Algorithmic economy for insurers

The industry with 6x the TSR vs. the average 2–3× is… insurance. Insurers that lead with AI aren’t just keeping pace, they’re creating 6× the shareholder returns of laggards. The reason? Making bold choices about where to build, buy, or partner ... and rewiring the business, not just dabbling in pilots. Often cast as risk-averse, insurance shows the opposite here: when insurers center strategy with AI, the rewards are exponential. Leaders have created six times the shareholder returns of laggards over the past five years. My colleague Tanguy Catlin has spent years guiding insurance and financial-services clients through transformation. He and our insurance colleagues highlight that, to win, insurers can double down on four of the six rewired components: (1) Business-led roadmap: tie AI directly to value creation, not tech curiosity. (2) Operating model at scale: embed AI into how the business runs, not just in pilots. (3) Flexible AI stack: technology designed for speed, modularity, and distributed innovation. (4) Adoption & change management: because even the best AI fails without human adoption. Here’s what outcomes look like for insurers who get serious: domain-level transformation has already yielded a 10-20% lift in new agent success and sales conversion, 10-15% growth in premiums, 20-40% lower cost to onboard customers, and 3-5% improvement in claims accuracy. These aren’t incremental tweaks, they move core levers that impact the top and bottom line. Full article linked below and authored by Nick Milinkovich, Sid Kamath, Tanguy Catlin, and Violet Chung, with Pranav Jain and Ramzi Elias. https://lnkd.in/df2GXpuq

🤔 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗛𝘆𝗽𝗲 𝘁𝗼 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻... while most carriers focus on operational efficiency — using AI to speed up existing processes — the real opportunity lies in fundamentally reshaping the cost curve itself... 𝗹𝗲𝘁 𝗺𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻: 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳 𝗶𝗻 𝗖𝗹𝗮𝗶𝗺𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗺𝗮𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸 𝗖𝗹𝗮𝗶𝗺𝘀 𝗖𝗼𝘀𝘁 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻:  𝗧𝗼𝘁𝗮𝗹 𝗖𝗹𝗮𝗶𝗺𝘀 𝗖𝗼𝘀𝘁 = 𝗟𝗼𝘀𝘀 𝗖𝗼𝘀𝘁𝘀 + 𝗟𝗼𝘀𝘀 𝗔𝗱𝗷𝘂𝘀𝘁𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗻𝘀𝗲 (𝗟𝗔𝗘) Loss Costs: Actual claim payouts (settlements, repairs, medical expenses) LAE: Operational costs to process claims (staff, technology, overhead) Trade-off Dynamic: Reducing LAE can increase Loss Costs if accuracy suffers; excessive LAE spending creates inefficiency 𝗧𝗮𝗸𝗲 𝘁𝘄𝗼 𝗽𝗮𝘁𝗵𝘀 𝗣𝗮𝘁𝗵 𝟭: 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗗𝗿𝗶𝘃𝗲 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗖𝘂𝗿𝘃𝗲) 𝗠𝗼𝘀𝘁 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲.. —using AI for incremental improvements: - Automated damage detection - Faster claim routing - Document processing acceleration - Fraud detection enhancement these efforts optimize existing workflows but operate within current structural constraints. 𝗣𝗮𝘁𝗵 𝟮: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗦𝗵𝗶𝗳𝘁 𝘁𝗵𝗲 𝗖𝘂𝗿𝘃𝗲) 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗰𝗮𝗿𝗿𝗶𝗲𝗿𝘀 𝗮𝗿𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 (𝗶𝗻 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗯𝗼𝘃𝗲) 𝗶𝗻 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗮𝗹𝘁𝗲𝗿 𝘁𝗵𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: - Computer vision, multi-modal systems that eliminate traditional inspection needs - 3D reconstruction from customer photos - Predictive models that enable proactive claim management - End-to-end digital experiences driven by agentic AI that generate compound data advantages 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 the carriers achieving 200%+ efficiency improvements aren't just automating—they're reimagining. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀: - 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗠𝗼𝗮𝘁: Proprietary datasets become more valuable over time - 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Technology amplifies expertise rather than replacing it - 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Each improvement enables the next breakthrough - 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗗𝗲𝘀𝗶𝗴𝗻: Better experiences drive data generation and business growth while your competitors optimize their current processes, the question becomes: are you using AI to get better at what you've always done, or are you reimagining what's possible entirely? 𝗧𝗵𝗲 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗵𝗮𝘀 𝗽𝗮𝘀𝘀𝗲𝗱..... 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗯𝗲𝗹𝗼𝗻𝗴𝘀 𝘁𝗼 𝘁𝗵𝗼𝘀𝗲 𝗯𝗼𝗹𝗱 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝘀𝗵𝗶𝗳𝘁 𝘁𝗵𝗲𝗶𝗿 𝗲𝗻𝘁𝗶𝗿𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝘂𝗿𝘃𝗲..... #AIinInsurance #Insurance #ArtificialIntelligence #Innovation

But what if insurance worked more like Netflix? Netflix tracks your viewing behavior and adapts recommendations instantly. If insurance products adapting the same way, premiums adjusting dynamically to fitness levels, coverage expanding with life stages, benefits rebalancing as goals evolve. McKinsey estimates AI-led personalization could lift insurer revenues by 10–15%, while lowering claims costs through early risk detection. And The technology already exists. Wearables generate 250+ daily data points per user around heart rate, sleep, activity. PwC reports 63% of consumers are willing to share health data if it results in cheaper or more personalized premiums. And Personlaized premiums is not a distant reality. It can be achieved by: 𝟏. 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 that allow secure ingestion of health and behavioral data at scale. 𝟐. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐬𝐚𝐧𝐝𝐛𝐨𝐱𝐞𝐬 that encourage innovation while protecting privacy. 𝟑. 𝐀𝐈 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 to ensure transparent pricing and avoid hidden bias. 𝟒. 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬 with health-tech, fintech, and wellness players to broaden value delivery. Insurance is likely evolve from a once-in-a-decade purchase to a living product. #DigitalIndia #Fintech #AI #technology #Fintech #AI #technology

AI is transforming insurance! Here’s how: Generative AI is revolutionizing predictions. With 34% of insurers finding it most effective in predictive analytics and which in turn now enables better demand analysis, ensures companies are prepared for market changes. Automated customer advice is another game-changer. Personalized experiences are now possible, enhancing customer satisfaction and loyalty. Natural language processing (NLP) and voice recognition improve underwriting processes, making them faster and more accurate. Fraud detection has seen significant advancements with AI-driven image recognition. This technology helps identify suspicious activities quickly, reducing financial losses and enhancing security. Productivity has notably increased in countries like Germany, Spain, and Austria. A 0.5% boost in productivity can lead to a 1% decrease in labor costs. This is crucial as the EU-27 workforce is expected to shrink by 20% by 2050 due to an aging population. Contrary to popular belief, AI is not a job killer. Allianz Research shows AI is more likely to boost productivity and skills rather than cause mass job losses. AI can help address labor shortages and aging workforce challenges. AI in insurance is about balancing innovation with regulation. It’s about leveraging AI’s benefits while addressing concerns. The goal is to enhance efficiency, improve customer experiences, and maintain robust security. If you’re in the insurance sector and want to harness the power of AI, let’s talk. Our team at CellStrat is here to help you navigate this transformation and solve your unique challenges. Reach out to us today for a consultation!

Insurers: EIOPA just dropped a clear AI rulebook. Keep models fair, use clean data, make them explainable, keep a human in charge, and protect them from hacks. Right size controls to the risk. If you use AI in pricing, claims, fraud, or underwriting, follow this shortlist: ✅map every AI use and owner; ✅test for bias before and after launch; ✅document purpose, data sources, and limits; ✅require human checks on big calls; ✅watch drift and quality; ✅lock down vendors and access. This Opinion turns guesswork into a clear playbook. Build to it so your AI stays fair, explainable, human in charge, and secure.

🚨 Hot off the press! 🚨 I’m honored to be featured in Modern Insurance Magazine – Issue 72 📰 with my article: “AI: Promise and Peril – How Insurance Leaders Can Harness the Power of Agentic AI and MARL Without Losing Control” 🧠⚖️🤖 🎯 In this piece, I explore how AI Agents and Multi-Agent Reinforcement Learning (MARL) are rapidly evolving from experimental concepts to enterprise-grade tools poised to reshape the insurance value chain. 🏗️ From automating claims triage to deploying self-learning fraud detection systems and optimizing underwriting in real-time, I break down how insurers can: ✅ Leverage Agentic AI to make smarter, faster decisions ✅ Deploy MARL-powered systems to dynamically adapt across complex processes ✅ Avoid ethical, regulatory, and operational pitfalls through robust AI governance and simulation platforms 💥 The article also outlines the 4 key pillars insurers need to master as they embrace intelligent automation at scale: 1️⃣ Intentional Architecture – Why point solutions aren’t enough anymore 2️⃣ Transparent Orchestration – The need for explainable, observable AI workflows 3️⃣ AI Governance at the Core – Managing risk, bias, and accountability 4️⃣ Business-Led Innovation – Enabling underwriters, claims leaders, and operations to safely experiment with AI Agents without waiting for IT 🔄 I also challenge the industry to move beyond narrow automation and begin simulating multi-agent business ecosystems that evolve, learn, and optimize autonomously. 👁🗨 Think of this as a call to action: Insurance firms must embrace a future where AI doesn’t just support humans—it collaborates, learns, and scales alongside them. 🤝🧠⚙️ I’m deeply grateful to be featured alongside a brilliant group of industry experts and innovators who are each transforming their corner of the insurance world: Katie King, MBA, David Alexander Eristavi Costas Christoforou, PhD, Darren Hall, Will Prest MBCS Lior Koskas Tracey Sherrard Jason Brice Simon Downing Mia Constable Nik Ellis Jane Pocock♻️🚙 Greg Laker – your perspectives on data, automation, ethics, claims, and the customer experience added incredible depth to this edition 🙌 🔗 If you’re an executive, innovator, or transformation leader in the insurance space, this one’s for you. Let’s shape the future of insurance—intelligent, adaptive, and human-centered. 👉 Contact me for more information about leveraging AI Agents in the Insurance Industry 🚀 #AI #Insurance #AIagents #MARL #AgenticAI #InsurTech #ClaimsAutomation #Underwriting #DigitalTransformation #FraudDetection #CX #ModernInsurance #ThoughtLeadership #ResponsibleAI #PX42AI #SimulationFirst #NoCodeAI #Governance

"This paper focuses on developing a conceptual blueprint for AI insurance that addresses unintended outcomes resulting directly from an AI system's normal operation, where outputs fall within the declared scope but diverge from intended behaviour. Such failures are already silently embedded in existing insurance portfolios, neither affirmatively covered nor excluded, and thus remain unpriced and unmanaged. We argue that dedicated AI insurance is necessary to quantify, price, and transfer these risks, while simultaneously embedding market-based incentives for safer and more secure AI deployment. The paper makes four contributions. First, we identify the core underwriting challenges, including the lack of historical loss data, the dynamic nature of model behaviour, and the systemic potential for correlated failures, and propose mechanisms for risk transfer and pricing, such as parametric triggers, usage-based coverage, and bonus-malus schemes. Second, we examine market structures that may shape the development of AI insurance and highlight technical enablers that support the quantification and pricing of AI risk. Third, we examine the interplay between insurance, AI model risk management, and assurance. We argue that without insurance, assurance services risk becoming box-ticking exercises, whereas underwriters, who directly bear the cost of claims, have strong incentives to demand rigorous testing, monitoring, and validation. In this way, insurers can act as guardians of effective AI governance, shaping standards for risk management and incentivising trustworthy deployment. Finally, we relate AI insurance to adjacent coverage lines, such as cyber and technology errors and omissions." Lukasz Szpruch Agni Orfanoudaki Carsten Maple Matthew Wicker Yoshua Bengio Kwok Yan Lam Marcin Detyniecki AXA