Faster processing of claims; reduced cost & time of investigation.
One of the popular applications of 'Computer Vision' has been in enabling the Autonomous Cars. The ability to process a video stream and continually extract information & context from it, is the cornerstone of self-driving. However, there is another under-appreciated application of Computer Vision (CV) in Automobile sector.
'Claims Processing' of Auto insurances is a worthy candidate to be disrupted by Computer Vision Algorithms. In this post, let's understand a few pain-points in claims processing and explore how AI/ML can solve them.
Claims Handling – Current Scenario
A claims process is initiated when the claimant submits a First Notification of Loss (FNOL) application. The insurer assigns each FNOL to a 'claims adjuster' who will inspect the vehicle, investigate the damage and causes leading to it. Based on the investigation, he will determine 'a fair amount' for settlement. Clearly, there is a significant investment of time, effort and resources in evaluating each claim.
For an insurer, this workflow must be repeated a hundred times, every week, and round the year. There is a clear case for automating the preliminary evaluation of FNOLs.
Motor Insurance Claims - USA (for 2017)
Here is what makes the things interesting. In our conversations with multiple Auto-Insurers, we learnt that over 60% of the claims eventually get categorized ‘low-value’ and/or ‘low-complexity’. The claims involving damaged mirrors, bumpers, grills, fenders, hood, headlights, tail-lights, body-dents and scratches fall under ‘low-value’ category. The ‘low-complexity’ cases refer to applications where investigation is straightforward, but the payout may involve a higher cost-value. An example could be ‘cracked windshield’.
In both cases, an in-person investigation is expensive and may not even be necessary. Oftentimes, it can introduce further delay (due to unavailability of adjusters) to the workflow causing customer displeasure.
The New-Normal (optimal solution)
Imagine an AR-powered Mobile App that will help the claimant record and upload a brief video of his vehicle, highlighting the damaged areas. It will factor-in the adequate lighting, focus, and ensure capturing the vehicle’s license plate and VIN number.
Once the video is uploaded, the computer vision algorithm will process it to first analyze the make and model of the car and then determine the external/visible damage to the car.
Today the Computer Vision algorithms are powerful enough to,
Based on the assessment by the system,
A Win-Win Scenario
The opportunity for insurers is obvious,
This approach is a win-win for both customers & insurers. Customers get ‘quick response and resolution’ while insurers ‘improve CSAT rating’ and ‘reduce the cost of operations’ significantly. Isn’t that cool?
Did you know? A recent study by J.D Power found that 42% of the claimants (of Auto Insurance) use mobile apps to submit photos & videos of their damaged vehicle. Furthermore, when insurers use the photos and videos submitted by claimants for the evaluation, the overall customer satisfaction went up significantly. Clearly, the claimants believe in sharing the damage details of their vehicle first-hand.
Now, that’s good right?
Would you like to share some interesting perspectives on the topic?
Or perhaps, explore this solution? Let us know.
AUTHOR: Jayanth Jagadeesh
He is a part of EVRY India and has years of experience in crafting technology (Software & IoT) solutions for Automobile sector.