Reinsurers are turning to artificial intelligence to strip out operational inefficiencies as softening market conditions make cost control a priority, according to Mea Platform chief executive Martin Henley. Highly skilled underwriters and operations staff remain bogged down in administrative work despite years of investment in sophisticated modelling tools.
Speaking at the Rendez-Vous de Septembre conference in Monte Carlo, Henley said the sector had already adopted advanced technology for catastrophe modelling, analytics and pricing. "Where it feels to me [that reinsurers are] missing a trick is around the workflow, which creates a huge operational drag around everything that happens," he said.
"The slips, the submissions, the bordereaux, all the unstructured data coming in. And then you've got expensive underwriting talent, or even highly skilled operations talent, just doing admin all day and moving things around."
Henley said the issue was not a lack of ambition. Reinsurers had often tried to force generic technology into complex insurance workflows rather than building for the specific demands of specialty risk. The quality of data feeding into those processes compounds the problem. "If you don't get it right up front, everything downstream from there, the quality of the data gets worse," he said.
Where AI fits in the reinsurance workflow
Appetite for AI for Insurance is growing fastest around risk ingestion, data extraction, triage and repetitive middle and back office tasks. These are areas where accuracy and speed matter but the technology is not being asked to make judgment calls.
Reinsurers remain cautious about letting AI near underwriting decisions. "Where we're seeing that people are still less comfortable, which is very understandable, is where you're letting the AI near decision-making, so near the actual underwriting," Henley said.
The conversation has shifted sharply. "If I think about the conversations we're having now compared to even nine months ago, it's a very, very different conversation," he said. "It's not evangelising 'this technology could help you'. It's 'we know it can help us. How do we actually make it work in our environment?'"
Specialty risk demands insurance-specific tools
Generic AI struggles with complex specialty risks where explainability, auditability and accuracy are non-negotiable. Henley argued that insurance-specific technology is becoming essential, not optional, particularly as the market cycle turns.
"As [the] market is softening, this efficiency point around understanding your data in lots of detail stops really being an optional thing," he said. He also pushed back on the idea that AI adoption is primarily a headcount reduction play. "Our view is this is an opportunity to get those expert people out of doing admin and help them to drive your business."
Why this matters for operations
For operations teams in reinsurance, the signal is clear: the technology conversation has moved from "if" to "how." The bottleneck is no longer awareness but implementation. AI for Operations is being targeted at the unstructured data workflows that consume the most manual effort - submissions, bordereaux, and triage - not at replacing decision-makers. The firms that reduce operational drag now will have a structural cost advantage as the market softens. The question for operations leaders is whether their current tools were built for insurance workflows or retrofitted from generic systems that were never designed for the complexity of specialty risk.
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