Seeing the Past Is as Important as Seeing the Future: Machine Vision in Insurance

Insurers now have access to an unprecedented quantity of image and video data. Many insurers manually review these data sources, and while this approach provides limited insight for specific cases, it stops short of deep analytics, automation, or broader risk evaluation. With the emergence of machine vision technology, there are new ways to process make sense of these vast unstructured image stores.

Analyzing images to determine claims and underwriting risk factors isn’t a new concept for insurers; underwriters have been using sources like Google satellite images for years for this precise purpose. By applying programmatic intelligence to visual imagery, insurers can change the way they think about photographs, satellite images, drone feeds, and other visual sources. Machine vision can help insurers evaluate a broader range of risk, reviewing images for a whole book of business rather one submission at a time. And the real-time nature of machine vision analysis can allow insurers to provide scoring or estimates to underwriters, or even automate decision-making in clear cases.

The technology behind machine vision and AI in general is still in its emergent stages. Insurers are likely to see more tangible results with machine vision platforms built specifically for insurance needs in claims and underwriting. General machine learning platforms may be capable of image- and video-based analysis of risk factors in the not-too-distant future, but purpose-built solutions will likely provide more value with fewer resources and less investment in the short term.

Most purpose-built machine vision use cases are focused on commercial and personal property underwriting and claims due to the proliferation of property imagery. Usage is emerging for auto claims and is mostly exploratory in other lines of business. Prominent vendors covered in Novarica’s report include Cape Analytics, Flyreel, Pointivo, Betterview, Claims Genius, Galaxy.AI, Tractable, and Lapetus.

As with adoption of any emerging technology, insurers should select platforms that align with their own organizational needs and business goals. More information on machine vision use cases in insurance and the market for vendor platforms is available in Novarica’s recent brief, Machine Vision in Insurance: Use Cases and Emerging Providers.

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