I’m thrilled to sit down with Simon Glairy, a leading expert in insurance technology and risk management, whose deep knowledge of AI-driven solutions is transforming the underwriting landscape. With years of experience in Insurtech, Simon has been at the forefront of integrating artificial intelligence into insurance processes to enhance efficiency and accuracy. In this interview, we dive into the innovative world of AI tools designed for underwriting, exploring how they tackle longstanding challenges like manual research bottlenecks, accelerate quoting times, and improve risk assessment. We also discuss the impact on underwriters’ daily workflows, the importance of consistent documentation, and the future potential of AI in this space. Let’s get started.
How did the idea of using AI to automate research tasks in insurance underwriting come about, and what specific pain points were you aiming to address?
The inspiration came from observing how much time and effort underwriters were spending on manual research. Underwriting is a complex process, and a significant bottleneck was the sheer volume of data that needed to be gathered and analyzed for each submission. We saw that underwriters were often stuck sifting through web sources and third-party data, which could take hours or even days. The goal was to eliminate this tedious work, allowing them to focus on strategic decision-making rather than data collection. We wanted to address inefficiencies, reduce human error, and ultimately create a more streamlined process.
Can you walk us through how an AI tool like this gathers and processes risk intelligence for underwriters?
Absolutely. The AI system is designed to pull information from a wide array of sources, including public web data and third-party databases, to build a comprehensive risk profile. It uses advanced algorithms to synthesize this data, identifying patterns and insights that might not be immediately obvious to a human researcher. The tool then prioritizes the most relevant information based on the specific context of the submission, presenting it in a way that’s actionable for underwriters. It’s like having a highly efficient research assistant that never sleeps and always knows where to look.
What does it mean for insurers to be able to answer more questions during the underwriting process, and how does that change their approach to risk?
Being able to answer more questions—often up to 30% more—means underwriters can dive deeper into the nuances of each case. They’re not just skimming the surface; they can explore additional layers of risk factors that might have been overlooked due to time constraints. This leads to a much clearer picture of the potential risks involved, which in turn allows for more accurate pricing and better-informed decisions. It’s about moving from a reactive to a proactive stance, where underwriters can anticipate issues before they become problems.
How does automating research tasks translate into cutting down quoting times so dramatically, sometimes from days to hours?
The key is in removing the manual legwork from the equation. Traditionally, underwriters had to spend days gathering data, cross-referencing sources, and compiling reports before they could even begin to craft a quote. By automating these initial steps, the AI tool delivers the necessary information in a fraction of the time. This speed allows insurers to respond to clients faster, often getting ahead of competitors who are still bogged down by slower processes. It’s a game-changer in terms of market responsiveness.
With underwriters saving significant time per submission, how do you see this reshaping their day-to-day responsibilities?
Saving hours per submission frees up underwriters to focus on what they do best: exercising judgment and building relationships. Instead of being buried in research, they can spend more time analyzing complex risks, collaborating with brokers, and optimizing their portfolios. This shift not only improves job satisfaction—since they’re doing more meaningful work—but also strengthens client relationships and drives better business outcomes. It’s about elevating the role of the underwriter from data gatherer to strategic partner.
Why is creating consistent audit documentation such a critical benefit for insurance teams using AI tools?
Consistency in documentation is vital for a couple of reasons. First, it reduces variability in decision-making across teams. When everyone is working from the same standardized data and research process, you minimize discrepancies that can arise from different research methods. Second, it’s a huge boon for regulatory compliance. Having a clear, traceable record of how decisions were made and what data was used makes audits smoother and ensures accountability. It’s a safeguard that protects both the insurer and the client.
How have you ensured that AI tools integrate seamlessly into the existing workflows of underwriting teams?
Integration has been a top priority from the start. We’ve worked closely with insurers to understand their current systems and processes, designing the AI tool to complement rather than disrupt them. It’s built to plug into existing platforms, so there’s no need for a complete overhaul. Early feedback from users has been encouraging—they’ve noted that the tool feels like a natural extension of their workflow, providing insights without forcing them to change how they operate. Of course, there were challenges, like ensuring compatibility with diverse systems, but we’ve tackled those through customization and ongoing support.
Looking ahead, what is your forecast for the role of AI in insurance underwriting over the next decade?
I believe we’re just scratching the surface of what AI can do in underwriting. Over the next decade, I expect AI to become an integral part of every stage of the process, from initial risk assessment to final policy issuance. We’ll see even smarter systems that not only automate research but also predict trends, personalize risk profiles, and offer real-time decision support. The focus will shift toward creating AI agents that can handle increasingly complex tasks with minimal human intervention, ultimately making underwriting faster, more accurate, and more strategic. It’s an exciting time to be in this field, and I think the potential for innovation is limitless.
