Claims Automation with Explainable AI for a P&C Insurer
Regional P&C insurer, Southeast US
01 / Challenge
Claims processing averaged 18 days per property claim, with a team of 34 adjusters handling 14,000 claims annually. State regulators had put the insurer on notice for claims handling delays. Three prior automation proposals had been rejected by the legal department over explainability concerns — if a claim decision was disputed, they needed a defensible audit trail.
02 / Approach
QuettaMinds designed a claims triage and decision support system with explainability at its core. The system auto-adjudicates straightforward claims (under $15K, no coverage disputes) using a model that generates a plain-language decision rationale for every determination. Complex and disputed claims route to adjusters with pre-populated analysis. All decisions include a complete evidence chain.
03 / Outcome
Average claim processing time fell from 18 days to 6 days. Auto-adjudication rate reached 52% of submitted claims. Legal approved the explainability framework in 4 weeks — the fastest legal approval for any technology initiative in the company's history. State regulatory notice was formally lifted 90 days after go-live.
Representative case study illustrating common agentic-AI deployment patterns in Insurance; not a specific QuettaMinds client engagement.
Related Case Studies
Underwriting Decision Support System for a Commercial Lines Insurer
Underwriter data gathering time fell from 65% to 18% of working hours. Three loss-ratio-challenged segments were identified and repriced, producing a 6.2 combined ratio point improvement within two underwriting cycles. Premium volume per underwriter increased 34% with no additional headcount.
Read case study →Actuarial Data Governance and Model Documentation Platform
All 180 models now have current documentation in a searchable inventory. Regulatory documentation requests are fulfilled within 48 hours instead of the prior 3–6 weeks. Three state rate filings were approved on first submission after documentation quality improved — the prior average was 2.1 rounds of regulatory questions.
Read case study →Fraud Detection Agent with Complete Audit Trail
SIU referral volume increased from 400 to 890 per month while investigator headcount remained flat — the additional referrals were higher quality cases from the automated scoring. Confirmed fraud payments identified and denied in the first year totaled $6.8M. Regulatory examination of the fraud detection program found the audit trail to be exemplary.
Read case study →Next Step
Ready to write your production story?
We understand the compliance context. Let's talk about where AI creates leverage in your specific situation.