One of my first significant impressions of computer vision camefrom The Terminator when the robot was able to see andidentify objects as they moved. That computer vision of the futurehas started to emerge, and as computers start to see things, thereare far reaching implications for the property andcasualty insurance industry.

How computers see

Like humans, a computer's vision implies two distinct things:identification and categorization. Humans do this unconsciously aswe view the world around us; however, computers perform each taskseparately and quite 'consciously.' A simple example is howFacebook recognizes faces and categorizes them as your friends. Amore advanced example is where a computer recognizes objects withinrunning movies (or live video feeds) and categorizes them as "safe"or "dangerous." I recently saw computer vision used to recognizepeople and objects in near real-time within a video, includingpeople running and a bag lying on the ground. Fascinatingtechnology.

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In insurance, there are virtually countless use cases wherehumans need to recognize and categorize object, e.g., "FordFocus — total loss." In that example, the object is bothrecognized "Ford Focus" and categorized "total loss." This kind ofcomputer vision is already is starting to take hold and has theability to dramatically speed claims and other insuranceprocessing.

I mentioned that sedentary bag in the video example becausecomputer vision is being used to look through closed-circuit television to spot suspiciousobjects. While the video feeds are not yet universally sharp enoughto see "everything," this is quickly changing and the securityimplications are pretty amazing.

Hyperbole aside, humans still have a big edge on how quickly weread and recognize pictures (movies, or moments in our lives, areseen as a massive number of individual pictures) compared tocomputers, but not for much longer.

Computers are catching up.

Humans also have a big edge because there are virtuallycountless exceptions to a single object type because of the nuancesthat exist in objects. Taking the totaled Ford Focus exampleagain — maybe it is not really a complete loss because theimage was somehow skewed. In a non-total loss example, there may beframe damage unseen in the picture, making it a total loss. Pointbeing that humans are still required.

Computer vision is doing even more than helping identify andadjust claims; collision avoidance is helping prevent claims fromever happening. (Photo: iStock)

Insurance use cases make computer visiontangible

In the world of claim inspections, computer vision has thepotential to significantly speed up the process, reduce errors, andlower fraud. The "identification" aspect is the object type(vehicle, house, etc.) like the Ford Focus example. Like allcomputer vision use cases, the real issue is getting enough samplesso the machine can accomplish its identification task. Untilrecently, the number of permutations and combinations was simplyimpossible for computers to process, which is exactly what ischanging now to make computer-assisted inspections possible.

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As classification gets more sophisticated, insurance agents canuse computer vision to adjust some claims. A good example is insituations where relatively simple geometry is used to estimate a loss. Consider damage tomanufactured homes. There are relatively few types of buildingmaterials, and many manufactured homes have straightforwardgeometry. Now that computers can interpret distance (see the TapeMeasure app on the iPhone X), a computer can identify not only theobject but how big it is, and then how much it may cost toreplace.

Taking both inspection and adjusting to the next level, some insurance companies are using drones tonot only perform identification and classification tasks, but alsoprovided the added value of reducing the risk of harm to adjusters.For example, instead of climbing a ladder to inspect roof damage, adrone can just fly there. Companies also are using drones to obtainhigh level views of catastrophes. Doing so not only provides roughorder-of-magnitude loss views, but also keeps adjusters out ofharm's way.

Computer vision is doing even more than helping identify andadjust claims; collision avoidance is helping prevent claims fromever happening. Collision avoidance requires extremely fastidentification speeds, and have some relatively simple applications(e.g., drones avoiding objects) along with complex applications(e.g., autonomous vehicles and proximity-based collisionavoidance). It is also not outside the realm of comprehension toimagine alarms that can help employees avoid workplace accidents.Collision avoidance is risk management taken to an entirely newlevel of sophistication.

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Making computer vision accessible

Unlike some big ideas of the past, it seems that computer visionis happening openly, through application programming interfaces(APIs) that anyone can use. This is very good news for theinsurance innovators of the world, since they can focus on the useof the technology versus the technology itself. There are a numberof vendors supplying computer vision APIs, which is a very goodthing for innovation, and likely why the technology is progressingso rapidly.

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What we can expect in the near future

Computer vision has come a long way, there is still much furtherto go. As the speed of identification and categorization increases,so does the applicability for insurance. It is not beyondcomprehension to have computers rapidly creating estimates forentire property damage claims, across a wide range of objectsincluding vehicles, buildings, and interior objects. Whenhandwriting recognition progresses far enough, the next step issignificantly improved transcription services as the work movestowards validation and evaluation and away from transcriptionitself.

Regardless of what comes to pass, one truth still remains:People plus computers are still more powerful than computers alone.The range of reality far exceeds the computational power ofcomputers. This means that people should focus energy on learninghow to interact and help computers maximize our collectivevalue.

Frank Neugebauer has dual roles at Genpactas the Digital CTO for Insurance and the Digital Consulting leadfor the Americas. He has over 19 years of property and casualtyinsurance experience in standard lines, excess and surplus, andreinsurance.

The opinions expressed here are the writer's own.

See also:

7 ways auto technology is impacting insurancecoverage

Using smart technology to combat insurancefraud