A regional P&C insurer handling 600,000+ annual claims across auto, home, and commercial lines - operating across the US and Canada under state DOI and NAIC data-handling requirements - needed to replace a manual intake operation that had never been automated. Every submission was read, sorted, and routed by hand. Document validation was checklist-driven. SLA breaches averaged 38%. The requirement: one agentic pipeline processing 30,000–50,000 claims per month without adding headcount.
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Faster claim processing
Reduction in manual workload
SLA adherence rate
Every claim landed in a shared queue where specialists routed it by hand - generating 18–24 hour backlogs from arrival. Because document validation only happened after routing, 31% of claims were missing required paperwork by the time an adjuster picked them up, sending work back through the queue. With 800 staff and no unified platform, there was no shared view of SLA status, queue depth, or case history. High-priority claims sat alongside routine ones with no escalation mechanism.
• No automated intake - every submission is manually read and categorised
• 31% of claims missing required documents, discovered only after routing
• No shared visibility across adjusters, team leads, and compliance teams
• 38% SLA breach rate with no escalation mechanism
Industry: Insurance (Property & Casualty)
Services: AI Services
Region: North America (USA & Canada)
An omnichannel ingestion layer accepts claims from web, mobile, email, and EDI feeds, normalises each submission into a structured object, and passes it immediately to the document validation agent - bypassing the human queue entirely.
Amazon Textract and Azure Form Recognizer extract and cross-reference every field at intake. Missing documents trigger automated claimant notifications before the claim reaches an adjuster - eliminating the loop of discovering missing paperwork after routing has already happened. This is the mechanism behind the 58% reduction in missing-doc resolution time.
A fine-tuned GPT-4o engine classifies each claim across 14 types and 3 severity tiers at 94.7% accuracy. The routing agent ingests real-time adjuster workload and live SLA clocks to compute optimal assignment. A separate escalation engine runs every 15 minutes: any case within 20% of its SLA threshold is proactively rerouted before breach occurs. That single mechanism is why SLA adherence rose from 62% to 91%.
A LightGBM fraud signal model scores document confidence and anomaly patterns on every submission, flagging suspicious cases for senior review. In the first 90 days, 214 claims were flagged - an estimated $680K in prevented losses. Real-time Power BI dashboards gave operations leadership a unified view of queue depth, SLA status, and throughput for the first time.
Adjusters reclaimed time for coverage decisions because intake, document checking, and routing were no longer their problem. The 15-minute SLA re-routing check meant at-risk cases were caught before breach - not escalated after. During a Q3 catastrophe event, claim volume spiked well above the normal monthly range; the platform scaled on AWS Lambda without infrastructure changes or added headcount.
• Every claim document validated at entry - no missing paperwork delays after routing
• 800 adjusters and intake staff on one unified platform for the first time
• 214 fraudulent claims flagged in 90 days - $680K in prevented losses
• Full audit trail for state DOI and NAIC compliance
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