Great Expectations for insurers

Insurance was built on one powerful belief: That past can price the future. For decades, actuaries studied historical loss data. Catastrophe models relied on past weather patterns. Underwriting assumed yesterday’s risks could guide tomorrow’s pricing. But something fundamental is changing. Floods are becoming more severe. Wildfires are spreading faster. Storms are hitting harder. And the numbers are becoming impossible to ignore. Global insured losses from natural catastrophes now exceed $100 billion every year. Even more alarming: Climate related losses could nearly quadruple by 2050. This is more than an environmental story. It’s a financial system story. Insurance quietly underpins the global economy. Homes, ports, factories, infrastructure projects and even renewable energy investments rely on insurance to unlock financing. But when risk is becoming too unpredictable, insurance is becoming expensive or in the future it might risk disappearing. We’re already seeing early signals. In some regions, insurers are pulling back from wildfire prone and coastal areas, leaving homeowners and businesses struggling to find coverage. And that reveals a deeper truth : What is not insurable will eventually become not investable. Industry knows that the stakes are rising. 88% of insurers are concerned about climate driven risks becoming systemic financial threats 96% worry about the long term insurability of infrastructure So the rules of risk are being rewritten. Historical models alone are no longer enough. Leading insurers are now combining satellite data, geospatial analytics, terrain mapping and advanced catastrophe modelling to build a forward looking view of risk. Underwriting itself is evolving: Higher deductibles. Hyper local pricing. Scenario planning focused on near term horizons like 2030. But disruption also creates opportunity. More than 90% of insurers now see growth in climate resilience and risk advisory, helping businesses and governments identify vulnerabilities before disasters strike. New solutions are emerging: Parametric insurance that pays automatically when triggers like rainfall or wind speeds are breached. Protection for nature based resilience such as mangroves and coral reefs that shield coastlines. Advanced analytics guiding climate adaptation investments. In short, role of insurance is quietly transforming. From paying for disasters to helping prevent them. Insurance is slowly evolving from a mere financial product to climate canary of global financial system. Ultimately when insurers start worrying about the future, it often means the future is already arriving. I would leave you with this important question: Are we prepared for a world where risk itself becomes uninsurable? Refer attached report for detailed insights.⬇️ #Insurance #ClimateRisk #RiskManagement #ClimateFinance

AI in insurance is not a productivity hack 🚫 Automating the past is safe and will generate marginal returns. The real value lies in underwriting the future! AI is being talked about everywhere in insurance. Too often, the conversation stalls at efficiency theatre. Faster underwriting. Cheaper claims handling. Fewer people doing more work. Useful, but small. The real opportunity sits elsewhere. Reimagining Risk in an AI-Driven World, developed by the International Insurance Society, captures this shift well. Having contributed to the report and led the executive workshop in Zurich, one message came through very clearly: the next decade will separate insurers making marginal improvements from those rebuilding their operating models around new forms of risk, data, and human judgement. AI is not the strategy. It is the unlock 🔓 The strategic upside is not incremental. It sits in: • New insurable risks emerging from intangible assets, cyber, AI, and climate • Proprietary knowledge graphs, data, decision systems become a true edge • Human judgement being augmented, not replaced, in a trust-based industry • Governance, talent, and data strategy becoming board-level differentiators, not IT issues 🤩 One stat should give leaders pause. Nearly 90% of firms are experimenting with GenAI, yet only around a quarter have anything in real production. Plenty of motion. Limited transformation. That gap is not about technology. It is about operating model courage. Keen to hear from peers across insurers, reinsurers, brokers, MGAs, and insurtechs: • Where have you seen AI move the needle beyond efficiency? • What is genuinely blocking scaled deployment? • Are we underwriting new risks fast enough, or just automating old ones? If insurance gets this right, we don’t just adapt to an AI-enabled world. We become one of its core stabilisers. Thoughts and counter-views welcome. Full report link in comments 👇 Anders Malmström, Joshua Landau, Colleen McKenna Tucker

The Insurance Industry Is at an Inflection Point – and AI Is Leading the Charge From outdated systems and unstructured data to rising customer expectations and talent shortages — insurers are under immense pressure. But with Generative AI, there’s finally a real way out. What’s Changing? 1. 60% of operational costs are still manual – AI can slash that. 2. 80% of data is untapped – GenAI reads, learns, and leverages it. 3. Only 18% of insurers currently use AI – but that’s about to change. Key Impact Areas: ✅ Underwriting: 90% data accuracy + new product models. ✅ Claims: 70% of simple claims can be auto-resolved + up to 50% faster processing ✅ Customer Experience: 48% higher NPS, 85% faster resolutions ✅ Fraud Detection: AI flags 75% of fraudulent claims in real time ✅ Sales & Distribution: AI agents, personalized funnels, smarter upsells ✅ Policy Admin: Real-time compliance, automated changes, predictive lapse alerts ✅ New Products: From behavior-based insurance to once “uninsurable” tech like drones & autonomy It’s not just about automating workflows. It’s about rethinking the very DNA of insurance using AI-first foundations. And those who don’t adapt — risk becoming obsolete. Whether you're transforming an incumbent or building the next vertical AI unicorn — the time is now.

Leadership in the Age of AI: Getting Educated AI is no longer just a “tech topic.” It’s a leadership priority — and one we can’t afford to delegate. If you’re in insurance and still unsure how AI works or where it fits, you’re not alone. But you are accountable. You don’t need to code. I don’t. But you do need to understand what AI means for your clients, your teams, your risk models — and your relevance. Here are some ideas of how you build AI fluency as a non-technical leader — and where I recommend others begin: AI Learning Starter Kit (Designed for Insurance Leaders) Podcasts • The AI Breakdown – practical daily briefings • In Machines We Trust – MIT’s ethical AI and tech impact series • McKinsey Forward Thinking – strategy + innovation insights Books • The Coming Wave – Mustafa Suleyman • Power and Prediction – Agrawal, Gans, Goldfarb • Futureproof – Kevin Roose 2024–2025 Reports That Changed My Thinking 1. McKinsey – The Potential of GenAI in Insurance (2024) Insurers leading in AI adoption share six traits — including leadership sponsorship and clear business alignment. 2. IBM – You Can’t Win If You Don’t Play (Oct 2024) 77% of insurance execs believe GenAI is critical to compete — but only 29% of customers are confident in it. 3. Deloitte – Are Insurers Ready to Scale GenAI? (Apr 2025) The shift isn’t from ‘no AI’ to AI — it’s from pilot to scale, and that requires strategy, accountability, and ROI clarity. 4. EY-Parthenon – Generative AI in Insurance (May 2024) 84% of insurers plan to stand up dedicated GenAI teams — but internal alignment and regulatory friction remain blockers. 5. SAS – Global GenAI in Insurance Study (Oct 2024) Almost 90% of insurers expect to invest in GenAI in 2025 — yet preparedness and implementation confidence lag behind. 6. PwC – GenAI Insurance Trends (Dec 2024) 70% of CEOs believe GenAI will reshape value creation; most expect gains in product and client experience within 12 months. These insights have helped me build confidence in AI — and they’re shaping how I lead. If AI is redefining risk, value, and service — then understanding it is no longer optional. We must be ready to lead through its implications. #AILiteracy #InsuranceLeadership #GenerativeAI #ExecutiveLearning #DigitalFluency #AIinInsurance #LondonMarket

When insurability becomes the new frontier of AI governance Last week, AIG, Great American Insurance Group, and W. R. Berkley Corporation asked US regulators to exclude genAI losses, arguing the technology is “too much of a black box” to insure. Insurers are the world’s early-warning system Their data reveals emerging fragility and a new danger: dependence on a few AI models creates systemic concentration risk. Reinsurers are tightening retrocession terms, proof the old risk model no longer fits AI. 3 signals dominate: 1️⃣ The statistical foundations have broken Insurance works when the future matches the past. GenAI doesn’t. Its shifting behaviour breaks historical predictability. As with pandemics, cyber and climate, actuarial models fail and risk is volatile 2️⃣ A new class of systemic risk GenAI failure scales instantly and identically: ▪️ one update → thousands of errors ▪️ one model → global exposure ▪️ one hallucination → sector-wide liability ➡️ Model monoculture is the real risk: one bad update can ripple through thousands of firms. 3️⃣ The regulatory pattern is predictable: 🔹 insurers tighten terms 🔹 supervisors tighten expectations 🔹 markets tighten governance The EU AI Act is a first step. US regulators are assessing operational-risk capital for AI. Asian regulators are reviewing dependency on US models. 4️⃣ Capital markets reinterpret the signal When insurers step back, capital markets reprice. What cannot be insured will become more expensive to finance, operate and justify to shareholders. ➡️ A sign that AI adoption has outpaced the world’s ability to govern it. 5️⃣ Liability shifts back to the enterprise When insurers exit, enterprises inherit the risk: more capital, slower rollout, tighter controls, and heavier governance. This is when experimental technology becomes a fiduciary exposure. Real-world cases show why insurers are moving now: 🔹 Google faces a $110M lawsuit after its AI fabricated a legal investigation 🔹 Air Canada was forced to honor a discount invented by its chatbot 🔹 Arup lost $25M after a deepfaked executive authorised transfers. ➡️ All ordinary workflows amplified by AI. The governance frontier Insurers will underwrite AI that behaves in ways the risk market can understand, audit and price. AI designed to operate inside limits: ✔️ narrow, auditable scope ✔️ constrained behavior ✔️ models small enough to reason about ✔️ provenance for every decision ✔️ traceability built in ➡️ The future of enterprise AI will be governed, safer, more explainable. 👉 Should uninsurable technology be allowed inside critical workflows? 👉 Where does liability sit when model behavior becomes systemic failure? Sana Yaakoubi Josée Deroy would love to hear from you. #RiskManagement #GenerativeAi #AIgovernance #Boardroom #StratEdge

Here’s Part 3 of my AI and Insurance Coverage series — A Playbook for Long-Term Resilience in a New Era As AI becomes central to underwriting, claims, and operations, insurers need a governance-driven, forward-looking strategy. The past year has shown both accelerating adoption and rising exposures, along with greater regulatory scrutiny and early signs of market tightening. A modern playbook must reflect these realities. 1. Governance as the Cornerstone of Insurability Expectations around AI governance have rightly become more stringent. Clear documentation, version control, continuous monitoring, drift detection, explainability, and human oversight for high-impact use cases are key. Independent audits and strong vendor-governance measures are increasingly important. 2. Policy Design Reflecting a Tighter Market AI liability coverage has matured, and insurer responses are shifting. Some are narrowing coverage, while others are piloting more targeted AI products. Key issues for effective policy are: Clear definitions of AI failures and governance thresholds Conditions tied to oversight, monitoring, and approved model updates Modular structures that insure auditable use cases while limiting opaque or high-risk systems On the flip side, insureds will need blended approaches that include contractual controls and internal safeguards along with insurance. 3. Claims Readiness Requires Cross-Functional Expertise AI-driven claims events may require super technical investigation. Claims teams may need supports from data scientists, engineers, and forensic specialists. Preserving model artifacts, training data histories, and decision logs will likely be critical for root-cause analysis and allocation of responsibility. Claim teams also should prepare for cascading failures affecting multiple insureds at once. 4. Aggregation and Systemic-Risk Management Common AI platforms, shared vendors, and cloud dependencies create correlated exposures. Stress-testing portfolios, monitoring vendor concentration, and modeling scenarios involving simultaneous failures should be looked at. Layered risk-sharing, through reinsurance or pooled structures, may become essential as systemic risk grows. 5. Regulatory Alignment Across the Lifecycle Regulatory expectations for transparency, monitoring, and fairness have increased. Insurers should look for ways to integrate compliance into underwriting, claims handling, and continuous oversight. In these circumstances, alignment with evolving standards is becoming a strategic requirement, not just a legal one. 6. Education and Collaboration Internal teams will need ongoing training on AI risk and operational dependencies. And insureds will benefit from guidance on governance, documentation, vendor oversight, and human-in-the-loop protocols. In this dynamic era, collaborative industry efforts are vital for building realistic and consistent risk frameworks. #Cyberinsurance #AI

The insurance industry is about to cross a critical threshold. 2025 was experimentation. 2026 is execution at scale. I've analyzed the latest research from BCG, McKinsey, and industry leaders. The pattern is unmistakable: Insurers are moving from "Can we trust AI?" to → "How fast can we integrate it?" Here are the 12 use cases driving real results: → Automated claims from first notice to settlement → Computer vision for instant damage assessment → Real-time fraud detection across millions of claims → Dynamic underwriting with telematics and IoT data → Document extraction from 200+ page submissions → 24/7 virtual assistants with omnichannel memory → Personalized policy recommendations at scale → Proactive alerts for renewals and coverage gaps But the real game-changer for 2026? Agentic AI. Multi-agent systems that work as "virtual coworkers." 1. One agent ingests documents. 2. Another builds risk profiles. 3. A third prices the policy. 4. A fourth checks compliance. 5. A fifth orchestrates the decision. Allianz already deployed this. Result: 80% reduction in claim processing time. The numbers across the industry: • 70-90% of simple claims processed automatically. • 30-50% cost reduction in AI-automated workflows. • AI spending growing 25%+ this year. Only 7% of insurers have scaled AI successfully so far. That gap is where competitive advantage lives. The advice I give my insurance clients: Don't wait for perfection. Start with one high-volume process. Build the muscle. Then scale. Which AI use case would transform your insurance operations most? ⬇️ Let me know in the comments → Join AI-Empowered Leaders: My weekly newsletter with actionable AI insights from my work as AI-advisor, trainer & coach. Sign up here 👇 https://lnkd.in/eUmy2Bdp ♻️ Repost to help your network prepare for the AI transformation in insurance