> ## Documentation Index > Fetch the complete documentation index at: https://docs.wayak.io/llms.txt > Use this file to discover all available pages before exploring further. # Insurance > Automate claims intake, estimate reserves, and detect fraud patterns with AI agents Insurance operations handle high volumes of claims under strict compliance and regulatory requirements. Every claim involves document extraction, policy verification, reserve calculation, and fraud screening — tasks that require pulling data from multiple systems, cross-referencing it, and making time-sensitive decisions. Manual processing is slow, error-prone, and expensive. A missed fraud indicator or a delayed reserve estimate can cost the organization thousands per claim. Wayak connects your claims management system, policy database, and document repositories into a unified data layer. Agents extract information from uploaded documents, verify coverage, estimate reserves from historical data, and flag suspicious claims — all while keeping humans in the loop for final decisions. Playbooks automate the intake pipeline, so new claims are classified and routed within minutes instead of hours. ## Use cases Extract policy numbers, loss details, and supporting documents from submissions and route claims to the right handler automatically. Calculate initial reserve estimates based on comparable historical claims using statistical analysis. Score claims against known fraud indicators and flag suspicious submissions for investigation. Identify and categorize uploaded documents — FNOL forms, police reports, medical bills, repair estimates — automatically. Cross-reference claim details against active policy terms to confirm coverage before processing. ## Platform capabilities used | Capability | How it's used | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Data sources | Claims management system (claim records, claimant history, payment data), policy administration system (active policies, endorsements, exclusions), general ledger (reserve postings) | | Knowledge spaces | Uploaded claim documents (FNOL forms, police reports, medical bills, repair estimates), policy documents, underwriting guidelines, fraud indicator reference materials | | Semantic layer | Objects for claims, policies, and claimants. Metrics for reserve amount, fraud risk score, average payout, and cycle time. Dimensions for claim type, severity, region, and status | | Agents | Claims Intake Analyst for document extraction and routing. Fraud Detection Analyst for risk scoring and investigation recommendations | | Playbooks | Event-triggered claims intake pipeline, scheduled fraud scans, reserve recalculation workflows |