Who this is for: Insurance claims operations leaders, CIOs, compliance teams, brokers and technology delivery teams
Start with one narrow claims-preparation task
Define the specific work that occurs before a claims professional makes a decision: intake completeness, missing medical or repair records, duplicate attachments, date extraction, invoice reconciliation or policy-document retrieval. Record volume, average first-review time, queue age, frequency of missing information and reasons a case returns to the customer or partner. Do not begin with a promise that AI will adjudicate or approve claims. A useful first pilot reduces the effort needed to prepare evidence without automating coverage, liability or benefit decisions.
Make document provenance a required field
For each document, retain an authorised claim identifier, document type, source system, received timestamp, accessible file reference and where available the page supporting an extracted statement. Compare OCR or model fields with the actual source, not only another generated summary. Mark unreadable scans, missing pages, mismatched claimant or vehicle IDs and contradictory dates. A confident text summary without a reliable source page should not pass the quality gate. Keep permitted access and retention conditions visible to the reviewer.
Use the correct policy, endorsement and effective date
Claim handling often depends on the relevant policy wording, rider, endorsement and effective dates rather than the latest generic wording. Define an approved repository and policy-version selection rules. Check how a system handles endorsements added mid-term, changed sum insured, expired cover and missing policy documents. Where records disagree, route for human clarification instead of selecting the model’s most plausible answer. Do not present automated eligibility or legal coverage interpretation as a substitute for licensed or authorised claim assessment.
Build a privacy-safe evaluation sample
Select a small permitted set of historical or de-identified claim files representing clean, incomplete and ambiguous records, with a separate held-out evaluation sample. Include scans with missing pages, conflicting evidence, multiple document versions and known reviewer escalations. Obtain the data controller’s permission, minimise personal and health information, restrict model and provider access, and document deletion or retention obligations. Synthetic demonstrations are useful for design conversations but cannot establish real claims accuracy, fraud detection efficacy or legal compliance.
Specify who approves, corrects and escalates
Design an intake-to-review trail: AI-assisted extraction, source-linked completeness flags, a reviewer queue, explicit approval or correction and an auditable closeout. Only named insurance staff should decide claim outcomes or disputed interpretations. The system should record tool and model versions, reason codes, corrections, missed urgent cases and evidence updates. Test outages, duplicate processing and integration retries so a technical failure never silently advances or closes a claim. Customer communications must follow the insurer’s approved process.
Score errors by operational consequence
Report field-level extraction accuracy, source-link correctness, missing-document recall, false flags, urgent cases missed and time to a usable review pack. Compare with the manual baseline for the same task mix. Separately report harmful errors such as attributing one person’s document to another claim, citing the wrong policy version or suppressing a required escalation. A high average extraction score cannot excuse a serious unreviewed exception. Cost analysis should include staff review, infrastructure, licensing, remediation and governance overhead.
Agree a bounded pilot decision gate
A short assessment should yield the chosen task, permitted sample, source-system map, reviewer ownership, acceptance thresholds and explicit halt conditions. Only after those decisions can feasibility, schedule and commercials be agreed. IRAH’s proposal is for a governed engineering workflow with measurable evidence—not a claim that a production insurer deployment, live connector or approved claim outcome already exists.
Make the next discussion specific.
Bring the current workflow, available records and the decision you need to improve. We can discuss an evidence-based assessment and sensible controls before proposing technology.
Discuss this workflow ↗