GenAI struggles with domain specific data in life insurance

Another one straight from ChatGPT -- PART 2 -- see prior post for points 1-8 Why should CCOs use https://www.ai.claims/? 9) Competitive pressure is accelerating Many carriers and insurtech platforms are already deploying AI for FNOL, adjudication, fraud detection, reserve analysis, and workflow automation. CCOs increasingly risk operational disadvantage if they rely entirely on manual claims handling. The strongest strategic argument for a CCO is probably this: AI.Claims aims to let experienced claims professionals spend more time making judgment calls and less time digging through documents. That’s where most carriers see the ROI: -lower cycle times, -more consistent file quality, -improved supervisory oversight, -better litigation readiness, -and increased adjuster capacity without proportional hiring growth. At the same time, the industry is cautious about over-automation. Community discussions around AI in claims repeatedly emphasize that AI works best for routine analysis and triage, while humans should still own nuanced liability, settlement, and escalation decisions.

The NAIC pilot is proof: Insurance regulators are moving from "principles" to "examinations." 12 states are currently using the new Evaluation Tool to gather data on how insurers actually govern their AI models in real-time. Source: https://lnkd.in/gkxfXSgv The number one thing they are looking for? Fairness and transparency in claims decisions. If your AI is handling "consequential decisions" without a transparent audit trail, you're in the crosshairs. I'm helping our insurance clients get ahead of the pilot before it becomes the national standard. #ClaimsInnovation #InsuranceTech #RiskMitigation #NAIC2026 #Callvu

Current underwriting practices in the life insurance industry still rely heavily on the life assured’s photograph as one of the important risk assessment indicators. In many cases, only when the image visibly suggests obesity or other noticeable health concerns does the customer get asked to undergo additional medicals or deeper underwriting scrutiny. Now imagine the impact AI-generated or AI-altered images can have on such processes. Last week, Google announced at its I/O developer conference that it is expanding SynthID digital watermarking and content verification tools into Google Search and the Chrome browser. OpenAI has also launched a public verification tool where users can upload images to check whether they contain C2PA metadata or SynthID watermark signals. https://lnkd.in/gNHk7iWm This opens up an interesting possibility for enterprise underwriting teams: Before relying on customer photographs for risk assessment, underwriters may eventually be able to verify whether submitted images show signs of AI generation or manipulation. Of course, this is still evolving and not foolproof. Metadata can be stripped, screenshots can bypass signals, and open-source models may not follow the same standards. But clearly, the industry has already entered a new era where: AI creates the problem. AI detects the problem. AI solves the problem. Earlier, underwriters worried whether the customer had hidden health conditions. Now they may also need to worry whether the customer’s photo has hidden AI modifications - like the image below. 𝘋𝘰𝘯 𝘬𝘰 𝘱𝘢𝘬𝘢𝘥𝘯𝘢 𝘮𝘶𝘴𝘩𝘬𝘪𝘭 𝘩𝘪 𝘯𝘢𝘩𝘪... 𝘈𝘐 𝘪𝘮𝘢𝘨𝘦 𝘬𝘰 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘬𝘢𝘳𝘯𝘢 𝘣𝘩𝘪 𝘮𝘶𝘴𝘩𝘬𝘪𝘭 𝘩𝘰 𝘨𝘢𝘺𝘢 𝘩𝘢𝘪. 😄 #ArtificialIntelligence #LifeInsurance #Underwriting #InsuranceInnovation #AIInInsurance #DigitalTrust #FraudDetection #InsurTech #RiskManagement #GenerativeAI #InsuranceTechnology #AIGovernance #FutureOfInsurance #C2PA #SynthID

As financial institutions and Fintechs increasingly integrate artificial intelligence into their business models and operations, it is crucial to continually evaluate the risks that AI presents to our industry, organizations, and clients. Insurance companies are recognizing the rise of AI adoption and are proactively taking steps to protect themselves from liability by implementing exclusions with minimal notice. Traditionally, banks have depended on standard cyber, commercial general liability (CGL), or technology errors and omissions (E&O) policies to address AI-related risks, even if these policies do not explicitly reference AI. However, as these traditional policies were not designed with AI in mind, the insurance market is undergoing a significant transformation. Major insurance providers, including Berkshire Hathaway, Chubb, AIG, and Berkley, have received regulatory approval to implement "absolute AI exclusions" in standard liability policies. These broad exclusions eliminate coverage for losses resulting from AI-generated content (such as hallucinations or code), system drift, or intellectual property disputes. To address this coverage gap, banks should explore new coverages and specific policy endorsements from insurers. For further insights, you can read more here: https://lnkd.in/gXSNVTUj

INSURANCE SERVICES OFFICE, INC. (ISO) just rolled out massive generative AI exclusions for commercial general liability policies. Insurers are aggressively limiting exposure to AI-generated content. New endorsements completely eliminate coverage for bodily injury, property damage, and advertising injury stemming from AI models or third-party AI tools. General liability no longer covers algorithmic errors. Every carrier will be scrutinizing AI usage during 2026 renewals. We track real deployments/case studies so you don’t waste time. 1K+ execs and operators already read it every week, it's FREE, no fluff, 3min/week. https://lnkd.in/gZNcdnVf hashtag#AIinInsurance hashtag#AIforInsurance hashtag#AIInsurance hashtag#InsuranceInnovation hashtag#Insurance hashtag#Insurtech hashtag#Insuretech hashtag#insurtechinsights

Insurance claims shouldn’t feel like a part-time job for your customers. For years, the "digital" insurance experience meant filling out a PDF and waiting 10 days for a response. That’s the old way. The new way? Agentic AI that doesn’t just chat: it executes. Modern insurance providers are ditching passive chatbots for production-ready AI agents: 🚀 Automated Claims: AI agents verify documents and trigger payout approvals in minutes. 💡 Frictionless Onboarding: Replace endless forms with a conversational journey that activates policies instantly. 🧠 Back-Office Modernization: Deploy agents to manage manual reconciliation and fraud detection. The result is a frictionless journey with massive speed-to-value. We help firms modernize and deploy these capabilities in just 60–90 days. Stop being a paperwork company. Start being a tech-first partner. Reach out to Chris Romano at cromano@bci-it.com or book a demo directly: https://lnkd.in/e4bs_Zvk https://lnkd.in/e3tJCEat

Zurich’s latest paper makes a compelling case: AI is pushing up against the limits of traditional insurability. Why? - liability becomes harder to pin down, - multi‑agent AI systems diffuse accountability, and - without clear measurement and attribution, effective insurance simply isn’t possible. It does advocate the 'human-in-the-loop' approach to using AI. I believe this is particularly important for the insurance industry as it is bound by FCA's Consumer Duty principles. https://lnkd.in/eXzMXpyn

A collaboration that brings together two great organizations for a Future-Ready Agentic AI Underwriting solution for Faster TAT that keeps Human Expertise at its CENTRE.

The insurance investors getting the most out of AI aren't smarter than the rest. They just have different permissions. Some context on why I have a view here. After stints at Lehman and Bridgewater, I spent seven years running a global macro strategy — using data science to extract signal from noise across rates, FX, and commodities. That track record ended up in the 99th percentile for macro strategy on eVestment. Code and data were the edge. I've brought that discipline into insurance AM, and I've been watching closely how AI is — and isn't — changing how our industry invests. Here's the pattern I keep seeing: Almost everyone uses a chat interface now. That's table stakes. The real bifurcation is happening one layer deeper — at the adoption of coding agents (tools like Cursor, Claude, Google's AI agents) that can actually touch data. The gap matters because an agent with proper permissions can do things a chat interface simply cannot: > Run and iterate on quantitative analysis > Clean and manipulate datasets > Model second-order effects numerically These aren't incremental. They're a different category of capability. The reason insurance companies have been slow is legitimate — PHI exposure, data integrity, and yes, the stories about agents modifying production systems are real. I'm not dismissing those concerns. But the right response isn't to wait. It's to sandbox properly. A virtual machine environment with controlled, limited data access lets you capture most of the capability with a fraction of the risk. The hard part isn't technical — it's the conversation with your IT security and risk teams. They'll have valid concerns. Your job as an investment leader is to make the case that staying on the sidelines is also a risk — one that compounds quietly, then shows up suddenly in your team's output relative to peers. IMO, the firms that solve the permissioning problem now will have a real edge in two years.