What AI actually means for insurance

Artificial intelligence, commonly known as AI, has been perhaps the most “buzzed about” technology over the last year or two.  With stunning applications ranging from always-on virtual assistants, to self-driving cars, and robo-advisors that manage entire investment portfolios, the future of an AI-powered world is no longer just science fiction. It’s a reality that’s making its presence felt across industries.

Like other emerging technologies, AI is expected to have a transformative effect on the insurance industry, and incredible amounts of funding are already pouring in. Worldwide spending on cognitive and AI systems is expected to triple over the next three years, with total 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 need to answer the question: What exactly can AI do for insurance?  It’s clear that the value of AI technology is in automation and in uncovering insights only accessible by using advanced computing power to process massive amounts of data.

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 digital insurance continuum. The question we must ask (and answer) is: How do we use AI in the insurance workflow to enhance the overall process? And what challenges is AI helping us solve from a client standpoint?

Early adopters

In order to better understand what AI means for insurance, let’s take a look at how AI can be applied — or is already being applied — to key areas along the digital delivery process, from consumer engagement through underwriting, purchase and policy management.

AI in customer service and claims management enables real-time interaction with a chatbot to report a notice of loss, automate damage evaluation, and anticipate patterns in claim volume. According to consulting firm Capgemini, AI can even be used to take over the handler’s administrative functions, thus freeing up time to concentrate on investigating, evaluating and negotiating.

Claims management in auto insurance is one of the early use cases of AI application along the insurance value chain. Major carriers such as State Farm and Allstate have experimented with deploying AI to track and detect when motorists are engaging in distracted or unsafe driving. And Progressive utilizes machine learning in conjunction with data collected from drivers through its Snapshot mobile app, with the ultimate goal of using that data to predict driver patterns and the likelihood of future accidents, or for rewarding safe driving.

AI and machine learning can similarly be used in digital claims management for Property & Casualty: Think of a camera combining with machine learning to extract property data using aerial imagery. Another example is tech startup Cape Analytics which uses machine learning and geospatial imagery to automatically pull out data points — like building geometry and roof condition information — that insurers can then use to evaluate risk.

Reducing human error

One of the most practical use cases of AI and cognitive learning technology is in improving data accuracy and reducing manual errors associated with human input. AI applications can be used in identifying bad data from application processing, which in turn helps reduce overpricing, automate application processing, and reduce human errors in data entry. It can also create efficiencies by analyzing large quantities of data to do things like identify claims disputes where an attorney would be necessary.

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. But this doesn’t mean robots will be replacing humans anytime soon; ideally technology like AI and machine learning, if implemented properly, can free up humans from rote tasks like data entry to focus on the more high-touch and value-added aspects of customer service.

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

While it may sound far-fetched now, insurers must be prepared to respond to the changing business and technology landscape. But in order to do so, there needs to be a plan. AI in and of itself cannot accomplish anything, nor is it a magic panacea that can solve all our problems. But when integrated thoughtfully into the digital insurance continuum it can drive efficiencies, result in cost savings and drastically improve customer service.

Peter Gaillard (Peter.gaillard@insuriq.com) is executive vice president of InsurIQ, a firm focused on the digital transformation of the insurance segment for consumers, agents and carriers.

These opinions are the author’s own.

See also: Top insurance technology issues nagging at industry leaders