The ability to analyze countless data points almost instantaneously creates new and exciting ways for insurers to assess situations and predict patterns that humans could not do on their own. (ALM Media archives) The ability to analyze countless datapoints almost instantaneously creates new and exciting ways forinsurers to assess situations and predict patterns that humanscould not do on their own. (ALM Media archives)

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Artificial intelligence, commonly known as AI,has been perhaps the most "buzzed about" technology over the lastyear or two.  With stunning applications ranging fromalways-on virtual assistants, to self-driving cars, androbo-advisors that manage entire investment portfolios, the futureof an AI-powered world is no longer just science fiction. It's areality that's making its presence felt across industries.

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Like other emerging technologies, AI is expected to have atransformative effect on the insurance industry, and incredibleamounts of funding are already pouring in. Worldwide spending oncognitive and AI systems is expected to triple over the next three years, withtotal spending predicted to reach $77.6 billion in 2022.

Pressing questions

Amid the discussion about and funding for AI applications in the insurance industry,carriers, brokers, program administrators, MGAs and MGUs now needto answer the question: What exactly can AI do forinsurance?  It's clear that the value of AI technology isin automation and in uncovering insights only accessible by usingadvanced computing power to process massive amounts of data.

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But, to effectively implement AI — and to get the maximum value out of the technology —insurers need to figure out where it fits into the digitalinsurance continuum. The question we must ask (and answer) is: Howdo we use AI in the insurance workflow to enhance the overallprocess? And what challenges is AI helping us solve from a clientstandpoint?

Early adopters

In order to better understand what AI means for insurance, let'stake a look at how AI can be applied — or is already being applied— to key areas along the digital delivery process, from consumerengagement through underwriting, purchase and policymanagement.

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AI in customer service and claims management enables real-timeinteraction with a chatbot to report a notice of loss, automatedamage evaluation, and anticipate patterns in claim volume.According to consulting firm Capgemini, AI can even be used totake over the handler's administrativefunctions, thus freeing up time to concentrate oninvestigating, evaluating and negotiating.

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Claims management in auto insurance is one of the early usecases of AI application along the insurance value chain. Majorcarriers such as State Farm and Allstate have experimented with deploying AI totrack and detect when motorists are engaging in distracted orunsafe driving. And Progressiveutilizes machine learning in conjunction with data collected fromdrivers through its Snapshot mobile app, with the ultimate goal ofusing that data to predict driver patterns and the likelihood offuture accidents, or for rewarding safe driving.

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AI and machine learning can similarly be used in digital claims management for Property &Casualty: Think of a camera combining with machine learning toextract property data using aerial imagery. Anotherexample is tech startup CapeAnalytics which uses machine learning and geospatialimagery to automatically pull out data points — like buildinggeometry and roof condition information — that insurers can thenuse to evaluate risk.

Reducing human error

One of the most practical use cases of AI and cognitive learning technology is inimproving data accuracy and reducing manual errors associated withhuman input. AI applications can be used in identifying bad datafrom application processing, which in turn helps reduceoverpricing, automate application processing, and reduce humanerrors in data entry. It can also create efficiencies by analyzinglarge quantities of data to do things like identify claims disputeswhere an attorney would be necessary.

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The ability to analyze countless data points almostinstantaneously creates new and exciting ways for insurers toassess situations and predict patterns that humans could not do ontheir own. But this doesn't mean robots will be replacing humansanytime soon; ideally technology like AI and machine learning,if implemented properly, can free up humans from rote tasks likedata entry to focus on the more high-touch and value-added aspectsof customer service.

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As AI becomes more embedded in insurance processes, how could itfurther change the industry? McKinsey predicts this example from afuture of endlessly integrated devices: A personal assistant mapsout a potential route for a driver and shares it with his mobilityinsurer, which then responds with an alternate route that has alower likelihood of accidents and auto damage as well as thecalculated adjustment to his monthly premium.

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While it may sound far-fetched now, insurers must be prepared torespond to the changing business and technology landscape. But inorder to do so, there needs to be a plan. AI in and of itselfcannot accomplish anything, nor is it a magic panacea that cansolve all our problems. But when integrated thoughtfully into thedigital insurance continuum it can drive efficiencies, result incost savings and drastically improve customer service.

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Peter Gaillard ([email protected]) is executive vicepresident of InsurIQ, a firm focused on the digital transformationof the insurance segment for consumers, agents andcarriers.

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These opinions are the author's own.

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See also: Top insurance technology issues nagging at industryleaders

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