Detect Vehicle Damage Automatically with AI-Powered Image Processing

The need for speed 

In 1913, there were 606,124 motor vehicles produced in the world. It was the eve of “automobility” taking the world by storm. The sweep was so rapid that just over a decade later the groundbreaking Model T was already retired from production. 

The breakneck pace of car-driven mobility hasn’t let up since. So much so, that in the U.S. every 60 seconds there is a car accident. And 94% of accidents or instances of vehicle damage can be attributed to human error.

Auto insurers pick up the pieces

That adds up to a lot of vehicle damage to be assessed and addressed—and paid for. The current size of the automobile insurance industry in the US (revenues) is $311 billion. On a global scale, the size more than doubles: According to a Research and Markets report, the 2020 auto insurance market was valued at $761 billion.

As a volume-based industry, insurers face a quicksand situation, in constant battle with losses due primarily to missed damage detection, rising claims processing costs, and fraud.

The issue of not detecting damage right away is exemplified by the case of missed total loss. Triage of damage failed to flag the total loss at the outset, resulting in weeks of lost cycle time and costs for attempted repairs. Adding to the loss: unhappy, out-of-patience customers.

Processing car insurance claims is the frontline for insurers and for the car insurance customer experience. The stakes are high, and insurers are battling rising costs for the specialized skills required to assess the damage. Without accurate assessment, there is a higher risk of overspending on repairs which is the other part of the claims processing losses story.

And let’s not forget about fraud.

The F-word

Fraud is not a theoretical threat in auto insurance. Fraud is happening. And it’s a big loss issue. Latest estimates peg 13%-17% of car insurance claim payments as excessive or fraudulent. 

Given the widespread love of automobile transportation, the numbers add up quickly.  Insurance scams are estimated to leave insurers with a $29 billion tab, including $3 billion that alone is associated with car accident damage.

A better horse

The famous Henry Ford quote “if I’d asked them what they wanted they would have said a better horse” is a lesson for today, too. Mobility is on the move. The ‘just around the corner’ autonomous vehicle future will dramatically shift mobility norms—and necessitate redefining auto insurance norms too.

To start, driverless cars are being designed to dramatically reduce the issue of human error. Remember those human-error accidents representing 94% of all incidents? Fewer accidents overall, and fewer driver-associated accidents, is indeed a dream come true. But according to Deloitte’s attempt to quantify the impact, this near-future also implies that car insurance premiums will fall by 33% over the coming decade, and market consolidation will be the name of the game. 

Making lemonade

Just like in 1913, the opportunity to get ahead of the industry curve is now. How? Decrease costs with automation and increase value/customer experience to policyholders to build brand loyalty.

New normal of vehicle damage detection?

At the same time as future projections spell disruption for automobile insurers, today and tomorrow carry disruptive forces, too. The pandemic made virtual vehicle damage claims a customer expectation and ignited a drive toward fully automated, or “touchless” claims processing by car insurers. 

But the race to the future of touchless claims processing, like driverless mobility, faces real on-the-ground hurdles.   

Vehicle damage appraisal is hard

It’s hard because it remains a complex task for human experts. That is someone who has years of training and experience before they are able to deliver consistent appraisals. 

That means assessing car damage takes hands-on time, and not just because the appraisal is happening in person. In-person inspections are slow and costly but so is the expert manual analysis of remote images from car claims. In both cases, damages and estimated costs provided to the insurer and customer are often not accurate enough.

Road ahead for touchless claims

There is an opportunity to make touchless claims possible by employing AI to process unstructured vehicle damage data. However, the challenge has been that AI for vehicle damage detection relies on massive data sets (in the millions of historical data points) that need to be correctly labeled. And that’s just the data.

Skilled AI talent to build and maintain this imaginary AI solution for vehicle damage detection is in short supply which makes it both hard to find and costly. 

In the case of ramping up an AI model for something like car vehicle damage appraisals, what does that mean? It means that for most car insurers building AI for processing unstructured damage data in images and videos is a dead end. 

Where car insurers can go from here 

Recently Forbes shared key business trends for 2022 which placed at #2 the need to combine human and automation in the right balance. 

Here is where Super.AI comes in. Super.AI works by maximizing the human + AI equation for faster, more accurate, MUCH more affordable applications of AI, right now.

What this means for vehicle damage detection

Super.AI’s solution, Super.Image breaks down images into components and uses the combined intelligence of trained labelers and AI to ramp up AI processing with unmatched accuracy, in a few short hours, using minimal historical data for training. Just in case you missed the math there, that’s zero development costs, zero AI talent sourcing costs and salaries, and next-to-zero AI ramp-up time. 

The benefits to vehicle damage detection and claims processing are wide-ranging. AI-driven processing of unstructured damage data eliminates the need for onsite visits. Instead, car drivers and owners can share remote, self-service assessments which reduce the time and cost of damage appraisals, with incident reporting through to appraisal happening in a matter of hours. And accuracy can continually improve with ongoing training and human-in-the-loop feedback on data output, while AI for vehicle damage detection offers the additional benefit of preventing fraud.

Hassle-free damage assessments mean happy policyholders. 

Take a test drive into the future of vehicle damage detection

Automating damage detection and estimation is possible today with Super.Image.  The results include reduced processing time, lower claims processing costs, and dramatically fewer errors.  

Want in on the future of AI for automobile insurance? Check out what Super.AI can do with a 1-on-1 demo.