Fender benders. Bumper scrapes. Door dings. Parking lot taps.
These scenarios can happen to the best of us. And most people know that after a fender bender, the claims process can sometimes be emotionally trying because it includes adjusters, appraisers, rental cars and body shop visits.
In order to make the claims experience faster and more efficient, USAA and Google Cloud have developed machine learning models that will allow for nearly instant vehicle damage estimates from digital images.
Here is how it works: First, images of damaged vehicles are sent to Google Cloud. The images are then analyzed by Google Cloud models in real-time, which makes damaged part predictions that are returned to USAA. Next, the predictions are sent digitally to estimating and technology solution provider Mitchell International, whose platform maps the predicted parts to real-parts and incorporates them directly into an actual estimate. USAA appraisers then review the Mitchell estimate, and make changes as needed. The machine learning models and estimate integration help save appraisers time, and the appraiser feedback helps improve the models synergistically.
USAA has already begun to experiment with image-to-estimate through the use of drones and aerial imagery during catastrophes. Artificial intelligence will continue to streamline appraisals, enabling faster processing of claims by allowing appraisers to focus on complex cases and eventually supporting end-to-end touchless claims.
“The damage estimation process can be a complex, emotionally charged event, and we always aim to reduce friction and improve the member experience,” said Ramon Lopez, VP Innovation. “The future of customer experience in insurance is more convenient, cost effective solutions, backed by machine learning and computational power.”
In early tests, the machine learning models have been able to predict damage across a diverse vehicle set with a high degree of accuracy. USAA will continue to add features, unlocking the potential for end-to-end touchless claims in the future.