Editing AI for Insurance Underwriting (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Insurance AI Application ROI
! Application !! Typical Improvement !! Key Data Source !! Regulatory Challenge
|-
| UBI (auto telematics) || 15-30% better loss ratio || OBD/smartphone || FCRA, state insurance regs
|-
| Property imagery underwriting || 10-20% fewer surprises || Aerial/satellite || State unfair discrimination laws
|-
| Accelerated life underwriting || 60-80% fluidless approval || EHR, Rx data || FCRA, MIB data use
|-
| Claims severity prediction || 10-20% reserve accuracy || Claims history || None (internal use)
|-
| Fraud detection || 20-40% more fraud caught || Claims + external || FCRA if adverse action
|}

'''Failure modes''': Proxy discrimination — telematics variables correlating with race/income can produce disparate pricing by protected class. Model opacity preventing regulatory review. Data quality — telematics device failures, GPS spoofing, gaps in data. Adverse selection in opt-in UBI — only low-risk drivers opt in, creating selection bias. Catastrophe model failure — novel climate patterns outside historical CAT model training distribution.
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