With premiums flat and interest rates hovering near historic lows, the way for insures to profitably grow is through attracting new customers. Winning over consumers requires meeting demands for transparent, affordable pricing, and customized products.

How to accomplish this, particularly through digital technology and AI, was a frequent of topic of conversation at this year’s InsurTech Conference. Many attendees said they saw AI as a way to improve customer centricity, because algorithms and machine learning can potentially provide new insights into customer journeys, accelerate underwriting, and add precision to risk management.

The transformative potential of AI, and the ability to orchestrate it with domain talent, is crucial to EXL. The growing interest in AI within the insurance industry was examined as part of a panel discussion with some of the insurance industry’s foremost leaders. This wide-ranging conversation covered three main areas:

  • How insurers can prepare for AI
  • Strategies for leveraging AI
  • Overcoming the challenges of AI

Prepare for AI by preparing your people

For insurers to scale the use of AI in the future, they must start to prepare today. Unfortunately, the human element can get lost during technology implementations. Still, preparing for AI requires that companies focus on their most important resource: people.

No matter how many chatbots, algorithms, or bots insurers use, the industry will always succeed on the strength of its people. Many employees have heard predictions that one day almost everyone will be automated out of a job. More recent analyses show these fears are unfounded. AI won’t replace human workers. It will augment them. While AI can automate many customer interactions, it cannot replace the human touch of a good agent who understands a customer’s financial goals and recommends the right products.

Having clear, frank conversations with employees about what they can expect from AI, and how it can augment but not replace them, will go a long way towards allaying their fears. As AI reduces the rote, rule-based parts of insurance workflows, human skills such as empathy and creativity will increase in value.

Insurers must also reexamine how they recruit new talent. AI initiatives require employees with digital skillsets in high demand. Competition for these employees can be difficult, especially without the “cool factor” of a hip new startup or tech company. At the same time, many of today’s new recruits want meaningful, purpose-driven work. Insurers can adapt their approach to recruitment by playing up the role that technology plays in the workplace, and how the insurance industry helps people solve their financial challenges.

Leveraging AI takes having the right data, not just a lot of data

Everyone knows that AI requires data. Previously, companies thought that if algorithms could analyze huge amounts of data then all data was valuable, in and of itself. Instead, having the right kinds of data is more important. For instance, training a machine learning algorithm to proactively reach out to customers to decrease risky behaviors takes knowing what data correlates with these behaviors.

Insurers should also look for new sources of data. Much of the information companies have on their customers is traditionally volunteered by the customers themselves, such as the kind of car they drive, health conditions, and where they live. This information is enough to determine risk levels for traditional insurance products, but that’s not enough anymore.

Today, customers want insurance products that are customized not only to them, but where they are at that point in time. There’s an opportunity to move from providing car insurance that lasts for six months to car insurance that’s effective during a six-minute Uber ride, or from homeowners’ insurance that only accounts for the age of the house to a policy that uses smart devices to set a variable monthly premium based on how well the home in maintained. Doing this requires a combination of existing customer data with external data sources.

Walking the fine line of privacy and other AI challenges

At the same time, data is a double-edged sword. Collect the right kinds at the right amounts, and insurers can provide unparalleled customer experience. Collect data in a way that consumers feel violates their privacy, and the consequences could include significant regulatory or reputational blowback.

There’s also the potential for data to be intentionally misused. Conscious and unconscious biases can creep into AI algorithms, negatively impacting customers and company brands.

Combatting both of these challenges takes a culture where people want to do the right thing for both the customer and the company. This sounds simple, but companies can miss the nuances of data privacy and usage when they fail to view issues from the customer lens. This is why many insurers are beginning to create AI governance and ethics boards. These bodies can bring fresh perspectives and oversight to AI programs, while helping to combat data and bias challenges that may otherwise go overlooked.

It all comes back to the customer

Preparing for, leveraging, and solving the challenges of AI all requires an unwavering commitment to the customer. By emphasizing to employees how AI solves consumer issues and augments their own roles, and by understanding the right data to leverage to create better customer experiences while not overstepping data privacy boundaries, insurers can position themselves for AI success.

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