Federato Launches the First Enterprise-grade Agentic AI for the Insurance Industry
Agentic AI is moving from demo to desk. If Federato is bringing an enterprise-grade platform to market, the question for insurers isn't "what is it?" - it's "where does it move the numbers?" Let's translate the headline into practical implications for underwriting, claims, and distribution.
What "agentic AI" means for insurance
Think systems that can plan multi-step tasks, call tools, and complete work with guardrails - while keeping humans in control. It's more than a chatbot; it acts on submissions, policies, and claims with context and memory.
- Underwriting: intake triage, appetite checks, data enrichment, exposure lookups, draft referrals, and bind-ready files.
- Claims: FNOL classification, document extraction, coverage checks, assignment, fraud flags, and subrogation cues.
- Distribution: broker prioritization, renewal risk alerts, and follow-up cadences that actually get done.
What "enterprise-grade" should include
If you're evaluating any agentic AI, hold it to the same standards as your core systems. Security, auditability, and control aren't optional - they're the cost of entry.
- Security and privacy: SOC 2/ISO alignment, PII/PHI handling, data residency options, network isolation.
- Controls: role-based access, approval paths, dual control for sensitive actions, configurable guardrails.
- Auditability: full decision trace, prompt/action logs, versioning, replay, and exportable evidence for regulators.
- Model risk: bias testing, drift monitoring, fallback behaviors, and documented limitations.
- Operations: SLAs, throttling, error handling, and business continuity plans.
- Integration: connectors for core (Guidewire, Duck Creek), CRM (Salesforce), data lakes (Snowflake), document stores, and data vendors.
Where it could move the needle
- Submission quality and speed: fewer touches to quote, cleaner referrals, faster bind.
- Hit rate: higher broker responsiveness through smart prioritization and follow-up.
- Underwriting discipline: appetite adherence and portfolio steering at the point of decision.
- Claims cycle time: quicker coverage confirmation, better triage, and more consistent recoveries.
- Leakage: fewer missed endorsements, subrogation, or salvage opportunities.
- Combined ratio: marginal gains across many workflows add up.
Vendor evaluation checklist
Cut through the buzzwords with questions that force specifics. You want proof of controllability, safety, and measurable outcomes.
- What tasks can the agent complete end-to-end without human help? Which steps require approval?
- How are guardrails enforced (policies, allow/deny lists, tool scopes, sandboxing)?
- Show the audit trail for a real case: prompts, tools called, data accessed, outputs, and human overrides.
- What happens on low confidence? How does it fail safely and escalate?
- Which integrations are production-grade today? Provide references using them.
- How are models updated, validated, and rolled back? How is drift tracked?
- What metrics improved at pilot customers (TAT, hit rate, STP, LAE)? Over what baseline and sample size?
Data and integration readiness
The tech is only as strong as the data it can reach. Map the data, clean the seams, and standardize formats before the pilot - you'll save months later.
- Core mapping: lines, classes, appetite rules, rating factors, and referral criteria in a structured form.
- Documents: ACORD forms, loss runs, endorsements, invoices, and adjuster notes with reliable extraction.
- External data: property, geospatial, weather, sanctions, and credit-adjacent signals consistent with your compliance stance.
- Real-time events: submissions, status changes, claim milestones, and broker activity streams.
- Reinsurance: treaty rules and facultative thresholds encoded for on-the-fly checks.
Compliance and risk controls
Anchor your program to recognized frameworks so audits go smoother and stakeholders stay aligned. Two helpful references:
- Written policies: data usage, human oversight, explainability, approval thresholds, incident response.
- Model inventory: purpose, owners, inputs, outputs, limits, validation dates, and monitoring plans.
- Privacy and consent: retention limits, field-level masking, and region-aware controls.
- Testing: fairness checks, adverse impact analysis, stress scenarios, and red-team reviews.
90-day pilot plan
Keep scope tight, automate what's repeatable, and prove value with numbers. A focused pilot beats a sprawling POC every time.
- Weeks 1-2: baseline metrics, success criteria, guardrails, and sandbox integrations.
- Weeks 3-6: single use case (e.g., commercial P&C submission triage) in parallel mode; daily review of exceptions.
- Weeks 7-10: partial production with defined approvals; compare to control group.
- Week 11-12: ROI analysis, audit package, scale decision, and backlog for the next two use cases.
Metrics that matter
If it doesn't change a number you care about, it's theater. Track these from day one.
- Turnaround time (quote, bind, and claim milestones)
- Straight-through processing rate and manual touches per item
- Hit/bind rate, premium per FTE, and referral acceptance
- Loss adjustment expense and leakage indicators
- Complaint rate, audit exceptions, and override reasons
- Model drift and low-confidence frequency
Change management that sticks
Adoption is the moat. Train people on the why, give them clear playbooks, and reward outcomes - not usage.
- Role clarity: who approves, who monitors, who tunes prompts and policies.
- Lightweight SOPs with screenshots and fallback steps.
- Weekly enablement: 30-minute sessions on real cases, wins, and misses.
- Aligned incentives: time saved, quality improved, and customer impact.
Practical next steps
- Pick one high-volume, rules-heavy workflow with measurable latency or error rates.
- Codify appetite, referral, and compliance rules as data - not tribal knowledge.
- Set guardrails and approval thresholds before any production traffic.
- Run a controlled pilot, publish the scorecard, and then scale methodically.
If this launch signals where the market is heading, expect more agent-based workflows baked into daily operations. Your advantage won't come from the label on the model - it'll come from clean data, crisp guardrails, and teams that know how to use them.
Want to upskill your team on practical AI workflows in insurance? Explore curated programs by role here: AI courses by job.
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