Advanced intelligence for insurance carriers

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

Insurance industry doesn’t need more AI pilots. It needs real transformation. For too long, we’ve been celebrating proofs of concept while missing the bigger picture. Fact of the matter is that you can’t treat AI as a side project. It’s the engine to rewrite how insurance operates, competes, and grows. Those waiting for a perfect playbook are already behind. Let’s talk solutions. To lead, insurers must narrow their bets sharply. Focus where AI can tilt the playing field: For example: underwriting speed for P&C giants, claims cost reduction for health insurers, customer service reinvention for retail players. Insurers need to keep in mind that best don't do everything, they do the right things with obsession. For the players that have implemented one of the above mentioned solutions, impact is visible now. AI driven underwriting is improving efficiency by 36% and shaving 3 points off loss ratios. Smart customer service assistants are boosting productivity by 30%. Claims journeys are being reinvented, simple claims now settled in real time, operational costs slashed by half. In sales, AI is freeing brokers from admin burdens and giving direct writers an AI powered army to qualify and route leads. Even IT is moving from bottleneck to enabler, with AI halving cloud migration times and rewriting code faster and smarter. Is everything progressing smoothly? Not at all . Challanges still remain and implementation of AI remains more of a leadership test than a technological constraint. And in this era of smart implementation, success isn’t about who has better algorithms. It’s about who has the capability to reimagine people, processes, and priorities. Only 10% of success is tech itself; 70% is culture, skills, and bold execution. Insurers who invest now in reskilling, literacy, cross functional teams will dominate the next era. Those who wait will become case studies of missed opportunity. My final 2 cents .. AI will not replace your strategy. But it will brutally expose its weaknesses. And the time to move is not tomorrow. It’s already slipping away today. Refer attached report for detailed insights.⬇️ #Insurance #Insurtech #AIInInsurance #InsuranceInnovation #DigitalTransformation #FutureOfInsurance #LinkedIn

How Can We Be Sure the AI Isn’t Just Making Things Up? In insurance, AI systems can’t just sound intelligent—they have to be right. Especially when decisions involve regulatory requirements, financial outcomes, or customer trust. At Agentech, we built our platform to prioritize verifiable accuracy, not just speed or automation. Our strategy is rooted in traceability, control, and continuous validation. Evidence-Based Reasoning Outputs are grounded in your policy documents, claims files, and regulatory guidance—not generated out of thin air. Our retrieval-based approach avoids speculative or made-up responses. Click-to-Evidence Transparency Every recommendation can be traced back to the source clause, rule, or data point that informed it—so users never have to guess where it came from. Real-Time Confidence Monitoring When confidence in an answer falls below a set threshold, we don’t guess. The task is flagged or routed for human review. Pre-Deployment Scenario Testing Before any module goes live, it’s tested against real-world edge cases and claims data, reflecting the complexity of your workflows. Continuous Feedback Loop When users correct or flag outputs, that input feeds right back into the system—improving accuracy and reliability over time. This isn’t just smart AI. It’s AI you can hold accountable. #ExplainableAI #ClaimsProcessing #InsurtechSolutions #TrustworthyAI #RiskManagement #ComplianceTech #InsuranceExecutives

Insurance companies are betting big on AI to tackle shrinking margins and fierce competition. The promise is huge, but in practice most AI initiatives are often bogged down by a critical challenge: AI evaluation. Traditional software testing simply doesn't cut it for LLMs. How do you really know if your LLM-powered claims system is accurate, reliable, compliant, and unbiased at scale, especially in a highly regulated industry? Manually reviewing thousands of outputs is unfeasible, but so is the risk of LLM failures like hallucinations. That's where HoneyHive comes in. We've been working directly with the largest US insurance companies to transform LLM evaluation from a tedious, manual process into a data-driven, automated workflow. Here's how HoneyHive helps insurance companies conquer LLM evaluation: ✅ Data Governance & Compliance Ready: Centralize, version control, and enrich your logs for targeted and compliant evaluation. ✅ Seamless LLM Integration: Easily connect HoneyHive to your LLMs (like Azure OpenAI / Bedrock). ✅ Automated & Scalable Evaluation: Evaluate thousands of test cases efficiently with parallel processing and real-time performance tracking. ✅ Meaningful Metrics, Tailored to Insurance: Go beyond basic accuracy. Define custom metrics for conciseness, completeness, regulatory adherence, and agentic tool-use. ✅ Deep Observability & Traceability: Understand why your LLM performs a certain way. Inspect inputs and outputs, capture traces, and understand how your agent behaves in production. The Results? Faster iteration cycles, reduced deployment risk, improved compliance, and most importantly, unlocking the full potential of AI in insurance. ➡️ Check out our example cookbooks for building and evaluating claims processing agents in the comments.

🤔 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

CSIRO's Data61’s and the Insurance Council of Australia have released a comprehensive roadmap for responsible AI in general insurance. Five Priority AI Use Cases 1. Automated claims processing & triage: Faster and more accurate processing for high-volume, low-complexity claims, improving repair allocation and customer communication. 2. Fraud detection & prevention: Spot fraud patterns in claims, decrease investigation workload, and help reduce fraud-related costs. 3. Enhanced underwriting & risk assessment: Improves risk models by integrating new data sources, leading to more tailored, fair pricing and stronger risk awareness. 4. Natural disaster impact prediction & response: Applies predictive models and data to boost disaster warnings, claims activation, and supply chain effectiveness. 5. Operational control & compliance: Automates compliance monitoring, anomaly detection, and complaint handling, supporting timely and consistent actions. Key Guardrails 1. Human oversight: Human decisions remain central, especially for high-risk cases. 2. Transparency & explainability: Maintain records and clear explanations for automated actions. 3. Bias and fairness controls: Regularly test and exclude protected attributes from models. 4. Privacy & cybersecurity: Strong data security and regular audits are mandatory. 5. Regulatory compliance: Align with current laws, perform regular model reviews, and clearly disclose AI usage. #AI #Insurance https://lnkd.in/gsfptWzC

🚨 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

Can Insurance Employ AI That Is Both Powerful and Fair? Artificial intelligence is rapidly reshaping how insurance companies process claims, detect fraud, and manage risk. But to be effective and fair, AI must be developed and deployed with careful attention to data quality, model transparency, and ethical use. AI systems are only as good as the data they are trained on, and if that data is biased or incomplete, the outcomes will reflect and even amplify those problems. In a conversation filled with lived experience, John Standish⁠, Co-Founder and Chief Innovation and Compliance Officer at Charlee.ai, laid out a powerful and pragmatic vision for how artificial intelligence must be built for the insurance industry. Having transitioned from a long and substantial career in law enforcement and insurance fraud investigations to the world of InsurTech, John offers rare dual expertise: a regulator’s scrutiny and a technologist’s curiosity. His perspectives cut through hype and buzzwords and land squarely in the domain of real-world consequences, compliance, and human-centered innovation. John underscored the importance of domain-specific AI models that are trained with relevant, clean, and unbiased data. He cautioned against using generic models and stressed the need for explainability, transparency, and regulatory compliance in all AI-driven decisions. The conversation illuminated a crucial point: AI isn’t a magic fix for outdated processes—it’s a force multiplier for organizations willing to rethink their foundational data strategies and workflows. For the insurance industry, embracing this challenge is not just a matter of innovation, but of survival in a rapidly changing digital landscape. #technology #innovation #frauddetection #claimsmanagement #artificialintelligence #insurance #insurtech Look for the full YouTube episode in the comments.

The insurance industry has been promising revolutionary change since the early 2010s ⌛ Your smart home would know when a pipe was about to burst and shut off the water before you knew there was a problem. As you locked the front door, your insurance would seamlessly shift from home to motor, adjusting your premium in real time based on road conditions, your driving history, and the weather. Every conference presentation showed the same timeline: "3-5 years away." 2015 came and went. Then 2020. Now we're halfway through 2025, the "blue sky thinking" sessions have fizzled out, and the industry has learned to be more cautious with timelines. But the fundamental challenge remains: we're still not delivering the transformation the industry keeps promising. What's different this time? AI has reached the capability threshold needed to handle insurance's complex, unstructured data reality. 👉 5 insurance AI applications that I'm genuinely excited about: ↳ End-to-end claims automation - you crash at 3am, AI handles everything overnight, you wake up with repairs booked and money transferred ↳ Intelligent fraud detection - AI spots fake damage photos, synthetic identities, and coordinated fraud rings operating across multiple insurers ↳ AI broker assistants - AI agents that simultaneously negotiate with multiple insurers, optimising your renewal terms automatically ↳ Cross-carrier fraud networks - AI systems that share intelligence across the entire industry ↳ Zero-friction underwriting - AI pulls from hundreds of data sources to assess risk instantly without you filling out anything The reality today? Only 11% of UK insurers report successful AI outcomes. Over 50% of pilots stall because of data quality issues. The winners by 2030 won't be the companies with the most cutting-edge AI - they'll be the ones who make it work consistently. The gap between promise and reality is still enormous. But for the first time in years, I'm genuinely optimistic we might finally start to close it. Are you seeing real AI progress in your industry, or is it still mostly hype? 👇

Imagine filing an insurance claim and having it resolved in minutes—not weeks. That’s not science fiction; it’s the promise of AI in claims transformation. Over the next decade, we’re going to see a radical shift in how claims are handled, and it’s all thanks to AI-driven innovation. Here’s the kicker: AI won’t just make the process faster—it will make it smarter. Think about this: today, claims adjusters sift through mountains of paperwork, phone calls, and emails to process a claim. It’s tedious, time-consuming, and often frustrating for the customer. Now, picture this instead: AI scanning photos of a damaged car and generating an estimate for repairs in seconds. Natural language processing reading and analyzing claims reports to flag inconsistencies, reducing fraud. Chatbots walking customers through every step of the claims journey, ensuring no question is left unanswered. What does this mean for the industry? Claims professionals will spend less time on manual tasks and more time focusing on complex cases that need a human touch. Insurance carriers will reduce costs and improve accuracy, leading to better customer experiences. But this transformation isn’t just about technology; it’s about trust. AI will only work if customers believe in its fairness and accuracy. Insurers will need to be transparent—showing customers not just what decisions were made, but how and why AI made them. The next decade will redefine claims as we know it. The question is, are we ready to embrace this future? What’s your take—what excites or concerns you most about AI in claims?