InsTech - insurance & innovation

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. 

AI experimentation is everywhere in insurance. But how much of it is actually making it into production and delivering measurable value?  In this episode, Robin Merttens speaks with John Hele, Co-Founder and Executive Chairman, and Greg Framke, CEO and Co-Founder of Portage AI, about why they believe the biggest opportunity for AI lies not in automating the easy work, but in tackling some of insurance’s most complicated processing and data challenges.  John and Greg bring experience from both sides of large-scale technology transformation. Before founding Portage AI, they worked together at Resolution Life on a major technology programme to establish independent operations following acquisitions in Australia, New Zealand and the US. The transformation involved around $400 million of investment and was designed to deliver annual savings of $150 million. That experience, alongside their work applying AI in production, helped shape their view of where the technology can create meaningful value today.  Greg explains why Portage is deliberately targeting the difficult “last 20%” of processing: work that has historically been too complicated, expensive or technically challenging to automate. Rather than starting with the technology, their approach is to identify a specific problem, train models on a client’s own data and demonstrate value through a production-ready solution before asking the client to commit.  The conversation explores one of the insurance industry’s longest-running challenges: turning fragmented, inconsistent data into something genuinely useful. In specialty insurance and reinsurance, information can arrive from brokers, clients and other parties in different formats and using different terminology. John explains how AI can help establish a common ontology across that information, potentially overcoming data cleansing and standardisation problems that insurers have spent years trying to solve.  But better processing is only the starting point. Once detailed underwriting and claims information is structured and accessible, John and Greg believe insurers can begin applying predictive models to identify patterns that humans may struggle to find. John uses the example of “Move 37”, the unexpected move played by AlphaGo against world champion Lee Sedol, to illustrate the possibility: could AI uncover similarly unexpected insights in insurance data that lead to better pricing, risk selection and loss ratios?  They also discuss where this is already being applied. Greg explains the opportunity in post-bind processing, bordereaux and delegated authority business, particularly across London, Bermuda and the US. He also discusses Portage AI’s acquisition of technology and people from Insurwave and how bringing together pricing submission processing with post-bind capabilities could ultimately make operational data more useful for underwriting decisions.  The bigger ambition is not to replace insurers’ existing technology stacks. Instead, Greg describes an AI engine that can sit between systems, receiving and structuring information before feeding it into the applications that need it. With AI developing rapidly, he also explains why remaining technology-agnostic could matter, allowing different underlying models to be selected and trained depending on the problem being solved.  Finally, John and Greg explain why they see AI as a much bigger change than another wave of process automation. John believes the industry is entering a fourth industrial revolution and that, within the next three to five years, some insurers will begin using these technologies to grow market share profitably. Their message to insurers is simple: move beyond experimenting with AI, apply it to meaningful business problems and demand demonstrable value from the technology.  In this episode you’ll learn:  Why John and Greg believe AI represents one of the biggest technology shifts of their careers  What their experience delivering a major insurance technology transformation taught them about putting AI into production  Why some of the greatest opportunities for AI could lie in the difficult “last 20%” of insurance processing  How AI can help extract, harmonise and structure information from complex insurance datasets  Why creating a common ontology could unlock more value from underwriting and claims data  How better structured operational data could ultimately contribute to pricing, risk selection and loss ratio improvements  Why specialty insurance, reinsurance, delegated authority and bordereaux processing are particularly interesting applications for AI  How Portage AI approaches accuracy, explainability, security and human oversight when deploying models  Why Greg believes AI should integrate with existing insurance technology rather than requiring insurers to replace their core platforms  What AlphaGo’s famous “Move 37” can teach insurers about the potential for AI to uncover patterns and opportunities humans may have missed  Why John believes the next three to five years could reveal which insurers are successfully turning AI into competitive advantage  Why insurers should move beyond proofs of concept and demand measurable business value from AI  If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

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.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Introduction AI is already changing underwriting, but is the industry focusing on the part of the job where it can create the most value?  In this episode, Matthew Grant speaks with Jamie Wilson, VP of Strategy at hyperexponential, about the evolution of underwriting technology, the move from AI-assisted tools towards agentic workflows and what 350 senior underwriters across the UK and US say they actually want from AI.  Jamie explains why much of the value delivered by AI so far has been concentrated on efficiency and reducing manual administration. Yet when underwriters were asked what makes someone exceptional at their job, speed barely featured. Instead, they pointed to qualities such as risk selection, technical depth, judgement and commercial instinct. It raises an important question: rather than simply helping underwriters work faster, can AI help them make better decisions?  The conversation explores what changes as underwriting moves from AI-assisted to agentic. Instead of an underwriter controlling the workflow and calling on AI for individual tasks, an agentic system can move work through the underwriting process itself, involving a human when a decision requires expertise or falls outside predefined guardrails. Jamie discusses why those guardrails will be critical and how different portfolios could require very different levels of human involvement.  They also examine the implications for underwriting teams. As technology takes on more routine work, experienced underwriters could spend more time applying their expertise to complex risks, while technical portfolio teams may play a growing role in determining where and how greater autonomy should be used.  Finally, Jamie shares one of the most striking findings from the research: the "succession paradox". Underwriters identified the loss of senior judgement before it can be passed to the next generation as their biggest future concern, yet coaching junior colleagues ranked last among the investment priorities explored in the survey. Could AI help bridge that gap by making accumulated underwriting knowledge and expertise more accessible to the next generation?  In this episode you’ll learn:  Why focusing on efficiency alone could underestimate the potential of AI in underwriting  What underwriters believe separates exceptional underwriting from simply working faster  The difference between AI-assisted and agentic underwriting  How guardrails can determine when an AI agent acts autonomously and when an underwriter steps in  Why different portfolios may require very different approaches to agentic underwriting  How greater automation could allow underwriters to focus their expertise on more complex risks  Why existing technology architecture and point solutions could complicate end-to-end agentic workflows  What the "succession paradox" reveals about the industry's approach to developing junior underwriting talent  How AI could potentially help transfer expertise from experienced underwriters to the next generation  Book recommendation:   Thinking, Fast and Slow by Daniel Kahneman for its insights into human judgement, data-driven decision-making and the psychology of adopting new technology.  Read hyperexponential's Underwriting Edge Report Discover what 350 senior commercial P&C underwriters say defines underwriting excellence, where AI is delivering value and what could hold the industry back in hyperexponential’s Underwriting Edge 2026 report. Curious for more? Catch-up with all that happened at hx Live NYC on 17 September. If you like what you’re hearing, please leave us a review on whichever platform you use or contact Matthew Grant on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Introduction  Facultative reinsurance can give insurers the additional capacity they need to write risks they might otherwise decline. But for smaller, more transactional placements, its value increasingly depends on how quickly that capacity can be accessed.  Joining Matthew Grant on the podcast, Dom Sindall, Head of Digital Broking, Facultative at Marsh Re, and Stephen Sheehan, Head of Analytics, Facultative at Marsh Re, explain how digital trading, automation and better data could reduce facultative placement times from days to hours or even minutes.  Dom explains why much of the delay in facultative reinsurance comes not from the work itself, but from the elapsed time between people completing each stage of a placement. Removing some of those dependencies could make facultative reinsurance significantly more accessible for smaller risks, where speed can matter more than price.  The discussion explores how advances in AI are helping automate another source of friction: insurance data. Stephen explains how extracting policy information from documents has become increasingly straightforward, while exposure data remains more challenging. Schedules of values (SOVs) can contain dozens of tabs, multiple currencies and inconsistent terminology, making ingestion, standardisation and quality control critical parts of the automation process.  They also examine where humans should remain involved. Fully automated workflows offer greater speed, but Dom and Stephen explain why firms need to determine what level of data quality is ‘good enough’, where validation gates are required and which capabilities should be built internally or bought from technology partners.  Looking ahead, Dom explains why the growth of digital trading could expand the facultative market itself. Reinsurers that invested early in data standards have been able to move quickly, while the next wave may increasingly look to partnerships to accelerate their own digital capabilities.  In this episode you’ll learn:  Why speed is becoming increasingly important for smaller facultative placements  How automation could reduce placement times from days to hours or minutes  Why AI has made extracting policy data significantly easier  Why exposure data remains a more difficult automation challenge  How to decide where human validation belongs within automated workflows  Why ‘good enough’ data can be more commercially valuable than pursuing perfection  How insurers and brokers can approach build versus buy technology decisions  Why early investment in data standards is creating an advantage in digital trading  How reducing friction could help grow the facultative reinsurance market  Book recommendations:  Stephen Sheehan: Don’t Believe Everything You Think by Joseph Nguyen  Dom Sindall: The Innovator’s Dilemma by Clayton Christensen  If you like what you’re hearing, please leave us a review on whichever platform you use or contact Matthew Grant on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Insurers are moving quickly from asking what AI can do to asking how they can actually make it work in underwriting. But experimenting with AI is one thing. Running it reliably across an insurance business is another.  In this episode, Matthew Grant speaks with Andy Moss, GM Underwriting at Duck Creek and former Co-founder and CEO of Send, following Duck Creek’s acquisition of the underwriting technology business.  Andy explains why successful enterprise AI needs to run “on rails”, supported by the infrastructure, governance and workflows required to move beyond proofs of concept and into production. He explores how underwriting workbenches are evolving into orchestration engines and why that shift could become increasingly important as insurers begin introducing AI agents into their workflows.  The conversation also examines a growing challenge for the industry: how to preserve underwriting knowledge as experienced professionals retire. With underwriting becoming more complex, Andy argues that insurers need to codify the processes, referrals and institutional knowledge that often still exist in people’s heads, creating a technology foundation that can support both underwriters and AI.  They also discuss how insurers should approach the increasingly important question of whether to build or buy technology. Andy’s advice is simple: “Buy the commodity and build the differentiators.” Rather than recreating established systems, insurers have an opportunity to focus their development efforts on the data, processes and underwriting expertise that genuinely give them an advantage.  Finally, Andy shares the story behind Send’s acquisition by Duck Creek, how a search for growth investment became an acquisition in around 120 days and what building and selling Send taught him about the importance of assembling a team that genuinely cares about the problems customers are trying to solve.  In this episode you’ll learn:  Why enterprise AI needs to run “on rails” to succeed in underwriting  How underwriting workbenches are evolving into orchestration engines  Why AI proofs of concept can struggle to make the transition into production  How agentic AI could change underwriting workflows and automate previously manual tasks  Why capturing institutional knowledge is becoming increasingly urgent as experienced underwriters retire  How technology can codify the processes and knowledge that currently sit inside underwriting teams  Why insurers should “buy the commodity and build the differentiators”  Where internal technology teams can create the greatest competitive advantage  What bringing underwriting orchestration and core insurance systems together could enable  How Send’s search for growth funding developed into its acquisition by Duck Creek  Why Andy believes shared values and genuine commitment to customers were critical to building Send  Andy’s recommendations:  Invest Like the Best with Patrick O’Shaughnessy  Desert Island Discs  Read InsTech's recent report with Send on 'Underwriting orchestration engines: equipping insurers for the AI age' here. Register for InsTech's upcoming 7 October Evening Event with Send and Duck Creek here: Bridging between the islands of automation. If you like what you’re hearing, please leave us a review on whichever platform you use or contact Matthew Grant on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Introduction:   Growth equity can open the door to the next stage of an MGA’s growth. But the bigger question is how that capital can be used to strengthen underwriting, accelerate technology and build a delegated business that can compete at greater scale.  Joining Robin Merttens on the podcast, Rebecca Ince, Chief Operating Officer at Carbon Underwriting, and Mark Oldroyd, Chief Technology Officer at Carbon Underwriting, explain what comes next following Carbon’s growth equity investment from FTV Capital.  Rebecca explains why finding the right investment partner was about more than funding. With Carbon growing from £150 million of premium in 2023 to £450 million this year, its next phase requires greater investment in technology alongside ambitions to expand further into the US. FTV’s experience across financial technology and US insurance made both areas important factors in the decision.  The discussion explores how that investment could accelerate Carbon’s technology strategy, particularly its proprietary Graphene analytics platform. Mark explains why centralised, structured data provides a strong foundation for AI and where the technology is already creating practical value, including claims matching. He also argues that MGAs need to distinguish between technology they should build themselves and capabilities they can buy, while protecting the underwriting expertise, workflows and intellectual property that differentiate them.  They also discuss why bordereaux are unlikely to disappear anytime soon, despite widespread appetite for richer and more frequent data exchange. Rather than waiting for the entire ecosystem to change, Mark explains why the opportunity is to remove friction from collecting and standardising data, allowing insurers to focus more attention on the insights and decisions that come from it.  Looking ahead, Rebecca explains why Carbon plans to remain focused on delegated underwriting while expanding into new classes and geographies, particularly the US. They also explore how analytics can help identify rate-adequate opportunities as conditions soften and what it takes to protect culture as a business rapidly scales beyond 100 people.  In this episode you’ll learn:  Why Carbon chose growth equity and what it looked for beyond capital when selecting an investment partner  How centralised, structured data creates a stronger foundation for applying AI across underwriting and claims  Where AI is already delivering practical value within Carbon’s insurance workflows  How MGAs can decide which technology capabilities to build and which to buy  Why proprietary underwriting expertise, workflows and data are becoming increasingly important sources of differentiation  Why bordereaux are unlikely to disappear from delegated underwriting in the next three to five years  What is preventing carriers and MGAs from exchanging richer, more frequent data  Why Carbon sees the US as its next major growth opportunity  How analytics can help underwriters find rate-adequate business as market conditions soften  What rapidly growing MGAs can do to protect their culture as they scale  Hear more from Carbon at The Golden Age of MGAs  Carbon is sponsoring InsTech’s upcoming The Golden Age of MGAs? Building to win in any market cycle event on 24 September. Rebecca will moderate the Capital diversification and the resilient MGA panel, joined by Carbon Co-Founder and Managing Director Ben Laidlaw, CFC Underwriting’s Philippa Berry and Gallagher Re’s Jane Fenton. They’ll explore practical approaches to multi-carrier strategies, long-term capacity agreements, fronting, reinsurance access and patient capital structures that can withstand a full market cycle.  Find out more and register here. Career opportunities at Carbon  Carbon is continuing to grow its team and is looking for people interested in helping shape the future of delegated underwriting across underwriting, technology, data and operations.  View current opportunities at Carbon.   If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Introduction  AI is reshaping insurance, but successful transformation isn't just about adopting the latest technology. It's about designing tools that people actually want to use.  In this episode, Robin Merttens is joined by Dani Katz, Co-founder and Director of Optalitix, to explore why the future of pricing lies in bringing underwriters and actuaries closer together rather than forcing them into the same way of working.  Drawing on recent industry research and practical experience supporting insurers and reinsurers, Dani explains why human-centred design is becoming just as important as technical innovation. From the enduring role of Excel to the rise of natural language AI, the conversation explores how technology can remove repetitive work while giving insurance professionals more time to focus on judgement, strategy and commercial decision-making.  You'll also hear why Dani believes AI will create new opportunities across the insurance market rather than replace the next generation of talent.  In this episode you'll learn:  Why successful pricing transformation depends on people as much as technology  How AI can simplify underwriting without becoming a 'black box'  Why actuaries and underwriters need different tools and different user experiences  What insurers can learn from the continued popularity of Excel  How modern pricing platforms are helping bridge the gap between actuarial models and underwriting workflows  Why removing manual data preparation could unlock more strategic work for actuaries  What the future of the London Market could look like as AI and automation become mainstream  Why expanding insurance coverage, not reducing headcount, could be AI's biggest impact on the industry  If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Introduction  Insurance has never been short of technology promises. From Blueprint 2 to successive waves of digital transformation, the ambition has often outpaced the results. According to Elizabeth Wooliston, that's beginning to change.  Joining Robin Merttens on the podcast, Elizabeth draws on more than 30 years in the London Market to explain why the current wave of intelligent automation feels fundamentally different. It's not simply that AI has become more capable. Market conditions have shifted, brokers have embedded digital strategies into their operating models and carriers are under increasing pressure to respond to risks faster without compromising underwriting quality.  The discussion explores where automation is delivering value today, from follow markets and facilities to the far more complex challenge of open market placements and policy servicing. Elizabeth also explains why organisations should think beyond AI itself, arguing that success depends on structured data, specialist insurance knowledge and governance rather than simply adopting the latest large language model.  They also discuss how attitudes towards technology are changing across the market. Instead of replacing underwriters, intelligent automation is increasingly being viewed as a way of removing repetitive administration, allowing experienced professionals to spend more time applying judgement, developing client relationships and mentoring the next generation of talent.  In this episode you'll learn:  Why Elizabeth believes the London Market has reached a genuine inflection point for technology adoption  How brokers and carriers are creating new momentum for digital risk placement  Where intelligent automation is already improving underwriting workflows  Why open market placements represent the next major challenge for AI  The trade-offs insurers should consider when deciding whether to build or buy AI capabilities  What organisations need in place before agentic AI can be deployed successfully  Why governance and insurance-specific expertise are becoming competitive advantages  How changing expectations across the workforce are influencing technology adoption  What Artificial's recent expansion says about the growing demand for intelligent insurance infrastructure  If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

For decades, catastrophe modelling has largely been about choosing the best view of risk. But what happens when no single model can capture the full picture?  In this episode, Matthew Grant speaks with Rob Newbold, President of Catastrophe and Risk Solutions at Verisk, about why the future of catastrophe modelling is becoming more open, collaborative and accessible.  Rob explains the thinking behind Verisk's new Model Exchange platform and why enabling insurers to access third-party models alongside Verisk's own is less about changing strategy and more about continuing a long-standing commitment to giving clients greater choice. The conversation explores how broader access to catastrophe and cyber models could help organisations build a more complete understanding of risk while making advanced analytics available to a much wider audience.  They also discuss why the global protection gap remains stubbornly difficult to close, why parametric insurance has not yet delivered on many of its early promises and where new modelling capability is still urgently needed, from flood to wildfire.  Looking ahead, Rob shares his perspective on how agentic AI could fundamentally change catastrophe modelling workflows, allowing insurers to automate scenario analysis, respond more quickly to emerging events and make sophisticated risk analytics available without requiring specialist modelling expertise.  In this episode you'll learn:  Why Verisk has opened its platform to third-party catastrophe and cyber models  How Model Exchange helps insurers build a more comprehensive view of global risk  Why collaboration between model providers could strengthen resilience across the industry  Where the biggest gaps remain in catastrophe modelling, including flood and emerging risks  Why the protection gap remains a persistent challenge despite better analytics  The reality of parametric insurance and why basis risk continues to limit wider adoption  How cloud platforms and SaaS delivery are making catastrophe models accessible beyond specialist insurance teams  What practical applications of agentic AI could look like for catastrophe modelling over the next 12 months  Why openness, transparency and competition may ultimately improve risk understanding across the market  Rob's recommendations:  Outliers by Malcolm Gladwell  Smart Brevity by Roy Schwartz, Mike Allen and Jim VandeHei  If you like what you’re hearing, please leave us a review on whichever platform you use or contact Matthew Grant on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.