AI & Machine Learning
Next-generation claims: Transforming vehicle accidents with AI
Editor’s note: Today we’re hearing from risk management software provider Solera Holdings on how they transformed their automotive claims process using machine learning from Google Cloud.
Stuck on hold with your car insurance claims department? If a fender-bender isn’t enough to send your stress levels through the roof, negotiating costs and insurance deductibles with a claims adjuster probably is.
At Solera Holdings, our business is automobile damage estimation. We deal with around 60% of the claims worldwide between insurance companies, drivers, and the automotive industry. Like anything today, when people want their cars fixed, they want it done as fast as possible. But unlike other modern services such as rideshare or food delivery, claims departments at your insurance company likely aren’t quite up to speed. That’s why we decided to transform Qapter, our established claims workflow platform, into a touchless intelligent claims solution.
Better safe than sorry—but no one wants slow
When I joined Solera in 2020, I came with the understanding that no one particular artificial intelligence (AI) or machine learning (ML) technology could be applied to solve every business problem, no matter how innovative or disruptive that technology might be. In my experience, solving issues always requires multiple in-house and cloud technologies. My vision was to effectively implement AI technologies to the right problems to gain and maintain competitive advantages for Solera. So, I was delighted to discover my team was already way ahead of me and had been working on a way to solve one of their biggest problems with the help of AI and ML.
Based on input from insurance companies over the years, the Solera product team knew that customers wanted an AI-based claims process. While repair estimation technology has evolved from estimation spreadsheets to three-dimensional models, modern customer expectations are fast outpacing yesterday’s solutions and processes. Unfortunately, many insurance providers take a “better safe than sorry” approach to existing systems, and the end result is a customer experience that is as frustrating as it is slow. It was clear this was an area that was ripe for improvement, and with our long history of transforming the insurance and automotive industry, we wanted to be the ones to crack the case.
The challenge with any AI project is applying the right technologies to the problem at hand. It’s essential to understand the space and scope so we can use technology effectively, or risk falling short. Several insurers had already tried (and failed) to use computer vision to automate the collision damage repair process. While they managed to build working in-house solutions, all of these AI projects ultimately ran into issues when it came time to scale.
What could we do differently to avoid failing as an AI project? First, we kept our focus narrow, only looking at ways to apply AI to identify vehicle damage in the collision claims workflow, not the entire repair process. We then chose to augment our existing backend systems with ML to leverage our substantial existing database of proprietary automotive images and parts catalogs to streamline the process of offering precise methods, cost, and time estimates for repairs.
Additionally, before I arrived at Solera, the team had already built a previous version of an automated claims system that helped eliminate several less successful approaches. The original version gave us a strong blueprint to work off and enabled us to reimagine Qapter’s full potential when combined with the latest cloud and AI technologies. We knew where we wanted to go—all we needed was the right AI solution and the latest cloud technologies to help us transform the initial damage assessment into an AI-powered process.
Google Cloud: An AI technology toolbox with everything we need
Our team was already experienced with cloud technology when we started looking for an AI/ML solution that could integrate with a full suite of advanced cloud technologies. While we host our own data lake for contractual reasons with our customers, our accident claim workflow was already cloud-based. We knew that choosing the right technology vendor would be critical to a successful outcome for the next-generation platform.
After completing a thorough technology bake-off, we found that Google Cloud’s AI/ML solutions were more sophisticated, robust, and scalable than what other vendors could offer. Having best-in-class technologies for building and deploying AI applications, such as Google Kubernetes Engine and Cloud Run, that integrate with the entire Google Cloud ecosystem played a definitive role in our decision. In short, Google Cloud had everything we needed to take full advantage of AI and ML solutions for processing touchless claims while also providing us with additional sophisticated capabilities and tooling that speeds up development and deployment rather than worrying about maintaining infrastructure.
The core value of Qapter is its ability to understand how the vehicle is composed using 3D vehicle models. We repurpose this data and put it through different workflows, such as vehicle inspection or collision estimation. Using Vision API and TensorFlow, we built a system that allows us to collect and recognize claims information, such as vehicle make and model, damage information, and parts required for repairs—all based on collision images.
Starting with Vision API’s simple image processing, we used its optical character recognition (OCR) to collect license plates and VINs. We then used TensorFlow to build custom algorithms and machine learning models for image recognition and vehicle data extraction, which enables us to collect other important information like vehicle make and model, damage information, and parts for repairs. In addition, Cloud GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) enabled us to accelerate our data model processing and increase our ability to train large, complex models faster.
Now, all we need is a picture of the damaged car—and Qapter does the rest. Once Qapter has the image, it compares it against our massive repository of claims images to estimate the extent of the damage, recognizes the vehicle’s make and model, identifies what parts are needed, and estimates the final repair cost.
From breakdown to breakthrough
We started rolling out the new Qapter in France and the Netherlands during 2020, and there’s no doubt that it has dramatically changed the entire claims experience. Our customers are thrilled with the new AI-based approach. Instead of sending a claims adjuster to examine a vehicle physically, all a driver has to do now is take a picture of the car, upload it, and start the process.
It’s been a game-changer—within months of the initial launch, Qapter could auto-authorize 50% of damage claims, reducing estimation costs by nearly half. It has also provided an unexpected benefit across the entire damage claims value chain during the COVID-19 pandemic. While Qapter reduces time and costs for drivers, insurers, and auto repair providers—ultimately, it also cuts down on the need for human interaction.
Even in a world of social distancing, necessary services must still be available. Qapter keeps the vehicle repair cycle running smoothly, so drivers can get back on the road, repair shops can continue working, and insurance companies don’t have to send out employees to assess claims in person.
At Solera, we want to continue developing and building new products and services on top of the new Google Cloud framework we’ve created. Computer vision has a lot of applications within the damage estimation space, such as window and windshield damage, insurance coverage assessments, rental or lease returns, and fraud detection. Google Cloud isn’t just a spot solution for solving an issue, it’s a core competency for us that can be leveraged across the entire company.