Underwriters have access to more data than ever before, but how much of the risk are they actually seeing? A submission might identify two ports and one supplier, while the reality is a far more complex network of exposures. According to Diya Sawhny, Head of Commercial Strategy and Head of Customer Engagement at Moody's Insurance Solutions, connecting these hidden relationships could fundamentally change how insurers assess and price risk.

Joining Matthew Grant on the podcast, Diya explains how knowledge graphs, connected intelligence and AI are making it possible to bring together information that has traditionally been scattered across multiple systems. Drawing on her experience across property, casualty and financial risk analytics, she explores how insurers can move beyond the information provided in a submission to develop a more complete understanding of the businesses and assets they insure.

The discussion examines the practical impact of better risk intelligence, including a case study where combining credit, financial and company data helped a US specialty insurer identify opportunities to improve underwriting profitability. Diya explains how removing the riskiest 10% of a surety portfolio in a historical test resulted in a reduction in loss ratio of more than 50%, demonstrating the potential value of applying established analytical capabilities to new areas of insurance.

They also explore how AI is changing the way insurers access and use information. From automating the preparation of exposure data to making sophisticated analytics available through AI agents, Diya explains why the next generation of insurance technology will depend on more than powerful models. Reliable, governed data and insurance-specific expertise will be essential if these tools are to support decisions that underwriters can trust.

Looking ahead, Diya shares her perspective on how connected intelligence could help insurers identify previously overlooked concentrations of risk, develop new insurance products and make more informed decisions without adding complexity to underwriting workflows.

In this episode you'll learn:

• Why traditional insurance submissions often fail to reveal the full extent of a company's risk exposures
• How knowledge graphs connect corporate ownership, supply chains, property information and physical risk data to give underwriters a more complete picture
• What a US specialty insurer's surety portfolio reveals about the potential impact of predictive risk scoring on underwriting profitability
• How financial and credit risk analytics can be applied to casualty underwriting, including D&O, surety and trade credit
• Why connecting existing datasets can be more valuable than simply collecting more information
• How property characteristics such as roof condition, tree overhang and building occupancy can improve risk assessment and catastrophe modelling
• Why data governance, verification and auditability are critical to the successful adoption of AI in insurance
• How generative AI and model context protocol (MCP) could make risk data and analytics more accessible within existing underwriting workflows
• Where AI agents could reduce manual data preparation and support faster, more informed underwriting decisions
• How partnerships, acquisitions and internal innovation are shaping the next generation of insurance analytics

Diya's podcast recommendations

Diya shares two podcasts she enjoys listening to for perspectives on financial markets, politics and current affairs:

• The Real Eisman Playbook with Steve Eisman: Exploring financial markets, investment trends and economic developments, including the property and casualty insurance sector.
• Left, Right & Center: Bringing together different political perspectives to explore the issues shaping the United States.

Introduction

What happens when you build an MGA from scratch with today’s AI capabilities, rather than trying to layer them onto legacy technology?

In this episode, Robin Merttens speaks with Theo Butt, CEO of Kinetic Insurance Services, Andy Roberts, CTO of Kinetic Insurance Services and Guillaume Bonnissent, CEO of Quotech, about Convex’s new hybrid carrier-MGA platform, the technology being built behind it and what an AI-native underwriting operation could look like in practice.

Theo explains why Convex created Kinetic to extend into lines of business that are complementary to its existing underwriting appetite. The model is designed to give entrepreneurial underwriters some of the advantages of building their own MGA, while providing access to cornerstone capacity, technology, operations, claims and compliance infrastructure. The aim is simple: allow underwriters to spend more time building profitable portfolios and less time building the machinery around them.

Andy takes us inside Kinetic’s technology strategy. Starting with a greenfield environment has given the team an opportunity to design around agentic AI from day one, with the ambition of automating as much non-revenue-generating work as possible. That could mean workflows running from the moment an email arrives through to quote generation, while keeping the underwriter in control of the decisions that require human judgement.

But building an AI-native insurance business is not simply about automation. Andy explains why explainability, auditability and human oversight have to be designed into agentic workflows from the beginning, rather than added later. Every action needs to be traceable, with controls over what agents can do and clear visibility into how and why work has been completed.

Guillaume explores what could come next. As large language models become the interface through which employees access information and complete tasks, he believes underwriters could increasingly start their day in their company’s LLM rather than moving between email, underwriting systems and other applications. The technology to do this already exists, he argues, making adoption, governance and cost control increasingly important challenges.

The conversation also looks at one of the biggest advantages and risks of building on the technological frontier. Kinetic does not have the legacy infrastructure that can make deploying AI at scale difficult, but the pace of change means today’s new technology can quickly become tomorrow’s legacy. Building for flexibility and continuous change may therefore be just as important as choosing the right technology today.

In this episode you’ll learn:

• Why Convex chose a hybrid carrier-MGA model for Kinetic
• How the model aims to give entrepreneurial underwriters more time to focus on underwriting
• Why Convex intends to provide cornerstone capacity to Kinetic’s underwriting cells
• How Kinetic is building agentic AI into its workflows from the outset
• Why explainability, auditability and human oversight are critical when deploying AI in a regulated market
• How automation could change the roles of underwriting and operations teams
• Why flexible technology matters when new underwriting teams need to be launched quickly
• How large language models could change the way underwriters access their systems and information
• Why AI adoption may now be a bigger challenge than technological capability
• How legacy infrastructure can make deploying agentic AI at scale more difficult
• Why the accelerating pace of technological change is forcing insurers to rethink what it means to future-proof their architecture

If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn.

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