PLAYBOOK · INSURANCE
An on-prem claims processing network from FNOL to settlement.
FNOL intake, document OCR, coverage check, fraud signal, payout decision — every stage is a sub-intent. This playbook orchestrates them as one VDF AI Network with end-to-end traceability.
A claim is a small workflow disguised as a document. OCR extracts the form, retrieval finds the coverage clauses, a rule call resolves entitlement, a fraud signal weighs the risk, and an approval router moves the case to the right desk. VDF AI composes all of it as a single Network with an unbroken audit trail.
The problem
Claims still move paper
Even digital claims arrive with scanned receipts, photos, policy PDFs, and free-text descriptions. Adjusters spend hours on data extraction before they get to the decision.
The VDF AI approach
One network, every stage
OCR built-in. Policy vectors in pgvector. Rule agents wired to your engine. Approval routing baked into the Network. A claim enters; an evidence-backed decision exits.
WHY THIS MATTERS NOW
Claims operations are the fastest payback for on-prem AI
The cost of a single bad claim handoff dwarfs the cost of a single bad LLM token. Insurance leaders feel that asymmetry every quarter. The right unit of optimization is therefore the case, not the prompt.
VDF AI treats every claim as a case to be decomposed: OCR extraction, coverage RAG, rule call, fraud weighting, payout decision, approval routing. Each sub-intent is a tool or agent under one Network. Each step is replayable. Adjusters spend their time where their judgment actually matters.
WHAT YOU NEED TO START
Prerequisites for a pilot
Intake & documents
- FNOL channel endpoints (email, portal, API)
- Document store or DAM
- Adjuster system credentials
- Existing OCR provider (optional)
Rules & policy
- Internal rule engine endpoint
- Policy document corpus
- Fraud signal taxonomy
- Approval authority matrix
People
- One claims operations lead
- One fraud manager
- One IT integration owner
- One actuary or product manager
REFERENCE ARCHITECTURE
FNOL to settlement, one network
email · portal · API
Rule engine
Intent: process-claim
PLAYBOOK · STEP BY STEP
From FNOL to evidence-backed decision
Ingest the claim
FNOL hits a Custom HTTP tool that routes payload + attachments into the Network.
OCR + structured extraction
The built-in ocr tool plus an extraction agent turn images into typed fields with provenance.
Coverage retrieval
Policy documents are vectorized. The Coverage Agent finds the right clauses, cites them, and feeds them to the rule engine.
Decision and approval routing
The Payout Agent emits a decision; the Network routes by authority threshold to auto-approve, adjuster, or fraud team.
Audit and learn
Every claim's run is replayable. SEEMR optimizes which model handles which sub-task.
OUTCOMES
Throughput and trust at the same time
average cycle time on routine claims.
evidence packs include OCR provenance + policy citations.
fraud team focus on real signals, not data wrangling.
SEEMR REFERENCE
Adjuster time goes to real decisions
SEEMR learns which sub-intent (extraction, coverage, fraud) maps to which model, balancing cost, latency, and accuracy.
FREQUENTLY ASKED QUESTIONS
What teams ask before shipping this playbook
Will it replace adjusters?
No. It removes the data-entry burden from adjusters so they can spend more time on judgment-heavy cases.
How is fraud risk handled?
A Fraud Signal Agent runs in parallel and contributes a score to the case evidence pack. High-risk cases route to fraud teams; low-risk are auto-approved within policy limits.
Can it work across business lines?
Yes. Each LOB (auto, property, life, health) uses its own intent template, tools, and indexes; one Network surface orchestrates them all.
How are exception cases surfaced?
The Network exposes structured exceptions with a reason code. Adjusters see the queue with priority signals and one-click case context.
Is this compatible with SAP, Guidewire, or in-house core systems?
Yes — all integrations are Custom HTTP tools. Wherever there is a typed API, VDF AI can call it.
What about audit and regulator reporting?
Every case run produces a Vault-backed audit record. Regulator-ready reports can be generated from the standard event log.
RELATED PLAYBOOKS
Continue with related VDF AI patterns
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You Have Questions
Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.