AI could change the economics of re/insurance growth: mea CEO

While the whole market is debating rate, there is an opportunity right now to reshape the operating model, take out cost and cycle time, and create capacity for growth, says mea CEO Martin Henley.

Key points:
AI can create underwriting capacity
Early adopters start to pull ahead
Performance is the test for pilots

As re/insurance pricing comes under pressure, AI’s biggest opportunity may be in the economics of growth.

Martin Henley, chief executive of mea Platform, has an obvious stake in that argument. His AI-native insurance technology business, founded in 2021, automates re/insurance workflows for carriers, brokers and MGAs. Initially bootstrapped, mea secured a $50 million minority growth investment from SEP in February and is scaling across the US, London, Bermuda and Europe, as well as moving into Asia.

“In my view, growth in this market is really going to come from the operating model, not so much the rate,” Henley told Monte Carlo Today.

For all the attention AI is getting at board level, however, the industry remains a long way from that becoming the norm. Henley sees much of the current activity concentrated on experimentation and back-office efficiency, with only a “pretty small group” running AI in production at scale. Some have been doing so in parts of their operations for two years or more.

“We literally have clients saying to us, ‘I’m going to win this market and this product line because of this tech,’” he said.

After 30 years in insurance, Henley finds that striking. “It is unusual to hear underwriting teams making statements like that.”

He expects the gap between those already operating the technology at scale and slower adopters to become increasingly visible.

Changing the economics

The more consequential question for Henley is what insurers choose to do with the productivity AI creates. 

“Are you using it as a way to reduce headcount and make those sorts of public commitments, or are you using it as a way to get your expert people off more administrative tasks and onto what you’re really about, which is understanding risk?” he said.

“If you get those things right and the cost of processing a risk comes down sharply, then the economics of your whole book start to change.”

“The overall prize here is capacity. You’re able to handle more new business and more submissions coming in. You’re able to quote faster and convert better.”

Underwriters stay in the decision. Henley sees AI taking on repeatable work while specialists spend more time on pricing, risk selection and relationships.

Some clients are using that capacity to expand appetite or enter lines they did not previously write. He said: “If you get those things right and the cost of processing a risk comes down sharply, then the economics of your whole book start to change.”

From pilots to performance

Henley is sceptical of judging AI programmes by the number of pilots or proofs of concept underway. The tougher test is whether the technology is running on live business and producing measurable underwriting or financial results.

“If you get it right, AI is able to put better risk information in front of underwriters – or brokers – faster, at the point of decision,” he said.

Cleaner, more complete information should ultimately show up in the measures insurers already use to judge performance.

“How has this changed our hit ratio? How has this impacted our loss ratio? How has this impacted our combined ratio?” Henley asked. 

His enthusiasm for AI comes with a sharp critique of what insurance technology has delivered before it. Much of the transformation Henley has witnessed during his career amounted to digitising existing processes without fundamentally changing the work or workflow. Core-system replacements, he noted, have sometimes consumed tens or hundreds of millions over several years for “questionable benefit”.

“It’s quite easy to run a proof of concept and also quite easy to run a pilot,” he said. “Turning that into something real, something we are now relying on for the business – whatever the AI is doing – is quite tough.”

That distinction becomes more important as market conditions soften. Rate may be outside an individual company’s control; the efficiency of its operating model is not.

“The soft markets will reward operators,” Henley said. “While the whole market is debating rate, there’s an opportunity right now to take real cost and cycle time out of your operating model.”

On that basis, the AI race goes to the companies that turn the technology into measurable performance.

“The opportunity to move from pilots to production is now,” he concluded.

Martin Henley is the chief executive officer of mea Platform. He can be contacted at: martin@meaplatform.com

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