What it is
AI triages claims, extracts data from documents, and reduces cycle time.
Business problem
Claims teams drown in manual intake, document processing, and routing—creating long cycle times and inconsistent CX.
How the solution works
Use OCR + document AI to extract fields, validate policy data, and auto-route to adjusters with priority scoring; integrate with core claims systems.
Typical workflow
1) Ingest & normalize data (APIs/files/forms)
2) Extract/transform key fields
3) Apply rules/models with guardrails & exceptions
4) Write back to systems and notify stakeholders
5) Monitor quality and iterate
Expected outcomes
Claims 30% faster, reduced costs.
Key success metrics (to validate)
- Claims handled 30% faster
- Manual errors reduced 20%
- CSAT improved 15%
Data & integrations
e.g., Policy/Claims Admin (e.g., PAS/claims system), DMS/e-signature, Data Warehouse/BI
Risks & controls
- Data privacy/PII minimization & redaction
- Explainability, audit logs, versioning & rollback
- Model drift monitoring; thresholds for human review
- Experiment governance (holdouts, approval gates)
Implementation path (SprintOps)
- Audit & opportunity mapping (2–4 wks): data readiness + KPI baselines
- Pilot (4–8 wks): narrow scope, measurable KPIs, human-in-the-loop
- Scale (ongoing): expand coverage, harden integrations/MLOps, governance
ROI (example, static)
Inputs to capture: volume, time saved per unit, loaded $/hr, monthly cost
Illustrative formula: Savings = Volume × Time Saved × $/hr; Net ROI = Savings − Cost
FAQ
- Will this replace people? It removes low-value tasks; staff focus on exceptions and customer impact.
- How do you ensure accuracy/governance? Sampling QA, audit logs, change control, and fallbacks.
- How long does a pilot take? Typically 2–8 weeks depending on scope and data readiness.
