Underwriting and claims now run on AI. Your continuity plan should too
Carriers have embedded AI across the policy lifecycle: submission intake, underwriting assistance, claims triage, fraud detection, and customer service. These are volume businesses — when AI-assisted throughput drops, backlogs form in hours and loss-adjustment expenses climb.
Where AI is embedded
How insurance runs on AI today
- Submission intake and risk summarization
- Underwriting assistance and guideline search
- Claims triage and document extraction
- Fraud pattern detection
- Policyholder service assistants
Sector-specific risk
What makes resilience harder here
Catastrophe events are AI demand spikes
Claims AI is most critical exactly when volume surges — after a CAT event. That's also when API quotas exhaust and provider latency climbs. Quota resilience planning is CAT planning.
Cycle-time SLAs have thin margins
State-regulated claims handling deadlines don't pause for vendor outages. AI-RTOs for claims workflows need to be set against regulatory clocks, not IT convenience.
Model drift meets underwriting discipline
When a vendor retires or updates a model, underwriting assistance behavior shifts silently. Change management for model versions is an underwriting-integrity control, not an IT nicety.
The regulatory picture
NAIC model laws on claims handling timelines, state market-conduct exams, and growing AI governance expectations (NAIC AI Model Bulletin) all favor documented, tested AI continuity.
Assess your insurance AI estate
The AIR Assessment maps your AI dependencies against sector-specific failure modes and regulatory expectations — in 3 to 6 weeks.