How to Build an AI Agent for Insurance Claims Verification
Building an AI agent for insurance claims verification is one of the most practical ways to reduce cycle time without compromising accuracy or compliance. Claims teams spend a huge amount of effort on repetitive verification work: checking coverage, reconciling documents, confirming eligibility, and drafting summaries for review. An agentic workflow can handle much of that heavy lifting, so adjusters and examiners can focus on judgment calls instead of paperwork.
This guide walks through an evidence-first, audit-ready approach to designing an AI agent for insurance claims verification. You’ll get a reference architecture, an end-to-end build process, guardrails for privacy and governance, and a realistic MVP scope you can ship in 30–60 days.
What “Claims Verification” Means (and Where AI Helps)
Claims verification is the process of confirming that a submitted claim is complete, consistent, and eligible under the policy before it moves to adjudication or payment decisions.
It’s important to separate three related activities:
Claims verification: Are the basics true and supported by evidence? Is the policy active? Are documents complete? Do dates and parties match?
Adjudication: What is owed, under what coverage, and how much should be paid?
Fraud investigation: Is there intent to deceive, and does this warrant a Special Investigations Unit (SIU) referral?
An AI agent for insurance claims verification focuses on preparing a clean, evidence-backed file. It should not “invent” facts or make irreversible determinations. Instead, it orchestrates data retrieval, document AI for insurance, rules checks, and structured outputs that a human can approve quickly.
Typical verification checks
Most claims validation workflow checklists include:
Policy coverage and status Confirm active status, effective dates, limits, deductibles, exclusions, endorsements, and waiting periods.
Claimant identity and relationship Confirm the claimant is a named insured, authorized driver, dependent, beneficiary, or otherwise covered party.
Loss timing and location validity Ensure the date of loss is within coverage dates and the location aligns with policy constraints (as applicable).
Document completeness and consistency Validate that required documents are present and key fields match across forms, invoices, estimates, photos, reports, etc.
Duplicate claim checks Identify potential duplicates using policy number, incident details, invoice fingerprints, or near-duplicate text.
Where an AI agent adds value
Insurance claims automation often fails when it relies on one model output. Agents work better because they combine multiple deterministic and probabilistic steps:
Orchestrating OCR for claims documents, classification, extraction, and validation checks
Calling systems of record (policy admin, claims system, data warehouse) as sources of truth
Creating consistent summaries and routing decisions with confidence gating
Producing an audit trail: what was checked, what evidence was used, and what the agent recommended
A practical example is a claim processing agent that gathers a policy number and uploads, checks policy status and coverage in a data store, cross-checks documents against eligibility rules, and then routes for approval or rejection with a personalized message to the policyholder. Done right, this reduces staff workload while improving consistency and turnaround time.
The Business Case: KPIs, Risks, and Success Criteria
The best way to justify an AI claims processing initiative is to treat it like an operations quality program. Start by baselining today’s performance, then design to measurable outcomes.
KPIs to baseline and improve
Track a small set of metrics you can influence directly:
Average handling time (AHT) and time-to-first-touch
End-to-end cycle time (from FNOL automation or intake to verified file)
Straight-through processing (STP) rate or “touchless verification” rate
Reopen rate and appeals/complaints triggers (signals of poor verification quality)
Leakage indicators (overpayment risk) and “false flag” indicators (unnecessary SIU referrals or manual reviews)
Adjuster override rate (how often humans disagree with the agent, and why)
Risks you must explicitly manage
An AI agent for insurance claims verification touches regulated decisions and sensitive data. The main risks include:
Wrongful denial or delay caused by incorrect extraction or overconfident recommendations
Privacy issues: mishandling PII, financial account details, medical information (line of business dependent)
Biased outcomes if the workflow relies on proxies for protected characteristics
Weak defensibility: inability to explain what evidence led to a recommendation
Define “done” with acceptance criteria
Most teams skip this and end up arguing about “accuracy” forever. Instead, define acceptance criteria per verification check, plus an error budget.
A simple quality gate rubric:
Pass: Evidence found in system of record or documents; deterministic rules satisfied; confidence above threshold
Needs more info: Missing required document or ambiguous field; agent generates a targeted request
Needs human review: Conflicting evidence, low-confidence extraction on high-impact fields, or edge-case coverage logic
Blocked: System of record unavailable, corrupted file, policy mismatch, or potential security issue
Also define human override policies:
What decisions can be auto-actioned (e.g., requesting missing documents)
What decisions must remain recommendation-only (e.g., coverage interpretation, denials)
Reference Architecture for a Claims Verification AI Agent
A durable architecture prioritizes sources of truth, evidence links, and controlled automation. Think of the agent as an orchestrator that coordinates specialized components rather than a single “all-in-one” model.
Core components
Channels and intake Email, portal uploads, EDI/ACORD messages, call center transcripts, mobile app photos, vendor feeds.
Document processing layer OCR + layout extraction + document classification (invoice, estimate, police report, medical bill, etc.).
Agent orchestrator Controls flow: which tools to call, what to validate next, when to stop and escalate.
Rules and policy engine Deterministic checks: coverage dates, deductibles, limits, required forms, endorsement conditions.
Data connectors Policy admin system, claims system, CRM, payment systems, vendor networks, data warehouses.
Decision and explanation layer Evidence-backed rationale, confidence scoring, and structured recommendation categories.
Human-in-the-loop claims review Work queues, approvals, overrides, reason codes, escalation paths.
Observability and monitoring Logs, traces, exception analytics, drift monitoring, and feedback loops.
A common insurance pattern is for the agent to confirm policy status in a data warehouse (for example, Snowflake), cross-check uploaded documents and user input for ownership and eligibility, and then route for approval or rejection while generating communication for the policyholder.
Agent patterns that work well
Planner–Executor The agent plans verification steps based on claim type and available evidence, then executes tool calls in a controlled sequence.
ReAct-style iterative reasoning Useful when documents are messy: extract, notice inconsistency, retrieve more evidence, and retry with constraints.
Multi-agent (optional but powerful) Assign specialized roles:
Deployment models
API service plus workflow engine Good for integrating into existing claims platforms.
Event-driven, queue-based processing Scales to spikes (storm events) and supports retries and idempotency.
Hybrid or on-prem Often required for sensitive workloads, strict data residency, or legacy system integration.
Step-by-Step: Build the Agent Workflow
The fastest way to succeed is to start narrow, design for evidence, and expand only after you can measure performance.
Step 1 — Choose the claim type and verification scope
Pick a line of business and a claim segment with high volume and clear rules. Examples include:
Auto glass
Low-severity property damage
Simple device insurance claims
Routine supplemental documentation checks
Define what the agent can do automatically versus what it can only recommend:
Auto-actions (low risk): request missing documents, normalize forms, pre-fill claim fields, generate summaries
Recommendation-only (higher risk): coverage interpretations, eligibility edge cases, denials, SIU referrals
List required documents and minimum fields for a “verifiable file” (policy number, claimant info, date of loss, incident description, invoice/estimate basics).
Step 2 — Map the verification checklist to tool calls
Convert each check into a machine-executable unit with clear evidence requirements:
Inputs needed (fields, docs, identifiers)
Source of truth (policy admin, claims system, vendor feed, uploaded docs)
Deterministic rule vs probabilistic inference
Output schema (pass/fail/unknown), plus evidence pointers
Here are examples of how this mapping looks in practice (written as a checklist you can implement in a workflow builder):
Policy active status
Source: policy admin / warehouse
Rule: effective_date ≤ loss_date ≤ expiration_date
Evidence: policy record ID + timestamps
Deductible/limit extraction
Source: policy system (preferred) or policy PDF as fallback
Rule: numeric parse + validation against policy version
Evidence: record fields or extracted text span
Document completeness
Source: intake repository
Rule: required doc types present above confidence threshold
Evidence: document IDs + classification scores
Cross-document consistency
Source: extracted fields store
Rule: VIN/name/date-of-loss consistent within tolerance rules
Evidence: field provenance per document
Step 3 — Build document intake and classification
Claims work lives and dies on document quality. Build a robust ingestion pipeline before you touch “intelligence”:
Normalize file types (PDF, JPEG, HEIC), de-duplicate uploads, and virus scan
Assign a document ID and retain the original file in a secure store
Run PII detection and redaction where appropriate (depending on the workflow step)
For classification, use a model or hybrid model-plus-rules approach. Set thresholds:
High confidence: auto-route to extraction
Medium confidence: attempt extraction but mark as “review suggested”
Low confidence: send to human-in-the-loop claims review queue with a short “why” (blurry image, incomplete pages, unknown type)
A form processing agent pattern works well here: allow users to upload scanned or photographed handwritten forms, extract key fields from OCR text, flag missing or illegible fields, then format the result into a clear report stored in your system.
Step 4 — Extract and validate key fields
Field extraction is where document AI for insurance delivers immediate value, especially when intake includes handwritten or inconsistent forms.
Common fields to extract:
Claim number, policy number
Claimant name, contact details
Date/time of loss, report date
Vehicle or property identifiers (VIN, address, unit number)
Provider/shop information
Totals, line items, tax, invoice number
Diagnosis/procedure codes (health-related contexts, if applicable)
Then validate with rules that are simple, explicit, and logged:
Date consistency: loss date must not be after report date; loss date must be within policy period
Amount sanity checks: flag outliers relative to typical ranges for claim type (as “review,” not denial)
Arithmetic checks: invoice total equals sum of line items within rounding tolerance
Cross-document consistency: same VIN on estimate and repair invoice; same insured name across FNOL and policy
When extraction is ambiguous, the agent should not guess. Two productive fallback behaviors:
Needs more info: generate a short, specific request (e.g., “Please provide the full invoice showing line items and invoice number”)
Needs human review: route to a queue with highlighted fields and the exact document location that caused uncertainty
Step 5 — Verify policy and coverage
Coverage verification should be evidence-backed and conservative. Pull policy details from the system of record whenever possible:
Active status and term dates
Coverage limits and sublimits
Deductibles
Endorsements and exclusions
Covered parties and assets
Edge cases to plan for:
Lapsed policy with potential grace period
Backdated endorsements or mid-term changes
Multi-vehicle or multi-property ambiguity when identifiers are missing
Coverage triggered by specific conditions that need human interpretation
Your AI agent for insurance claims verification should produce a coverage summary that is reviewable, not a final denial. The key is to cite evidence sources (record IDs, extracted clauses) and note uncertainties.
Step 6 — Fraud/anomaly pre-screen (without overreaching)
Fraud detection signals can be helpful if you treat them as flags, not conclusions. Lightweight anomaly checks are often enough to prioritize attention:
Duplicate invoice detection (hashing, near-duplicate text, repeated invoice numbers)
Suspicious provider patterns (unusual frequency, repeated formatting artifacts)
Mismatched addresses or repeated bank details across unrelated claims
Claim frequency anomalies for a policy or claimant (depending on allowed data use)
Create an escalation policy:
Low concern: log and continue verification
Medium concern: route to “human review” with the signal summary
High concern: SIU referral suggestion with clear reason codes and evidence links
Guardrails matter here: avoid inferring sensitive attributes or using protected-class proxies. Keep the workflow focused on inconsistencies, duplication, and verifiable anomalies.
Step 7 — Decisioning, confidence, and human-in-the-loop
Output categories should reflect operational reality:
Verified (STP): all checks pass with sufficient evidence; agent can route forward automatically
Verified with notes: minor inconsistencies logged but low risk; proceed with caution
Needs human review: conflicts, low confidence on key fields, edge-case coverage logic
Needs more info: missing required documents or data fields
For confidence scoring, favor a weighted evidence score rather than a single model probability. For example:
Coverage checks sourced from policy system weigh more than extracted text from a blurry PDF
Multiple independent confirmations increase confidence
Any high-impact uncertainty forces a downgrade to review
Human-in-the-loop claims review should be designed for speed:
Highlight extracted fields and the exact evidence location
Provide a short “why” explanation for each flag
Include one-click approve/override plus required reason codes
Capture feedback in a structured way (this becomes training and tuning data)
Step 8 — Create an audit trail
Auditability is a feature, not an afterthought. Store artifacts that make decisions defensible:
Inputs (redacted when appropriate), with document IDs and hashes
Tool outputs (policy lookups, rules evaluation results)
Timestamps for every step
Model versions and prompt/config versions where applicable
Rationale text with evidence pointers
Human actions: who approved or overrode, when, and why
This audit trail supports internal governance, regulatory readiness, and faster root-cause analysis when something goes wrong.
Data, Privacy, and Compliance Requirements (Don’t Skip)
Insurance teams typically operate under GLBA and a web of state regulations; some workflows may also touch HIPAA-like considerations when medical information is present. Regardless of line of business, build controls into the workflow.
Data handling and security
Design for minimization and compartmentalization:
Collect only what the verification step needs
Encrypt in transit and at rest
Use RBAC/ABAC to restrict access by role, region, and claim severity
Segment tenants and environments (dev/test/prod)
Set retention policies aligned to legal and operational requirements
PII redaction should be a first-class step when routing documents to downstream tools or reviewers who don’t need full visibility. It’s especially relevant for emails, attachments, and freeform notes.
Model governance
If your agent influences operations, you need governance even if you’re not “training a model”:
Version everything: prompts, rules, models, extraction schemas
Maintain evaluation reports and change logs
Monitor for drift: new vendor invoice formats, new policy templates, seasonal claim spikes
Bake in explainable AI in insurance principles: evidence-first outputs, not opaque classifications
When you must not automate
Define hard stops requiring human review, such as:
High-severity claims, bodily injury, complex liability
Low-confidence extraction on fields that affect eligibility or payment direction
Conflicting documents with material impact (coverage dates, identity, location)
Any scenario where the agent would otherwise create a denial or SIU referral without a person reviewing evidence
Evaluation: How to Test Accuracy, Leakage, and Reliability
A claims triage AI workflow isn’t “done” when it runs. It’s done when it performs reliably under real-world messiness and business constraints.
Offline evaluation (before pilot)
Build labeled datasets for:
Document classification accuracy (per doc type)
Field extraction metrics (exact match, F1 where appropriate)
Verification agreement with adjusters (per check, not just overall)
Edge-case “golden set” claims (bad scans, missing pages, tricky endorsements, duplicates)
Also test failure modes:
System-of-record downtime
Corrupt documents
Conflicting information across sources
Online evaluation (pilot + rollout)
Run a controlled pilot (A/B where possible). Monitor:
STP rate and what drove it up or down
Distribution of exception reasons (missing docs, low confidence, inconsistencies)
Adjuster override rate and override reasons
Customer experience indicators: delays, complaints, appeals triggers
SIU referral volume and downstream quality signals (if applicable)
Error budget and threshold tuning
Decide what’s more costly for your line of business:
False approvals: risk of paying incorrect claims or creating leakage
False flags: risk of slowdowns and poor customer experience
Tune thresholds dynamically:
Higher automation for low-severity claims with clear evidence
More conservative routing for higher-severity or higher-impact decisions
Implementation Options (Build vs Buy vs Hybrid)
There’s no single right approach. The best choice depends on data access, compliance posture, and how differentiated your workflow is.
Build in-house
Pros:
Maximum customization
Tight integration into legacy systems and proprietary rules
Full control over governance
Cons:
Longer time-to-value
More engineering and maintenance overhead
Best when:
You have unique workflows, strict controls, and strong internal engineering capacity.
Buy components
Common components include OCR vendors, ID verification, rules engines, case management tools, and vector databases for retrieval.
Pros:
Faster rollout for standard processes
Mature tooling for specific tasks (OCR, IDV)
Cons:
Integration complexity across vendors
Governance spread across systems
Best when:
Your workflow is mostly standard and speed matters.
Use an agent platform / orchestration layer
An orchestration layer is often the “glue” that makes a claims validation workflow operational: tool calling, routing, governance, and human review in one place.
What to look for:
Tool calling and workflow controls (including safe retries and fallbacks)
Connectors to your data systems (policy admin, claims platforms, warehouses)
Observability: logs, traces, evaluation harnesses
RBAC, audit logs, and strict data processing controls
Human-in-the-loop controls for approval and override
StackAI is one example of a secure, no-code agent platform approach: it’s designed for enterprise workflows, supports building AI agents with drag-and-drop building blocks, integrates across systems, and emphasizes controlled automation with human oversight for higher-impact decisions. In regulated environments like insurance, those governance and security features often matter as much as model performance.
Common Pitfalls (and How to Avoid Them)
Most failures are design failures, not model failures.
Treating model output as ground truth Fix: require evidence links for every recommendation, and prefer system-of-record data over extracted text.
Over-automation in high-risk scenarios Fix: define hard stops and keep the agent recommendation-only where appropriate.
Ignoring poor document quality Fix: build robust intake, classification thresholds, and a clean “needs more info” loop.
No feedback loop from adjusters Fix: capture overrides with reason codes and feed them into tuning and rules refinement.
No escalation playbooks Fix: define what happens when the agent flags anomalies, finds missing docs, or detects conflicts.
Example: A “Minimum Viable” Claims Verification Agent (MVP)
You can ship a credible MVP in 30–60 days if you keep scope tight and focus on evidence-first automation.
MVP scope
One claim type (choose high volume and rule clarity)
3–5 document types (e.g., claim form, invoice/estimate, photos, police report if applicable)
10–15 verification checks (policy active, dates, identity match, totals consistency, duplicates)
Human-in-the-loop required for final decision and any escalations
Suggested workflow
Intake → classify → extract → validate → coverage check → inconsistency check → summary → route
This mirrors proven claim processing agent designs: gather the policy number and uploads, check coverage in a system of record, cross-check documents for eligibility, then route the outcome and draft communications.
Example output: verification summary template
Use a structured summary format that adjusters can scan quickly:
Claim overview Claim type, policy number, claimant, date of loss, reported amount
Documents received List of docs with confidence and notes (missing pages, low quality)
Checks performed (pass/fail/unknown) Short list with evidence pointers and reason codes
Coverage snapshot Active status, relevant coverage, deductible/limits, exclusions to review
Anomaly flags (if any) Duplicate indicators, inconsistencies, outliers (all as flags, not conclusions)
Recommendation
Verified (STP) / Verified with notes / Needs human review / Needs more info
Confidence score and what would raise confidence (missing doc, clearer image, policy record confirmation)
Conclusion + Next Steps
An AI agent for insurance claims verification works best when it’s built like an operational system: evidence-first, audit-ready, and designed for controlled automation. Start narrow with one claim type, codify verification checks into explicit tool calls and rules, and keep humans in the loop for high-impact decisions. Then measure outcomes relentlessly: STP rate, cycle time, overrides, and complaint triggers.
If you want a strong starting point, map your top 20 exception reasons today (missing documents, mismatched fields, coverage ambiguities) and design the agent to eliminate the most common ones first.
Book a StackAI demo: https://www.stack-ai.com/demo
