How to surface relevant data in insurance workflows

What’s the point of a massive context window if using over 5% of it causes the model to melt down? Bigger windows are great for demos. They crumble in production. When we stuff prompts with pages of maybe-relevant text and hope for the best, we pay in three ways: 1️⃣ Quality: attention gets diluted, and the model hedges, contradicts, or hallucinates. 2️⃣ Latency & cost: every extra token slows you down, and costs rise rapidly. 3️⃣ Governance: no provenance, no trust, no way to debug and resolve issues. A better approach is a knowledge graph + GraphRAG pipeline that feeds the model the most relevant data with context instead of all the things it might need with no top-level organization. ✅ How it works at a high level: Model your world: extract entities (people, products, accounts, APIs) and typed relationships (owns, depends on, complies with) from docs, code, tickets, CRM, and wikis. GraphRAG retrieval: traverse the graph to pull a minimal subgraph with facts, paths, and citations, directly tied to the question. Compact context, rich signal: summarize those nodes and edges with provenance, then prompt. The model reasons over structure instead of slogging through sludge. Closed loop: capture new facts from interactions and update the graph so the system gets sharper over time. ✅ A 30-day path to validate it for your use cases: Week 1: define a lightweight ontology for 10–15 core entities/relations built around a high-value workflow. Week 2: build extractors (rules + LLMs) and load into a graph store. Week 3: wire GraphRAG (graph traversal → summarization → prompt). Week 4: run head-to-head tasks against your current RAG; compare accuracy, tokens, latency, and provenance coverage. Large context windows drive cool headlines and demos. Knowledge graphs + GraphRAG work in production, even for customer-facing use cases.

The best AI for insurance is built for reality. It’s not about isolated intelligence. It’s about building smart systems that fit into how decisions are actually made. Here’s what I’ve learned while building intelligent systems for claims processing, particularly in healthcare insurance: Classification makes a difference. When a claim runs over 20 pages, organizing it into structured buckets—clinical, financial, contextual (we use 33 categories)—dramatically cuts search and review time. Visual triage speeds things up. A document looks like what it is. A CNN model can recognize SSN cards, payment summaries, or handwritten notes instantly, helping teams sort, route, and prioritize with greater speed. Context isn’t optional. Using domain-specific keyword models to extract terms like “oncology” or “chemotherapy” brings the right insights to the surface, enabling faster, more confident decisions. The takeaway? AI is most effective when it respects the workflow, reduces friction, and gives every user, from nurse to analyst, a faster path to clarity. Efficiency isn’t the result of AI. It’s the result of AI designed with intent. #AI #Insurance #WorkflowIntelligence #DesigningForClarity

𝐈𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐫𝐢𝐩𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 1/3 🡪 Claims processing In most insurance organizations, the front-end of claims processing is the slowest, least structured part of the value chain. • Incoming claims often arrive with incomplete or unstructured documentation. • Teams spend hours manually reading, classifying, and routing cases. • This introduces delays, inconsistencies, and operational inefficiencies. Contrary to popular belief, the most valuable AI transformation in this workflow isn’t in final approval or fraud detection. It’s in the document intake and triage process. 1️⃣ 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐈𝐧𝐭𝐚𝐤𝐞 AI-powered document processing enables insurers to: ➢ Classify unstructured documents like scanned PDFs, discharge summaries, and invoices ➢ Extract relevant data points (e.g., treatment date, policy number, diagnosis, claimed amount) ➢ Flag missing or duplicate documents based on claim type or policy rules The result is faster, more accurate claim setup and reduced manual effort at intake. 2️⃣ 𝐂𝐥𝐚𝐢𝐦𝐬 𝐓𝐫𝐢𝐚𝐠𝐞 AI models trained on historical claim outcomes can: ➢ Score claims for complexity, urgency, and potential risk ➢ Route low-risk claims for straight-through processing ➢ Prioritize complex or inconsistent cases for manual intervention This improves team efficiency, reduces bottlenecks, and speeds up processing time, especially for straightforward claims. ----------------------------- In one recent engagement at : • Automating document intake and triage reduced overall processing time by 40% • Human teams shifted from manual reviews to exception handling • The insurer was able to increase first-time-right submissions and improve turnaround times for clean claims For insurers serious about using AI to improve operations, claims triage and document intake are the right places to start. These processes have clear data inputs, defined decision points, and a measurable impact on cycle time, cost, and customer satisfaction. #insurance #aiforinsurance #insurtech