Importance of AI for Insurance

Insurance CIO Outlook | Wednesday, June 22, 2022

How can carriers increase AI in the insurance workflow to solve client problems and enhance the overall process?

The ability to analyze numerous data points almost instantaneously creates new and exciting ways for insurers to assess situations and predict patterns that humans could not do independently. (ALM Media archives)

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.

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? The value of AI technology is in automation and 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.

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Early adopters

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

AI in customer service and claims management permits real-time interaction with a chatbot to account for notice of loss, automate damage evaluation, and anticipate patterns in claim volume. According to consulting firm Capgemini, AI can even take over the handler’s administrative functions, freeing up time to investigate, evaluate, and negotiate.

Claims management in auto insurance is also the initial use case of AI applications along the insurance value chain. Major carriers such as State Farm and All state have experimented with deploying AI to track and detect when motorists engage in distracted or unsafe driving. And Progressive employs machine learning in conjunction with data gathered from drivers through its Snapshot mobile app, with the final aim to predict driver patterns and the likelihood of future accidents or reward safe driving.

AI and machine learning can equally be used in digital claims management for Property & Casualty: Think of a camera combined 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

The Important use cases of AI and cognitive learning technology are improving data accuracy and reducing manual errors associated with human input. For example, AI applications can identify insufficient data from application processing, which helps reduce overpricing, automate application processing, and decrease human errors in data entry. It can also create efficiencies by analyzing vast data to identify claims disputes where an attorney would be necessary.

The ability to analyze numerous data points almost instantaneously creates new and exciting ways for insurers to assess situations and predict patterns that humans could not do independently. But this doesn’t intend robots to replace humans anytime soon; preferably, technology like AI and machine learning, if appropriately implemented, can free humans from rote tasks like data entry to focus on the more high-touch and value-added aspects of customer service.

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