the cost of VIN capture and pre-fill are all the decisions you are making, or not making, with a fully informed “as built” understanding at the beginning of all your processes.

Stay tuned for Part Two where we’ll explore the impact of and improvements in vehicle valuation accuracy. 

We’re just scratching the surface on the breadth of better vehicle data that is now available to insurers to inform and improve their pricing and valuation strategies. If you’d like to discuss vehicle data more in-depth, let’s connect.

Where to find more insights like this:
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Reduction of operational expenses can be made using pre-fill from VIN, but getting the best possible "as built" information enables better decisions as well for a much larger impact on customer experience, future growth and profitability, and data governance.


You may also like:

Ultimate Guide to Vehicle Data

Catalog Data: Every Vehicle Everywhere All at Once

What can you really learn from a VIN?

Build Data: Your Insured Vehicle’s Birth Certificate

Feature Data – All in the Details

Know What You’re Insuring: Putting It All Together


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