Use-case guide

AI in Insurance Claims & Adjudication

AI in claims adjudication is the live wire of insurance regulation right now — distinct from underwriting AI. Three things changed in 2023-2025: (1) CMS issued binding rules requiring human review for Medicare-Advantage AI denials, (2) class actions against UnitedHealthcare and Cigna over algorithmic denials moved through US federal court, (3) state insurance commissioners started subpoenaing claim-AI documentation in market-conduct exams. The risk profile is acute because every wrongful denial is a discrete plaintiff with a discrete cause of action.

For: Claims VPs, SIU teams, healthtech claims platforms, Medicare-Advantage operators, AI claims-adjudication vendors

What's at stake

Medicare Advantage AI denials must have human review

CMS Final Rule (April 2023, effective 1 Jan 2024) requires AI/algorithmic tools used to make coverage decisions to be applied only after considering the individual patient's circumstances — and human review is required before adverse coverage determinations.

Class-action exposure under ERISA + state law

Estate of Lokken v. UnitedHealthcare and Barrows v. Humana have established the template for ERISA-based AI-claim-denial class actions. Damages stack: ERISA equitable relief + state UDAP statutory damages + punitive in some jurisdictions.

Colorado AI Act + state AI insurance rules apply

Colorado AI Act treats insurance claims as a consequential decision. Annual impact assessment + adverse-decision notice + human appeal route are statutory.

EU AI Act treats claims AI as high-risk (Annex III §5)

Risk-assessment and pricing AI in EU insurance includes claims-adjudication algorithms. Annex III §5(c) coverage; full risk-management + transparency + human oversight duties.

Regulations that apply

Do

  • ✓Document human-in-the-loop for every adverse decision — sample-based audits won't satisfy CMS or plaintiff lawyers. Per-decision human review record is the floor.
  • ✓Train your claims AI on data that includes the medical necessity criteria — not just claims-paid history. Otherwise you're reproducing historical denials.
  • ✓Build an audit log per AI decision: model version, input features, score, reviewing human, timestamp. ERISA discovery will demand it.
  • ✓Publish an algorithmic-decision policy at the member-facing level — Colorado AI Act requires consumer notice; CMS expects readable disclosure.
  • ✓Maintain a clinical-reviewer-override rate metric. If your AI is overridden <2% of the time, regulators question whether the review is real.

Don't

  • ✗Don't auto-deny based on length-of-stay benchmarks (Naviheath-style) without per-patient medical-necessity assessment — that's the exact pattern in Lokken v. UHC.
  • ✗Don't reuse a Medicare-Advantage claims model for commercial plans without revalidating — coverage criteria differ and disparate-impact analysis must be plan-specific.
  • ✗Don't take the claims-AI vendor's word for HIPAA-equivalent EU/UK GDPR-compliance — verify SCCs + DPA + processor obligations directly.
  • ✗Don't deploy a generative-AI summarisation tool that's used in denial decisions without Article 50 disclosure to members in EU markets.
  • ✗Don't disregard prior-authorization clinical guidelines because the model says no — the model is a tool; the clinical guideline is the legal floor.

Also worth knowing

If you operate in PPO/HMO commercial markets: ERISA's 'arbitrary and capricious' standard for benefits denials interacts with AI — courts increasingly hold that systematic AI denial patterns ARE arbitrary. If you're a third-party administrator (TPA): the TPA-as-fiduciary doctrine flows AI risk to you even if the underlying employer-plan bears nominal cost. If you operate in Medicaid managed-care: state Medicaid agency AI bulletins are emerging — California DMHC has been first-mover.

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Educational guide. Not legal advice. For specific compliance decisions, consult qualified counsel in the relevant jurisdiction.

Note: this guide was drafted with AI assistance — Anthropic Claude.