NLP analyzes notes to route high-severity claims instantly
Problem Statement
High-cost claims identified too late increase reserve volatility and duration. FNOL, notes, and medical data are siloed, NLP on adjuster notes is absent, and late severity signals create surprises.
What's holding you back
Poor NLP accuracy on unstructured notes
No clinician workflow integration
Ignoring false positives
Static models not retrained
Junior adjusters miss litigation signals
What success looks like
Claim duration decreases 20%
Litigation rate decreases
Paid-incurred ratio improves (approx. 15 %)
Closure rate increases
Catastrophic claims escalated immediately
How evolv helps
Applies NLP pipeline to adjuster notes and medical data surfacing litigation risk at intake
Embeds scoring in clinician workflows with clear escalation protocols and continuous retraining
Implements model governance ensuring explainability and falsepositive management