Regulators were not moving fast enough to keep pace with what was possible in insurance technology, said Rajiv Matta (pictured), chief innovation officer, MGA programs at MSI. Matta pointed to the gap between hyper-personalized risk pricing and the decades-old processes that still governed approvals.
“We have the data, we have the methodology to be able to do that,” he said of tailoring pricing to individual behaviors and lifestyle choices. Even small changes to a customer’s profile – buying a turkey fryer, building a backyard pool – could alter their risk. But these changes could not be reflected in real time because regulators were “still stuck in these rate cycles where approvals are needed to get new rate increases,” he said. Some regulators had only recently approved predictive models, leaving a long way to go before real-time repricing could be acceptable.
Matta said controlled testing environments had made it easier to launch new programs, products, and technology. As one of the largest MGAs in the US, his organization saw the momentum firsthand. “All indications are that there’s an acceleration as far as new program development and new product development goes, and new innovation coming online,” he said.
The sandbox model, he added, gave executives more certainty on ROI before committing to scale. “Testing in a sandbox environment gives you some certainty around the ROI… that’s definitely lowering the barrier for us to do more and the industry as a whole to do more.”
The leap from pilot to full deployment was far from guaranteed. Matta identified “solution hype” as a systemic problem – where the sector tried to import ideas from other industries without proving they solved an insurance-specific problem.
“Blockchain… has it been able to solve the problems that we as the insurance industry face? It hasn’t really helped with mispricing of risk, catastrophe related risk, [or] litigation,” he said. “If we are doing any kind of testing, any kind of sandboxing, make sure that it’s solving a real problem… versus trying to adapt something that might have worked somewhere else.”
Lowering the barrier to entry was also encouraging legacy carriers to experiment more. Matta said MGAs and insurtechs had been at the forefront for years, but now established carriers were “investing more in innovation” instead of just optimizing existing portfolios.
He credited both sandbox testing and AI as accelerators, with AI enabling “a lot of different experiments and a lot of different ways people are attacking problems.”
Yet the sector’s push toward more dynamic, individualized pricing would keep running into resistance unless regulators adapted. Matta compared the situation to Delta’s reported use of AI for dynamic airline seat pricing – controversial in a far less regulated market. “Everybody’s risk is very unique, so there’s been some resistance just to that as well,” he said, calling for greater collaboration with regulators to prevent innovation from stalling at the approval stage.
For Matta, the path forward was clear: scale innovation with proof, resist chasing technology that solved the wrong problems, and build regulator relationships early so the industry’s most powerful tools could reach the market.
