First-Contact Claims Triage Assistant

Insurance contact centers receive claims across phone, email and chat with inconsistent information, causing slow manual intake, frequent misrouting to specialists, and delayed decisions that raise costs and customer churn. Teams spend time on repetitive data entry and duplicate follow-ups while high-severity or potentially fraudulent cases can be missed until later in the process.

Deploy a hybrid NLU/LLM pipeline that ingests multi-channel inputs, extracts structured claim attributes, scores severity and fraud risk, and recommends next actions while keeping humans in control.

• Use NLU models and task-specific LLM prompts to extract claimant details, policy numbers, incident descriptions, and attachments (OCR for photos/documents).
• Apply a rules-and-ML scoring layer for severity, urgency, and fraud indicators; map scores to routing rules and SLA targets.
• Integrate with core claim systems and CRM to auto-populate intake fields and create tasks; surface AI recommendations in agent UI with transparent explanations.
• Human-in-the-loop governance: mandatory review for high-risk/low-confidence cases, audit logs, periodic calibration against outcomes, and access controls for PHI.

Teams typically see faster intake cycle times (20-50% reduction in first-contact handling time) and fewer misrouted claims (30-60% fewer escalations), yielding operational cost improvements of roughly 10-25% per claim processed. Customer experience improves through quicker acknowledgements and decisions, and the firm gains clearer auditability and lower error rates for regulators and internal risk teams.