On-Demand Claims Score: 3.95/5.0
On-Demand Knowledge Work | External audience
First Notice of Loss (FNOL) involves reactive intake, often long phone calls with policyholders, manual data entry, and multiple handoffs to different departments. Customer frustration is high; triage accuracy is 65-75%. Manual processes delay claims resolution from 30 days (average) to significantly longer for complex claims. Loss Adjustment Expenses (LAE) inflate due to inefficient workflows. Currently, claims staff spend 25-40 hours per claim on initial intake, assessment, and routing to appropriate handler.
Data Sources:
Data Classification:
Data Quality Requirements:
Voice transcription accuracy: 95%+ (medical and proper names must be verified). Photo analysis accuracy: 90%+ for damage severity classification. Policy data freshness: T+0 (real-time policy lookups). Repair estimate timeliness: within 24 hours of request. Medical records completeness: 100% if case involves injury. Telematics freshness: real-time. Weather data freshness: real-time.
Integration Complexity: High , Requires integration with voice/chat platform, computer vision API, Guidewire APIs (PolicyCenter and ClaimCenter), repair estimate databases, medical records systems (if applicable), telematics providers, weather feeds, police report APIs. Voice transcription and photo analysis require machine learning. STP logic requires policy-specific rule engines. Real-time orchestration across multiple systems.
| Criterion | Weight | Score (1-5) | Weighted |
|---|---|---|---|
| Time Recaptured | 15% | 4 | 0.60 |
| Error Reduction | 10% | 4 | 0.40 |
| Cost Avoidance | 10% | 4 | 0.40 |
| Strategic Leverage | 5% | 4 | 0.20 |
| Data Availability | 15% | 4 | 0.60 |
| Process Clarity | 15% | 4 | 0.60 |
| Ease of Implementation | 10% | 3 | 0.30 |
| Fallback Available | 10% | 4 | 0.40 |
| Audience (Int/Ext) | 10% | 4 | 0.40 |
| Composite | 100% | 3.95 |
Customer data and policy data are readily available (internal systems). FNOL process is standardized. Claims volume is high (10,000+ claims/month for mid-size insurer) = massive time savings. Computer vision for damage assessment is proven technology. Regulatory benefit: faster claim resolution improves customer satisfaction and reduces regulatory complaints. Fallback is straightforward: claims staff can manually intake if agent fails. External-facing but managed (phone/chat agent reduces customer friction). Clear ROI: reduce LAE by 20-30%; improve NPS by 15-20 points.
Sprint 0 (2 weeks) + 3 build sprints (6 weeks)
Sprint 0: FNOL workflow mapping, policy term extraction, damage assessment taxonomy, STP rule definition, voice/chat integration planning
Build Sprints 1-3: Voice transcription pipeline, photo analysis integration, Guidewire API integration, STP rule engine, claim summary template, adjuster workbench routing, monitoring dashboard, fallback and kill-switch testing
From zero to a governed, production agent in 6 weeks.
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