MLC expert, AI and the claims handling “birdie”

MLC expert, AI and the claims handling “birdie” | Insurance Business Australia

Claims

MLC expert, AI and the claims handling “birdie”

ANZIIF’s webinar examines proactive claims handling

“When you roll that up into some form of supporting algorithm, or co-pilot, the way I look at it is you’ve got this little birdie on your shoulder,” said Andrew Beevors (pictured above).

Beevors is chief claims officer with MLC Life Insurance. He was speaking during a recent ANZIIF [Australian and New Zealand Institute of Insurance and Finance] webinar hosted by EXL, the global analytics firm. The webinar looked at how insurers can proactively use artificial intelligence (AI), including Generative AI, in their claims process.

According to the World Economic Forum (WEF), Generative AI refers to types of algorithms that can use deep learning to generate new outputs from data “they have trained on.” It was this technology, deployed during a claims process, that Beevors compared to the claim handler having a co-pilot or bird on their shoulder.

“The first thing is what I refer to as the absolute basics: we have to build trust with our customers,” he said. Beevors said the speed of the insurers’ response and how quickly claims are paid are essential for trust building.

However, he suggested the industry is perhaps too obsessed with the customer’s experience and should focus more attention on its claim handlers.

“We really need to look at the claim handler from the view of their experience as well,” said Beevors. “Because when you create a really good experience for claim handlers, you can enable a great customer experience.”

He defined the claim handler’s daily role as decision making about where and on what they spend their limited time.

“We’re looking to provide support for our claim handlers to be better able to make decisions about which customers and what activities they actually do,” said Beevors.

Personalising the claim process

Once the claims handler has delivered on the “basics”, things arguably start to get more complex.

“That next level of support is really engaging the customer at a human level and this is where the personalization comes into play,” he said. “We’re looking to deliver the right support on the right claim at the right time.”

Beevors said these general rules of engagement apply across the insurance industry and “all elements of the insurance journey.”

“I’m obviously working in life at the moment and that’s bodily injury – but I’ve worked in motor, I’ve worked in home,” he said. “You really need to understand [personalisation] because some people are more focused on getting their car repaired than their own human body.”

Beevors said how a customer experiences this claims journey “is probably just as important as the outcome.”

“So that’s what I refer to as the human experience,” he said. “It’s generally pre-emptive, it’s empathetic and it’s enabling our claims people to support the right intervention and support for that individual customer at the [right] time.”

Augmented intelligence

AI’s role in driving this process, he said, is akin to “augmented intelligence.”

“It’s augmenting our claims handlers,” said Beevors. “So when we’re building these tools, it’s really important that we build them with the employee in mind as well [as the customer] because, fundamentally, if they are enabled and freed up to have great conversations with customers, that’s what the customer actually remembers.”

He said AI is a co-pilot “that supports the claim handler to deliver the best possible customer experience that your organization can define.”

Beevors said every firm will have points of difference about what’s important in customer experience. He said MLC is using AI as an assistant in the claims process. This form of co-pilot can act as a prompt for the claims handler, performing “some of those simple touch points” and taking care of “regulatory hygiene.”

“If the customers are happy, they’re not complaining and you keep regulators off your back,” he said.

Beevors included being paid on time and updated regularly as critical customer touch points.

Dynamic triage model

“Then we get into what’s referred to as the support phase, how we really bring support to customers along that journey,” he said, “the term I refer to is a dynamic triage model, or a dynamic risk assessment model.”

Beevors said this involves the claim handler deciding what information they need to make a decision on a claim.

“With that in mind, these dynamic triage models continually harvest signals from all the information that a claim handler would normally consume to make these decisions,” he said. “They harvest information from the structured data out of their core management system, from file notes, from listening to phone calls, but also from reading documents, reports.”

Beevors said AI technology can codify this harvest of information and emphasize “certain elements.”

In that way, he said, it acts as a “little birdie,” or co-pilot that can figuratively tap the claim handler on the shoulder when a claim’s status changes or when he or she may have missed a critical piece of information. The claim handler, he said, could then take a “deep dive” to investigate.

Without AI, he suggested, a claim handler could still reach the same decisions, but it might take weeks longer.

“If you’ve got that little copilot on your shoulder, they can prompt you a lot earlier and you can intervene a lot earlier,” said Beevors.

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