Claims intake met bestuurde document intelligence.
Extract gestructureerde data uit FNOL-documenten en correspondentie — route cases via policy-gated triage met volledige audit trail.
Pilot: Doelresultaten uit een actieve pilot — nog niet volledig in productie.
−40% target
Intake time
Policy-gated
Human review
100%
Audit coverage
De uitdaging
Claims intake teams process heterogeneous documents under strict data classification rules. Shadow AI tools bypass retention policies and create unauditable extractions.
Hoe het werkt
A governed copilot classifies incoming documents, extracts fields with confidence scores, and routes low-confidence items to human review. Every extraction and routing decision is logged with model, policy, and approver context.
Resultaten
- Faster first notice of loss processing with human checkpoints
- Row-level access to sensitive claimant data
- Exportable evidence for internal audit and regulator review
Capabilities
Document classification
FNOL, medical, and policy docs routed by type and sensitivity.
Confidence thresholds
Low-confidence extractions escalate to adjuster review.
Fraud signal surfacing
Pattern flags presented with evidence — no autonomous denial.
Retention policies
Data handling aligned to classification and jurisdiction.
Workflow
- 1
Receive
Ingest documents via secure channel with tenant isolation.
- 2
Extract
AI extracts fields; low-confidence items queue for review.
- 3
Triage
Policy gates route cases by severity, fraud signals, and coverage.
- 4
Assign
Approved cases export to claims management with audit pack.
Platform stack
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