AI types in insurance industry

During this year, we’ve seen mid-sized and large companies rush to “build agents” - skipping straight to the most hyped layer. Most begin with a quick automation and then, impatient, chase fully autonomous agents. That leap costs time, trust and money. There are three practical layers - each a different tradeoff between speed, control and capability. (A) Non-Agentic Workflows (where everyone should start) This is basic AI usage: User input → LLM processes the request → Output delivered. Great for narrow, well-structured tasks like- Summarising call transcripts into bullet-point action items Summarising product specs They’re quick to build, reliable, and inexpensive - but limited. B) Agentic Workflows: Example from a mid-size insurer that we worked with. Here, multiple systems/AI agents work together with some decision logic. You’re not just calling an LLM - you’re orchestrating steps. Goal: Cut insurance claim inquiry response time and reduce cost without adding headcount. The workflow + Agentic AI steps include: → Reads incoming claim requests → Retrieves policy and claimant data from internal systems → Checks claim status and required documentation → Generates an accurate, policy-compliant response → Escalates to humans only when risk or complexity flags trigger Impact: 38% of claims resolved end-to-end by the agentic layer 60% faster responses for claimants C) AI Agents (Not enterprise ready - for now) Here's the reality: Most "AI agents" are just fancy workflows with better marketing. Real agents should: Form a plan based on ambiguous goals Choose tools on the fly, not in a fixed sequence Learn from outcomes and adapt Escalate with clear reasoning We're certainly on a journey in that direction,, but the technology isn't quite there yet for most enterprise use cases (where process control is important) . Don't get caught up in the hype. Focus on building solid automation that actually reduces operational cost. Most companies wanna jump straight to "AI agents" and end up with broken, unreliable systems. Start simple. Build workflows that solve real problems. Then gradually add complexity. Srinivas K

🚨 The latest NAIC AI/ML Survey is out (Health Insurance) — and it's a must-read for anyone serious about responsible AI in insurance. 🚨 As a longtime follower of the NAIC’s evolving body of work on artificial intelligence and machine learning across insurance lines — Life, Auto, Homeowners, and now Health — I can say with confidence: this latest Health AI/ML Survey Report is one of the most comprehensive looks at how AI is actually being used in the real world. Key takeaways from the Health AI Survey: ✅ 84% of surveyed health insurers are actively using AI/ML ✅ AI is being applied in utilization management, prior auth, disease management, fraud detection, and sales experiences ✅ 92% of respondents have adopted AI governance aligned with NAIC AI Principles ✅ Companies are testing for bias, drift, equity, and integrating human oversight into AI decisions But here’s the bigger story: 💡 This is now the third major NAIC AI/ML survey, building on similar in-depth studies of Life, Auto and Homeowners insurers. That means we now have a multi-line, multi-sector view of how AI is shaping decisions across regulated insurance products — from pricing to fraud detection, from underwriting to claims. 🧭 For anyone in a regulated industry — whether in finance, insurance, healthcare or beyond — these surveys offer a blueprint: - How to govern AI responsibly - How to build transparency and oversight - How to align with emerging state and federal expectations - And how to balance innovation with consumer protection 🎯 My Key Message: Don’t just chase the hype around “agentic AI.” If you're building for the future — master the AI ML fundamentals first. Most of the real impact today is still being driven by traditional supervised and unsupervised machine learning — classification, prediction, anomaly detection, recommendation — not just autonomous agents. Understanding core AI/ML techniques, governance, and ethics is still the foundation..... What's next? The NAIC is exploring a model law/regulation on AI governance. Stakeholder input is being requested now — a critical moment to help shape the future of AI oversight. This is more than an insurance story. It’s a signal of what responsible AI regulation can (and should) look like. Full report link in the comments. #AIinInsurance #NAIC #ResponsibleAI #Insurance #Governance #InsurTech #MachineLearning #ConsumerProtection

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!

𝗔𝗜 𝗶𝗻 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗶𝘀𝗻’𝘁 𝗻𝗲𝘄. 𝗕𝘂𝘁 𝘁𝗵𝗲 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 𝗮𝗻𝗱 𝗽𝗮𝗰𝗲 𝗮𝗿𝗲. We’ve had AI in insurance for years: ✔️ Fraud detection ✔️ Risk scoring ✔️ Chatbots But something changed in 2023–2024: ➡️ Generative AI went mainstream ➡️ Foundation models became accessible ➡️ Carriers started actually piloting tools that work The result? We’re seeing underwriting times drop from weeks to minutes. Claims triage is being handled by AI agents. And startups are building predictive scoring models to help agents prioritize leads. This isn’t hype. It’s happening. But here’s the catch: AI won’t solve everything. It needs the right data, governance, and human oversight to work. The future of risk and risk related businesses is for the most part AI-assisted—not AI-replaced. Who's building in this space? Drop a 👋 below.

🚀 The Future of AI in Insurance: Why Agentic AI Changes Everything 🚀 The word “agent” used to mean a person selling a policy. Now, it means something far more powerful. In this issue of Insurance Amplified, I sat down with Kyle Geoghan, CEO & Co-Founder of Indemn, to unpack Agentic AI—what it is, why it’s misunderstood, and how it’s reshaping the very infrastructure of insurance. 🔍 Forget Everything You Think You Know About AI This isn’t about chatbots. This isn’t about simple workflow automation. Agentic AI is something entirely different: ✅ AI that operates with intent, not just rules ✅ AI that learns from your best people and executes in real-time ✅ AI that functions autonomously—even beyond traditional APIs 💡 Key Takeaways from Our Conversation ⚡ Agentic AI Works in Tiers 📌 Shallow Agentic AI – Automates structured workflows but doesn’t make decisions. 📌 Deep Agentic AI – Accepts a goal and autonomously works to accomplish it, adapting in motion. ⚡ APIs Aren’t Enough Anymore "APIs were built for form submissions, not for autonomous decision-making." – Kyle Geoghan Agentic AI doesn’t wait for human-triggered API calls—it proactively retrieves information, processes decisions, and interacts with other AI agents. ⚡ AI Agents Don’t Need Perfect Data "If you’re waiting for the industry to standardize data formats, you’re already behind." – Kyle Geoghan Instead of demanding clean, harmonized data, Agentic AI adapts in real time—extracting meaning from what’s available. ⚡ Stop Automating Broken Processes "Don’t repeat the RPA mistake. Start from first principles, not from what’s already broken." – Kyle Geoghan True AI transformation isn’t about speeding up outdated workflows—it’s about redefining them entirely. 🗣💬 Final Thought: AI in insurance isn’t just about efficiency—it’s about operating differently. The companies that embrace this shift will move faster, reduce costs, and redefine how insurance works from the inside out. 🎧 Listen to the full episode here: https://lnkd.in/eyvQJK5d 📩 Subscribe to Insurance Amplified to stay ahead of the AI revolution in insurance. 🔥 Thank you to IRYS Insurtech for sponsoring this season of Insurance Amplified! #InsuranceAmplified #AI #AgenticAI #Automation #Insurtech #DigitalTransformation

Get Smart – How AI is Driving Smart Auto Insurance Claims Management? AI is driving a major transformation in the auto insurance industry. With smart auto claims management, insurers and customers can now experience a fully automated, seamless workflow—from reporting an incident to final settlement. 𝗞𝗲𝘆 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • Faster claim processing • Improved operational efficiency • Greater transparency and customer engagement With the insurance industry undergoing a significant transformation driven by advanced technologies such as artificial intelligence (AI), the concept of smart auto claims management – a fully digital and automated workflow process – is transformational. Smart auto claims management harnesses AI to automate and optimize end-to-end claims management, from the First Notice of Loss (FNOL) to damage assessment, repairs, communication, and final settlement. The primary goal? To provide faster, more accurate, and less labor-intensive claims handling, thereby reducing the time it takes for customers to receive payouts, and enabling insurers to reduce costs and enhance customer service. A Fully Automated Digital Workflow: Smart claims management works by enabling a logical, step-by-step process, utilizing a fully automate digital workflow. Examples of key steps include: First Notice of Loss(FNOL). In a smart claims process, customers can instantly report an incident using a smartphone app that guides them through the process. AI-Powered Damage Assessment. Using AI and computer vision technologies, this reduces the need for human intervention, and allows faster decision-making, leading to better outcomes. Automated Communication and Customer Interaction. Automated communication tools keep customers informed at every step. Through AI-powered virtual assistants and automated notifications, customers receive real-time updates on their claim status. Repair Management and Supply Chain Optimization. This bridges any gaps in the process through a connected ‘ecosystem’ that links all stakeholders in a single digital platform. Final Settlement and Payment. The automation of settlement not only speeds up payment, but also reduces administrative overheads for insurers, as fewer manual processes are involved. Key Benefits for Insurers and Customers :   For Insurers: Operational Efficiency Faster Processing Times Fraud Prevention   For Customers: Convenience and Speed Transparency and Communication Personalized Experience