AI Claims Intake — L1 voice/text bot

An AI voice/text bot that handles incoming claims at L1: transcribes the claim, classifies severity and category, captures required structured fields (customer ID, transaction reference, timeline), and files the formal ticket in the case-management system (Jira / Zendesk / a legacy case-management tool). Surfaces compliance-relevant claims to a human reviewer within the SLA window.

Claims volume grows fast (a payments group saw 182% YoY growth → 16 claims/day) and each claim is regulatorily-bounded. A claims intake bot avoids low-single-digit-millions/year of L1 intake work and ensures structured-field capture for every claim — which is the bottleneck for downstream root-cause analysis and refund automation. The intake step is also where compliance failures (missed SLAs, missing fields) most often happen — automating it improves audit posture, not just cost.

1. Define the structured fields every claim must capture (customer ID, transaction ref, claim category, severity, timeline, evidence pointer).
2. Stand up the bot on whichever channel claims arrive (voice for retail, text for B2B).
3. Bot transcribes → asks targeted questions to fill each required field → confirms back to customer → files in case-management.
4. Add a severity classifier — anything financial >$X or compliance-tagged escalates immediately to human within 1 hour.
5. Connect to the case-management API (Jira or equivalent) so tickets land with structured fields populated, not free-text.
6. Measure: % claims with complete fields at intake (should jump from 40% manual baseline to 90%+).
7. Quarterly: review the 10% that needed human intake — fold patterns back into the bot.

• Compliance review of the bot script is non-optional. A claims-intake bot that omits a required field or fails to disclose recording can create regulatory exposure. Legal sign-off before launch, not after.