Structured claims from unstructured chaos
Speech-to-structure and document AI cutting FNOL cycle time 42% with 99.2% field completeness.
Claims arrive in every format from every channel; adjusters waste hours just normalizing the intake.
The Challenge
A regional P&C carrier received FNOL through email, portal, phone, and agent channels with no consistent structure. Adjusters spent 35% of their time normalizing loss details before they could begin coverage analysis. Missing fields triggered rework cycles averaging 2.3 touches per claim. Severity and peril classification varied by handler, making downstream triage inconsistent and reporting unreliable.
The Innovoco Solution
We deployed document and speech-to-structure models with business rules for peril classification, coverage hints, and severity scoring—feeding core and CMS systems with validated JSON and confidence scores. Human review queues handle low-confidence extractions and edge-case perils.
Phase 1 — Channel normalization and extraction
Built intake pipelines for each channel: speech-to-text for phone FNOL, OCR and NLP for emailed documents, and structured parsing for portal and agent submissions. Unified output schema with field-level confidence scores and source traceability.
Phase 2 — Triage automation and quality monitoring
Added peril classification, severity scoring, and handler routing rules. Adjusters review a configurable sample; extraction accuracy is tracked by field and channel with weekly quality reports driving model tuning.
Key implementations
Multi-channel intake
Phone, email, portal, and agent submissions normalized into a single structured schema with channel-specific extraction models.
Field-level confidence scoring
Every extracted field carries a confidence score; low-confidence fields route to human review queues with source context for rapid verification.
Peril and severity classification
AI models classify peril type and estimate severity from loss descriptions, enabling automated routing to specialized handlers.
Core and CMS integration
Validated JSON payloads create claims, attach documents, and populate exposure records in Guidewire, Duck Creek, and custom core systems via API.
Quality monitoring dashboard
Weekly accuracy reports by field, channel, and peril type with drill-down to individual extraction decisions and reviewer overrides.
Technical Innovation
A unified extraction schema decouples channel-specific intake from downstream business rules—so adding a new channel (e.g., chat, mobile app) requires only an intake adapter, not changes to triage or routing logic. Confidence-tiered review ensures human effort scales with uncertainty, not volume.
Impact
- 42% reduction in FNOL cycle time from submission to adjuster assignment.
- 28% reduction in manual triage effort through automated peril and severity classification.
- 99.2% field completeness on auto-processed claims, eliminating most rework touches.
- Consistent triage decisions across handlers and channels, improving downstream reporting accuracy.
Adjusters start coverage analysis minutes after FNOL—not hours—with structured, complete claim records that reduce rework and support faster, fairer settlements.
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