Editor’s observe: At this time we’re listening to from danger administration software program supplier Solera Holdings on how they remodeled their automotive claims course of utilizing machine studying from Google Cloud.
Caught on maintain together with your automobile insurance coverage claims division? If a fender-bender isn’t sufficient to ship your stress ranges by way of the roof, negotiating prices and insurance coverage deductibles with a claims adjuster most likely is.
At Solera Holdings, our enterprise is car harm estimation. We cope with round 60% of the claims worldwide between insurance coverage firms, drivers, and the automotive trade. Like something at present, when folks need their vehicles fastened, they need it accomplished as quick as doable. However not like different fashionable providers reminiscent of rideshare or meals supply, claims departments at your insurance coverage firm probably aren’t fairly on top of things. That’s why we determined to rework Qapter, our established claims workflow platform, right into a touchless clever claims answer.
Higher secure than sorry—however nobody desires gradual
After I joined Solera in 2020, I got here with the understanding that nobody specific synthetic intelligence (AI) or machine studying (ML) expertise could possibly be utilized to resolve each enterprise drawback, regardless of how progressive or disruptive that expertise is likely to be. In my expertise, fixing points at all times requires a number of in-house and cloud applied sciences. My imaginative and prescient was to successfully implement AI applied sciences to the correct issues to realize and keep aggressive benefits for Solera. So, I used to be delighted to find my staff was already manner forward of me and had been engaged on a strategy to remedy certainly one of their largest issues with the assistance of AI and ML.
Primarily based on enter from insurance coverage firms over time, the Solera product staff knew that prospects wished an AI-based claims course of. Whereas restore estimation expertise has developed from estimation spreadsheets to three-dimensional fashions, fashionable buyer expectations are quick outpacing yesterday’s options and processes. Sadly, many insurance coverage suppliers take a “better safe than sorry” strategy to current methods, and the tip result’s a buyer expertise that’s as irritating as it’s gradual. It was clear this was an space that was ripe for enchancment, and with our lengthy historical past of reworking the insurance coverage and automotive trade, we wished to be those to crack the case.
The problem with any AI mission is making use of the correct applied sciences to the issue at hand. It’s important to know the house and scope so we are able to use expertise successfully, or danger falling quick. A number of insurers had already tried (and failed) to make use of laptop imaginative and prescient to automate the collision harm restore course of. Whereas they managed to construct working in-house options, all of those AI tasks in the end bumped into points when it got here time to scale.
What may we do in another way to keep away from failing as an AI mission? First, we stored our focus slender, solely taking a look at methods to use AI to determine car harm within the collision claims workflow, not your complete restore course of. We then selected to reinforce our current backend methods with ML to leverage our substantial current database of proprietary automotive photographs and components catalogs to streamline the method of providing exact strategies, value, and time estimates for repairs.
Moreover, earlier than I arrived at Solera, the staff had already constructed a earlier model of an automatic claims system that helped remove a number of much less profitable approaches. The unique model gave us a robust blueprint to work off and enabled us to reimagine Qapter’s full potential when mixed with the newest cloud and AI applied sciences. We knew the place we wished to go—all we wanted was the correct AI answer and the newest cloud applied sciences to assist us rework the preliminary harm evaluation into an AI-powered course of.
Google Cloud: An AI expertise toolbox with the whole lot we want
Our staff was already skilled with cloud expertise once we began on the lookout for an AI/ML answer that might combine with a full suite of superior cloud applied sciences. Whereas we host our personal knowledge lake for contractual causes with our prospects, our accident declare workflow was already cloud-based. We knew that choosing the proper expertise vendor can be crucial to a profitable end result for the next-generation platform.
After finishing a radical expertise bake-off, we discovered that Google Cloud’s AI/ML options had been extra subtle, sturdy, and scalable than what different distributors may supply. Having best-in-class applied sciences for constructing and deploying AI purposes, reminiscent of Google Kubernetes Engine and Cloud Run, that combine with your complete Google Cloud ecosystem performed a definitive function in our choice. In brief, Google Cloud had the whole lot we wanted to take full benefit of AI and ML options for processing touchless claims whereas additionally offering us with further subtle capabilities and tooling that quickens improvement and deployment somewhat than worrying about sustaining infrastructure.
The core worth of Qapter is its capability to know how the car consists utilizing 3D car fashions. We repurpose this knowledge and put it by way of completely different workflows, reminiscent of car inspection or collision estimation. Utilizing Imaginative and prescient API and TensorFlow, we constructed a system that enables us to gather and acknowledge claims data, reminiscent of car make and mannequin, harm data, and components required for repairs—all based mostly on collision photographs.
Beginning with Imaginative and prescient API’s easy picture processing, we used its optical character recognition (OCR) to gather license plates and VINs. We then used TensorFlow to construct customized algorithms and machine studying fashions for picture recognition and car knowledge extraction, which permits us to gather different necessary data like car make and mannequin, harm data, and components for repairs. As well as, Cloud GPUs (Graphics Processing Models) and TPUs (Tensor Processing Models) enabled us to speed up our knowledge mannequin processing and improve our capability to coach giant, advanced fashions quicker.
Now, all we want is an image of the broken automobile—and Qapter does the remaining. As soon as Qapter has the picture, it compares it towards our large repository of claims photographs to estimate the extent of the harm, acknowledges the car’s make and mannequin, identifies what components are wanted, and estimates the ultimate restore value.
From breakdown to breakthrough
We began rolling out the brand new Qapter in France and the Netherlands throughout 2020, and there’s little doubt that it has dramatically modified your complete claims expertise. Our prospects are thrilled with the brand new AI-based strategy. As a substitute of sending a claims adjuster to look at a car bodily, all a driver has to do now could be take an image of the automobile, add it, and begin the method.
It’s been a game-changer—inside months of the preliminary launch, Qapter may auto-authorize 50% of harm claims, lowering estimation prices by almost half. It has additionally offered an sudden profit throughout your complete harm claims worth chain throughout the COVID-19 pandemic. Whereas Qapter reduces time and prices for drivers, insurers, and auto restore suppliers—in the end, it additionally cuts down on the necessity for human interplay.
Even in a world of social distancing, mandatory providers should nonetheless be obtainable. Qapter retains the car restore cycle working easily, so drivers can get again on the street, restore outlets can proceed working, and insurance coverage firms don’t should ship out workers to evaluate claims in particular person.
At Solera, we wish to proceed creating and constructing new services and products on high of the brand new Google Cloud framework we’ve created. Laptop imaginative and prescient has a whole lot of purposes inside the harm estimation house, reminiscent of window and windshield harm, insurance coverage protection assessments, rental or lease returns, and fraud detection. Google Cloud isn’t only a spot answer for fixing a difficulty, it’s a core competency for us that may be leveraged throughout your complete firm.