Insurance has become extraordinarily good at finding the few. The next competitive advantage may come from clearing the many.
In this episode of Scouting for Growth, I sit down with Manjit Rana, Executive Vice President for Insurance across the UK, EMEA and APAC at Clearspeed, to explore a deceptively simple question: can insurers establish trust at the same speed at which they are automating decisions?
More than 90 out of every 100 customers coming to an insurer are genuine. Yet much of the industry's fraud infrastructure has historically been designed around identifying the minority that may represent risk. Manjit's argument is not that those systems were wrong. They were built for a different information environment. Today, AI is accelerating both the automation of insurance decisions and the ability to manufacture convincing evidence, making the trust problem more urgent.
We explore the emerging concept of a Trust Intelligence Layer: a continuous, voice-derived, regulatory-ready risk signal designed to help genuine interactions move forward quickly while directing scarce human judgement toward the cases requiring closer attention. Manjit's airport metal detector analogy captures the distinction well. The signal does not make the final decision. It identifies where something unexpected deserves a closer look, allowing everyone else to keep moving.
The implications extend well beyond claims. Historical data and behavioural profiles can indicate risk, but they cannot necessarily reveal what an individual knows about an application, renewal or claim happening right now. As insurers automate more of those interactions, the challenge is to preserve trust without simply adding more friction, more profiling or more manual intervention.
Most importantly, the conversation moves from technology to operating model. If you could clear more genuine customers, identify more risk and make decisions faster, what would that change about your people, processes, workflows and economics? Manjit argues that this is where leaders should start. Because the question is no longer whether insurance will automate. It is whether insurers can tell which of those automated decisions deserve their trust.
KEY TAKEAWAYS
I want to challenge the way we have traditionally framed fraud and trust in insurance. For decades, we have become increasingly sophisticated at identifying the interactions that may represent risk. But if the overwhelming majority of customers are genuine, there is another side to the equation: how quickly can we establish sufficient trust in those customers to let them move forward? The distinction matters because every genuine interaction held up by unnecessary scrutiny creates friction, cost and a poorer customer experience. The opportunity is not simply to find more needles. It is to become much better at clearing the hay.
This is why we explore the Trust Intelligence Layer. The principle is not to replace existing fraud systems or human judgement with another automated decision-maker. It is to introduce an additional signal that helps determine where human attention may be valuable. That also exposes the limitations of relying exclusively on historical data and profiles. Data can tell us a great deal about patterns and previous behaviour, but it cannot necessarily tell us what an individual knows about the interaction taking place right now. For me, that is also where fairness and customer experience begin to converge: the closer we can get to assessing the interaction itself, the less we need to rely on broad proxies for suspicion.
The AI implications make this urgent. As underwriting, claims and customer journeys become faster and increasingly automated, trust cannot remain a slow layer sitting outside the process. It needs to operate at the same speed. But leaders should not begin by asking whether a particular technology works. They should first ask what happens to the business if it does. If more genuine customers can be cleared automatically and more high-risk interactions identified, do your people, workflows and governance know what to do next? The practical starting point is deliberately focused: one workflow, one clean baseline and 90 days. The advantage will not come from adding another score. It will come from redesigning the operating model around better information.
BEST MOMENTS
“More than 90 out of every 100 customers who come to an insurer are genuine.” – Sabine VanderLinden [03:42]
“We really need to look at a solution stack that looks at how do we clear the genuine people.” – Manjit Rana [07:16]
“The machine's job is just to go, there is something there that we're not expecting to see there, and then the human is making the decision.” – Manjit Rana [14:52]
“It's about the source. It's about the individual, not the evidence.” – Manjit Rana [24:18]
“It shouldn't be the trust intelligence layer that's making the decision. It's just another signal.” – Manjit Rana [39:53]
“The choice in front of every claims and underwriting leader listening is not whether to automate. That has been decided already. It is whether you can tell which of those automated decisions deserve your trust.” – Sabine VanderLinden [01:03:24]
ABOUT THE GUEST
Manjit Rana is Executive Vice President for Insurance across the UK, EMEA and APAC at Clearspeed. With three decades of experience in insurance, his career spans entrepreneurship, corporate innovation and consulting. He founded insurance innovation consultancy Ingenin, helped establish the AXA UK Innovation Lab, and previously led corporate innovation alongside Sabine at Startupbootcamp InsurTech. In 2026, he was named Insurance Personality of the Year at the British Insurance Awards.
At Clearspeed, Manjit focuses on taking voice-based risk assessment into production within major insurance organisations and exploring how an additional risk signal can help insurers establish trust throughout the policyholder journey. His work spans claims, underwriting, application and renewal, with an increasing focus on what trust means as AI and automated decision-making reshape insurance operations.
Download the report Moving at the Speed of Trust on the Clearspeed website.
ABOUT THE HOST
Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.
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