AI agents for insurance.
The agent reads the file, drafts the decision, and shows its work. Your underwriter or adjuster still signs. Every approval leaves an audit trail, so speed never costs you control. Named carriers have already published what this looks like in production.
cut in underwriter review time per case from AI summarisation of long medical reports, across an 18-month, 1,000-case test. Aviva, Nov 2025.[1]
of underwriter time saved in a year by Allianz UK's generative AI guidance tool, across 13,000+ queries. Insurance Post, 2025.[2]
reduction in processing and settlement time for low-complexity claims from Allianz's agentic claims system, with payout decisions never automated. Allianz, Nov 2025.[3]
How every deployment is framed
Each use case below follows the same discipline: name the workflow, define what the agent does, define what a person approves, and measure the outcome against a baseline recorded before go-live. In a regulated business the audit trail is the quiet advantage: every agent action is logged, so every decision can be reconstructed.
The workflows agents take on
Submission and claims intake
- Workflow
- Turning broker packs, emails, and first-notice-of-loss documents into structured, routable cases.
- What the agent does
- Extracts and structures the data, checks completeness, runs appetite and coverage checks, and routes the case to the right desk.
- What the human approves
- A person confirms the routing decision and handles every exception before the case enters underwriting or claims.
- Measured outcome
- Routing accuracy and intake cycle time; Aviva reports a 30% improvement in routing accuracy from its claims AI programme.[4]
Underwriting document summarisation
- Workflow
- Reading long medical or commercial files before a risk can be priced.
- What the agent does
- Reads files that can run past 90 pages, filters the noise, and surfaces the relevant facts as a concise summary with links back to the source.
- What the human approves
- The underwriter makes the decision and sets the price; the summary only shortens the reading.
- Measured outcome
- Review time per case, roughly halved in Aviva's published test.[1]
Claims triage and settlement drafting
- Workflow
- Moving high-volume, low-value claims from queue to resolution.
- What the agent does
- Assembles the case (coverage check, event verification, fraud screen) and recommends a settlement.
- What the human approves
- The payout. Allianz's published position on its agentic claims system is that payout decisions are never automated; a claims professional holds final responsibility.[3]
- Measured outcome
- Processing and settlement time, cut 80% in Allianz's published deployment.[3]
Fraud flagging
- Workflow
- Screening every claim against fraud signals without slowing honest claimants.
- What the agent does
- Scores claims on multiple signals, assembles an evidence trail, and flags anomalies for investigation.
- What the human approves
- The escalation-to-investigation or clear-to-pay decision.
- Measured outcome
- Flag precision and investigation hit rate against the pre-deployment baseline; 35% of insurance executives rank fraud detection among their top generative AI priorities.[5]
Complex-claim assessment and routing
- Workflow
- Assessing complex liability claims and assigning them to the right specialist team.
- What the agent does
- Assesses the liability inputs, drafts the assessment, and routes the case.
- What the human approves
- The liability determination and the reserve.
- Measured outcome
- Assessment cycle time; Aviva's programme is reported to have cut complex-case liability assessment by 23 days and saved over £60M in 2024.[4]
Customer and policy servicing
- Workflow
- Answering coverage, endorsement, and claim-status questions from policyholders.
- What the agent does
- Answers from policy data, drafts endorsements, and escalates edge cases to a person.
- What the human approves
- Any policy change or bindable commitment before it takes effect.
- Measured outcome
- Resolution time and escalation rate against the pre-deployment baseline.
What end-to-end agentic automation creates
The published carrier results above share one pattern: the agent compresses the reading and assembly work, and the human keeps the decision. Run end to end on one platform, that pattern turns intake, underwriting, claims, and servicing into a single governed flow: one set of guardrails, one audit trail, one place a regulator or reinsurer can look to reconstruct any decision. The published numbers are carrier disclosures, not Mirai360 client results; they define the bar this architecture is built to meet.
Our platform reasoning is public: see agentic AI in insurance: intake, claims triage, and audit trails and why running agents in your own cloud matters.
One 30-minute call.
We look at your book and your queues, find the one workflow worth starting with, and tell you honestly whether it clears the bar.
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- Aviva plc newsroom, "Aviva to launch groundbreaking AI underwriting tool," 10 November 2025: aviva.com/newsroom/news-releases/2025/11/aviva-to-launch-groundbreaking-ai-underwriting-tool/
- Insurance Post, "Allianz GenAI tool saves 135 days of underwriter time in a year," 2025: postonline.co.uk/technology/7959005/
- Allianz media centre, "When the storm clears, so should the claim queue" (Project Nemo), 3 November 2025: allianz.com/en/mediacenter/news/articles/251103-when-the-storm-clears-so-should-the-claim-queue.html
- McKinsey, "The future of AI for the insurance industry," 15 July 2025: mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-the-insurance-industry
- Deloitte, "Using AI to fight insurance fraud," 2025: deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-predictions/2025/ai-to-fight-insurance-fraud.html