Insurtech Company Growth

Is this the end of InsurTech as we know it? Every six months, we compile data on the European InsurTech ecosystem using market research, deal tracking, and our proprietary scouting tools. Below is a summary of our recent webinar, highlighting key KPIs, major trends, and future expectations. 1/ InsurTech KPIs Investment headlines often focus on declines, but the picture is more nuanced in InsurTech. While the number of deals dropped to 61 last year, total funding reached €820m, a year-on-year increase. France led in total funds raised (thanks to massive rounds by Alan and Akur8), the UK topped deal activity, and Germany lagged behind. Outside these core markets, Switzerland and Spain showed strong performance, particularly in "emerging risks." To me, InsurTech investment trends mirror global VC and FinTech dynamics, showing a post-peak stabilization rather than sector-specific decline. 2/ Major Trends The post-pandemic shift from “growth at all costs” to “profitable growth” is reshaping the ecosystem. Startups like Mila and Acheel (France), Clark (Germany), Cuuva (UK), and EIR (Sweden) have achieved profitability, with others aiming to follow by optimizing CAC/LTV ratios and operational efficiency. Meanwhile, private rounds and cost-cutting measures dominated last year, but consolidation is increasing. Allianz Direct was notably active, acquiring Luko, iptiQ, and Friday. CEO resignations were frequent in 2024, with 10 companies—including unicorns like Wefox and Clark—publicly announcing leadership changes. This reflects challenges & opportunities in navigating profitability and market shifts. 3/ What’s Next? a/ AI in Insurance With 30% of InsurTech funding going to AI-first companies, and 18% of deals focused on AI, automation is set to transform the sector. Agentic AI, predicted as the next wave of RPA, could unlock operational efficiencies across the industry. b/ Embedded Insurance Long discussed, embedded insurance is finally gaining traction. Platforms like Qonto and Ornikar did integrate insurance into their ecosystems, reflecting a broader trend where platforms adopt financial services—and insurance is the natural next step. c/ Emerging Risks Startups addressing risks like cybersecurity, carbon credit insurance, and climate-related threats are on the rise, accounting for 20% of deals last year. This segment presents opportunities for technology and data-driven solutions to support incumbents in managing new risks. #insurance #insurtech #venturecapital

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

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐰𝐚𝐧𝐭𝐬 𝐀𝐈. 𝐕𝐞𝐫𝐲 𝐟𝐞𝐰 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐚𝐫𝐞 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝 𝐟𝐨𝐫 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐱𝐩𝐨𝐬𝐞𝐬. The insurance industry crossed a line in March 2026 that most leaders are just starting to recognise. AI is no longer living in innovation decks. It is now embedded in live underwriting decisions, claims processing, customer interactions, and portfolio management and that changes the entire game. Here is what is becoming impossible to ignore this month: 𝟏. 𝐏𝐢𝐥𝐨𝐭 𝐩𝐡𝐚𝐬𝐞 𝐢𝐬 𝐨𝐯𝐞𝐫 Capgemini's 2026 outlook confirms it: AI is driving measurable value across underwriting, claims, and customer engagement right now. The question is not "Should we test AI?" anymore. It is "How do we scale this without breaking what works?" 𝟐. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐬 𝐚𝐫𝐞 𝐰𝐚𝐭𝐜𝐡𝐢𝐧𝐠 The UK FCA announced it will assess AI use in underwriting, claims, and consumer services this year. Translation: if you cannot explain how your AI makes decisions, you have a problem. 𝟑. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐚𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐭𝐡𝐞𝐫𝐞 Capgemini reports 60% of customers are willing to share personal data for more tailored coverage. The demand for personalized insurance is not coming. It is already here. 𝟒. 𝐀𝐈 𝐢𝐬 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤 Data quality is. Legacy systems are. Governance is. AI does not hide infrastructure problems. It amplifies them. And it is happening faster than most leadership teams anticipated. 𝟓. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐩𝐥𝐢𝐭 𝐢𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 𝐧𝐨𝐰 The winners in 2026 will not be the insurers using the most AI tools. They will be the ones who can clearly answer these three questions: • What decision is AI influencing? • Who owns accountability for that decision? • Can we explain it to regulators, customers, and the board in plain language? That clarity is where the market is dividing. Not between AI adopters and non-adopters. Between insurers scaling AI with governance and insurers still just experimenting. The differentiator is not AI anymore. It is operational readiness. What is blocking progress in your organization right now: data quality, governance frameworks, legacy infrastructure, or leadership alignment? #AIinInsurance #InsuranceLeadership #InsurTech #AIGovernance #FutureOfInsurance #DecisionIntelligence

In the first wave of insurtechs, Hippo, Root, and Lemonade each came out swinging. Each had a bold thesis: Hippo: Meet homeowners at their moment of need—mortgage closings, real estate flows, IoT devices. Root: Leverage smartphone motion data to price auto risk better, faster, and cheaper. Lemonade: Reinvent renters insurance with self-service quotes, instant claims, and a UX so frictionless it felt fun. But beneath the glossy branding was the same structural weakness: The Cascading Miracle Trap Each model relied on a stack of “ifs” that all had to click perfectly: Embedded distribution partners executing flawlessly. Regulatory buy-in at scale. Actuarial rigor catching up to new data sources. Customers staying loyal long enough for lifetime value to materialize. One broken link? The economics unraveled. Take Lemonade: Renters insurance turned out to be a high-churn product. At $5/month, it lacked the premium base to cross-sell into more profitable lines. Their bet that renters would “graduate” into homeowners—with the same brand loyalty—didn’t play out at scale. Or Root: Telemetry data was ahead of its time, but actuarial credibility lagged. Pricing precision didn’t keep pace with growth. Or Hippo: Their embedded flows depended on partner quality and underwriting consistency across fragmented channels. Both were harder to scale than anticipated. It’s like building a Jenga tower: Distribution, pricing, retention, loss ratios, and customer behavior—all critical. One loose block and the whole thing wobbles. What Smart MGAs Are Doing Instead The new generation of MGAs is taking these lessons to heart: ✅ Underwrite first. Grow second. ✅ Start with carrier-grade rigor, not just a sleek app. ✅ Focus on margin from inception—because in insurance, there’s no blitzscaling your way past bad pricing. The first wave showed what was possible. This wave is showing what’s sustainable. 👉 Full breakdown of each play—and how today’s operators are flipping the script: They Ran So We Could Earn: The Insurtech Lessons https://lnkd.in/gBfQ7g4u

The AI race in insurance is shifting from experimentation to implementation, and CB Insights’ hiring signals make this impossible to ignore. We identified the fastest-growing agentic AI-focused insurtechs and found that 7 of the top 9 are prioritizing implementation-focused roles. Two themes stand out: client education on AI adoption and forward-deployed engineering. These are roles designed to get AI working in production, not just in pilots. All but one of these companies raised funding since March 2025, suggesting that implementation capability has become a prerequisite for AI-focused insurtech funding. But here's the tension driving this hiring: insurtechs are doubling down on implementation in part because their customers can increasingly build in-house. CB Insights’ Hiring Insights on some of the largest insurers — including Aviva, Chubb, and MetLife — show they are moving quickly to build AI capabilities in-house. Insurance executives will increasingly expect implementation efforts to deliver measurable ROI. That bar will determine which insurtech partners win and which get replaced by in-house teams.

Insurance breaks startup timelines. Most startups plan 12-18 months for MVP → early traction → the next raise. But insurance takes 2x longer than founders expect. Your first 18 months vanish into authorisation, capacity and integrations. Why nothing moves fast here: 👉  Regulation isn't a checkbox: - Using another firm's licence (AR route): weeks to months, but move at your principal’s pace - Direct FCA authorisation? 6-12 months of detailed business plans - Full carrier (FCA + PRA)? 12-24+ months including mobilisation 👉 Partners shape your path: - You don't just "get a panel" - you earn capacity - Insurers want 12+ months of loss ratios, pricing models, and fraud controls - If you’re new, bring a credibility pack: team pedigree, explainable pricing, early selection signals, a claims plan - Translation: prove your book won’t blow up their balance sheet 👉  Integrations take quarters, not sprints: - Core systems, policy administration, claims platforms - most require lengthy integrations with legacy infrastructure that predates the internet - Everything moves at the speed of compliance, not code Extended timelines demand patient capital. When it takes 24+ months to prove your model, you need bigger investment rounds earlier, with backers who understand insurance cycles. A playbook that works: 1. Start lean (AR/MGA/DA). Ship narrow, fast - one product, one channel, configurable systems. 2. Prove the model. Show you can pick good risks, price fairly, stop fraud, and pay claims fast + accurately. 3. Earn capacity. Turn proof into paper/terms; engage early with partners and regulators. 4. Go deeper (MGA → full-stack) when you have a repeatable selection edge. Add lines, limits, markets. Few make it through the gates - and the survivors build outsized moats. The defensibility in insurance isn’t (just) the tech. It’s the track record you earn over time, proprietary distribution and data to improve pricing. The hardest thing to copy is a multi-year book that partners and customers trust. Which other sectors take years to reach the metrics most startups hit in 12–18 months? ----------------------------------------- ♻️ Share with someone building in insurance. 🔔 Follow Phoebe Chibuzo Hugh for more like this.

I’ve seen many insurers experimenting with AI - but only a few are realizing transformational value. In our latest report, which I had the pleasure of co-authoring, we examine what truly separates AI leaders from the rest. The results were striking: 📈 Over the past five years, insurers leading in AI achieved 6.1x the total shareholder returns of AI laggards. This is more than a technology advantage, it’s a strategic imperative. So, what sets the AI leaders apart? ✅ They take an enterprise-wide approach to AI—not isolated pilots. ✅ They rewire their core processes: underwriting, claims, distribution, and customer service. ✅ They build a modern capabilities stack—scalable infrastructure, high-quality data, and reusable components. ✅ They invest just as much in change management and workforce enablement as they do in technology. ✅ They view gen AI and agentic AI not just as tools, but as differentiators capable of reasoning, empathy, and creativity. AI is becoming the defining force of competitive advantage in insurance, and the gap between leaders and laggards is widening fast. 📘 Explore our perspective here: https://lnkd.in/ekaV_Jyy #Insurance #AILeadership #GenAI #DigitalTransformation #FutureOfInsurance #AgenticAI #InsureTech #McKinseyInsight #FinancialServices

Only 7% of insurers have managed to scale AI. Yes, you read that right. An industry built on data, discipline, and decades of decision making is stuck in pilot purgatory. Think about this : To everyone’s surprise, Insurance embraced AI faster than most sectors even rivaling tech and telecom in adoption. With vast reserves of customer data and a culture trained on analytics, insurers had every reason to lead. Yet scaling has stalled. Because barriers aren’t just technological, they’re deeply human. Insurance worships certainty. Actuarial models chase near perfect accuracy. But AI? AI lives in probabilities, in “likelihoods,” not absolutes. That cultural clash makes leaders hesitant. Add fragmented teams, tech debt, and legacy contracts and progress slows to a crawl. Still, promise is too big to ignore. Take for example a claims handler. Before AI, most of his/her day was spent drafting routine updates. Now, at one global insurer, GPT powered models draft more than 50,000 claimant messages every day, trained on company language, reviewed by humans. Productivity has soared by 30%. But real shift is human: that handler now spends more time on complex, emotional cases, like supporting a family after a car accident, instead of being buried in paperwork. But here’s the catch : 70% of scaling challenges come from people and processes, not technology. Without alignment, AI can deepen silos, weaken compliance, or even erode customer trust. To me way forward looks clear. Insurers must think bigger than pilots and redesign workflows end to end. They must deliver consistently by appointing enterprise owners, building cross functional teams, and embedding training into daily work. Above all, they must foster a culture where leadership signals visible commitment, where best practices are codified, and where AI learning becomes part of everyday growth. Because overtime AI will more than just shape insurance, it will reshape insurers. Some will build resilience, efficiency, and new growth. Others will remain trapped in pilots, watching competitors set the new standards. So real risk isn’t AI failing us, it’s us failing to scale AI. Refer attached report for detailed insights.⬇️ #Insurance #InsurTech #AITransformation #ResponsibleAI #FutureOfInsurance

I thought I had it all figured out when I started BriteCo. I was wrong. Spectacularly wrong. Coming from my family's jewelry business that started in 1958, I figured the transition to insurtech would be straightforward. After all, I understood jewelry, I understood customers, and I had a vision for tech-driven insurance. But building BriteCo taught me that confidence without preparation is just arrogance in disguise. Here are the biggest mistakes I made: → Underestimating regulatory complexity: I thought getting licensed in all 50 states plus DC would be tedious but simple. Reality check: each state has unique requirements, compliance standards, and approval processes. What I budgeted for 6 months took nearly 18. → Over-indexing on features during product development: I was so excited about what we could build that I lost sight of what customers actually needed. We spent months perfecting features that looked impressive in demos but didn't move the needle for real users. → Assuming my retail experience would directly translate: Selling jewelry and insuring it are completely different beasts. The customer journey, risk assessment, and relationship dynamics required me to basically start learning from scratch. Each mistake felt crushing at the time. But they forced me to become a better changemaker and leader. The regulatory hurdles taught me patience and the importance of expert partnerships. The feature bloat reminded me that simplicity often wins. The industry learning curve showed me the value of beginner's mind. What's the biggest mistake that actually made you stronger as an entrepreneur?