Driving computer vision into auto claims

Advances in automotive technology are breaking down silos and supporting partnerships between insurers and car manufacturers

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Advances in automotive technology to reduce accidents are occurring at a rapid pace.

These technology interventions will eventually lead to a better, safer tomorrow that protects people and companies, and enables insurers to thrive. By breaking down internal silos and supporting partnerships between insurers and car manufacturers, technology helps connect these ecosystems, collaborate, and share data to predict accidents before they occur.

Industry leaders will be the insurers that use technology like computer vision and other forms of artificial intelligence (AI) to drive predictive insights that will help them act and react at speed. At the same time this technology will also empower employees, creating a more agile, adaptive workforce.

This is the future of business: the instinctive enterprise. And key to its evolution is AI, connecting people, process, and domain knowledge to adapt instinctively, and make accurate, proactive decisions that benefit customers. The instinctive enterprise moves markets, reinvents business models, and amplifies the full potential of its people. It defines the next era of business in insurance.

The benefits of instant data

Computer vision, a technology that processes visual information and interprets data, can paint a fuller and more accurate picture of an auto accident, including the conditions, scene, and what repairs are needed.

When imagery is available — captured through cameras onboard vehicles or via street surveillance — computer vision technology can extract, analyze, and provide insights to aid and speed up the adjudication process, benefitting both insurers and the insured. It can determine who is at fault based on precise measurement analysis, road, and traffic conditions. So drivers who aren't at fault can breathe a sigh of relief.

Applying computer vision to vehicle imagery can also help assess damage post-accident. Algorithms trained on volumes of estimate data and photos can determine whether a car is repairable or a total loss, and list the parts damaged and to what degree, speeding up the repairs process and reducing the inconvenience for insureds. Soon this capability will be able to generate an initial estimate to further expedite the claims process.

Imagine how revolutionary this will be for drivers in accidents. Even before they return home or to the office, their insurer will have been alerted to the loss, approved the initial repair estimate, and booked it into the local auto repair center.

In the claims process, imagery using computer vision both before and during the accident provides tremendous visual data to analyze the weather, lighting, scene, speed, and traffic. These visuals contain many of the facts required to determine liability and feed into the adjudication of other issues, such as subrogation and injuries. In addition, computer vision also can help quickly decide the inspection path a vehicle should enter, and whether the claims process requires staff or third-party resources. Using technology to solve issues previously requiring someone else's eyes also helps lower loss adjusting expense.

Learning from driver behavior

Computer vision can analyze the factors associated with a driver's behavior preceding an accident. Over time, as an instinctive enterprise, insurers will combine various data sources to predict who may or may not be more prone to accidents. Machine learning technology will improve this analysis, accumulating more information from videos, imagery, telematics data, and even smartphones within the vehicle, deepening its understanding about the conditions, behaviors, and decisions contributing to accidents. When you think about it, knowing someone's speed is not as powerful as understanding — visually — the speed relative to that of other traffic, or its proximity to other vehicles.

Insurers will hone their instinct to determine whether driving habits played a role, or if the driver was the innocent party. Or were they simply in the wrong place at the wrong time? Companies can also use this data to reward drivers for good driving behaviors, which will improve traffic safety. In addition, fewer claims will boost the carrier's bottom line.

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The way forward for insurers

Insurers need to reimagine their systems, operations, and partnerships to successfully adopt computer vision. It will involve collecting and processing vast amounts of data. Carriers must have the right systems to capture inspections data in the form of pictures, videos, and annotations, and the security in place to safely store, access, and share data among key stakeholders.

By working with partners to access AI, data engineering, and other digital tools, insurers can take advantage of these new technologies as they come to market without waiting for them to become fully plug-and-play. They need to ensure that their claims processes augment new technologies and decide who is going to execute the outcomes.

The race is on

Like many digital technologies in the claims process, computer vision offers big benefits to both sides of the insurance contract.

Improved accuracy of liability, damage assessment, and subrogation efforts are just some of the ways insurer can streamline auto claims to improve the customer experience. By reducing many of the bottlenecks and pain points, consumers will benefit from more reliable decisions, speedier analysis, and shortened cycle times — all factors in an insurer's customer satisfaction score.

Insurers can expect to see a reduction in adjustment expense, improved accuracy of indemnity, and more accurate risk pricing and selection from more meaningful risk profiles.

Technology and insights are coming together in this age of instinct. The near future will change the financial transaction of buying insurance into something that reduces the stress and knock-on effects of an accident. The race to integrate this sea change into the claims processes is on and will be the major differentiator for insurers in the next couple of years.

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