Claims Triage & FNOL Automation for a Tier-1 Carrier
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
AI-assisted FNOL intake, vision-based damage estimation, and claims-adjuster copilot deployed across auto and property lines.
−46%
Claim cycle time
31%
Straight-through processing
+22 pts
Claimant NPS
The bottleneck
Average auto claim took 14 days to settle. Straight-through processing sat at 9%, and adjuster attrition drove reliance on contractors at 1.7× cost.
The system
- Voice + web FNOL agent that captures structured claim data on first contact
- Vision model estimating vehicle damage from photos with adjuster override
- Adjuster copilot summarizing policy, prior claims, and coverage nuances
- Fraud signal layer routing suspect claims to SIU before payout
Outcomes measured, not promised
$74M
Annualized LAE savings
4.2×
Adjuster capacity uplift
97%
Photo-estimate agreement w/ adjuster
0
Consumer-protection findings
What it cost, what it returned
Investment
$7.1M program (year one)
Payback
11 months
Annualized value
$74M loss adjustment expense reduction
Modeled from actuarial LAE baseline; validated by CFO office.
How it is built
- Omnichannel FNOL intake (voice, web, mobile)
- Damage-estimation vision service with confidence-gated auto-payout
- Adjuster copilot with RAG over policy language and jurisdictional rules
- SIU routing with explainable fraud signals
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
30-minute working session
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