Claims Support Designed for an Insurance Provider's Peak Seasons
The cost of volume pressure in claims support
High claim volumes can reduce intake quality. Rushed FNOL calls may miss critical details, slowing downstream claims processing.
Missing information often requires a follow-up call, adding time and rework. A surge-ready model must protect FNOL completeness under load, not simply answer calls faster.
The model behind the readiness
Simply Contact built the model around three things: staffing that flexes with volume, a process that protects data quality under pressure, and AI applied only where it truly earns its keep.
Peak-ready staffing
A cross-market pool of part-time agents is trained on claims handling year-round and ready to scale within 48 hours, avoiding a new hiring cycle for each volume spike.
Structured FNOL capture
Agents follow a standardized checklist covering policy, incident, damage, and contact details. Complete files are delivered without integration into the insurer’s intake system.
Claim status bot
A self-service AI bot handles routine claim status queries, absorbing a large share of peak-season volume and freeing agents to focus on FNOL calls that require human support.
Automated QA scoring
Every call is automatically scored against compliance and documentation criteria, replacing the client’s 5% manual sample with full coverage.
What changed once the model was in place
Average wait time during peak periods down from over 12 min
First-call FNOL completeness up from a 68% baseline
QA coverage up from a 5% manual sample, error rate under 2%
- Overtime spend during surge periods is down by roughly a third, replaced by flexible cross-market staffing that scales without a hiring cycle
- Claims processing backlog during peak periods is now cleared roughly 40% faster than under the prior staffing model
Process first, AI where it adds value
Peak-season resilience came from disciplined processes and a flexible staffing model.
AI was applied selectively: automating routine claim status queries and extending QA from limited manual sampling to full call coverage.
Staffing flexibility, structured FNOL intake, and regular reporting remained process-led and supported by trained agents.
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