Flow is in pre-release. Flow-Edge and the audit trail are part of the deployment offer (roadmap) — not capabilities marked live today. For status in writing: hello@theflow-ai.com.
Claims and underwriting decisions carry two obligations at once: move fast enough to serve the policyholder, and be able to justify every input afterwards. Automation that drops the second one is the failure mode.
The constraint
Insurance automation is judged by a different standard than most. A claims decision has to be defensible to a regulator and to the insured; an underwriting input has to be actuarially justified, not merely applied consistently. Rate filings, market-conduct exams, and unfair-discrimination rules all assume someone can reconstruct why a specific decision came out the way it did — and "the model produced this" is not an answer that survives that examination. Meanwhile claims files, medical records attached to them, and applicant data are exactly the material carriers cannot casually hand to a third-party service.
How Flow addresses it
- Deployment that fits the data — roadmap. A masked secure cloud tenant, or fully local Flow-Edge when claims files and applicant data cannot leave the building, is the deployment path designed into the engagement — not a live product toggle today.
- Immutable audit trail — roadmap. For regulated deployments, an append-only record is planned: what was reviewed, what was proposed, who approved it, and what executed. Not a live product toggle today; closed in the engagement. That is the evidence a market-conduct exam or a rate filing actually asks for.
- Human gates on the calls that matter. Adverse decisions and edge cases surface for an adjuster or underwriter rather than executing silently.
- Inputs you can name. Deterministic execution over a defined set of inputs, so a proxy-discrimination question has a factual answer rather than an inference about model behaviour.
Research on insurance deployment
- AI-Driven Claims Automation: Efficiency, Fairness, and Regulatory Oversight
- AI in Insurance Underwriting: Rate Filing, Proxy Discrimination, and the Actuarial Justification Standard
- Auditability by Design: Logging, Provenance, and Human Oversight
- Model Drift: Why a Validated AI System Doesn't Stay Validated on Its Own
- What Is Data Masking? A Technical Guide to De-Identifying Data
Automation an examiner can follow
See how approval gates are designed, and where the decision record sits on the deployment roadmap.
Read the Security Page