Evidence for regulators and reinsurers produced as agents run. Get claims and service agents live faster, for less.
Hand agents real claims and service work without losing control of payouts.
Give claims agents a mandate for the records and decisions they may handle. For a payout agent, block an unapproved payee change or hold a suspicious claim for a person.
Governed decisions record who asked, which rule applied and who approved. Reviewers can start from those records instead of assembling evidence after the event.
Reuse one control plane across claims, underwriting support and service instead of rebuilding controls for each agent.
Insurance AI is governed through state insurance law and guidance, privacy law, and international operational-risk frameworks. The applicable obligations depend on jurisdiction, product line, and how the system influences claims or underwriting.
The NAIC model bulletin gives state regulators a template for assessing insurers' use of AI. It is not itself a nationwide rule; obligations depend on each jurisdiction's adoption and implementation.
State model guidanceColorado SB 21-169 and other state laws, regulations, and bulletins create jurisdiction-specific expectations for external consumer data, algorithms, and predictive models in insurance. Multi-state carriers must assess each applicable rule.
StateEuropean insurers manage AI-related operational and model risks within the broader Solvency II framework, while DORA establishes ICT risk-management, resilience-testing, incident, and third-party oversight obligations.
EUFrom first notice of loss through settlement, AxonFlow provides governance controls that align AI automation with regulatory requirements.
Configure HITL approval gates on claims above internal dollar or risk thresholds. AI agents can triage and prepare claims while selected settlement actions wait for human review. Interactions routed through AxonFlow produce decision records for later review.
HITL Audit Trail Threshold GatesGovern AI assistants that help underwriters assess risk. Policies can require disclosure fields, block actions the assistant may not take, and redact applicant identifiers before they reach the model.
PII Protection Policy EnforcementFraud workflows need traceable inputs, policies, review actions, and downstream case evidence. AxonFlow records governed policy decisions and can export them for reconciliation with the insurer's investigation system.
Audit Trail Evidence ExportGovern AI-generated communications to policyholders. AxonFlow detects and redacts personal data before it reaches the model, and content policies block or escalate coverage commitments the agent is not allowed to make.
PII Detection Content PolicyAxonFlow provides technical controls that can support insurance AI governance programs. Map specific regulatory requirements to platform capabilities, then validate the final control design with legal and compliance teams.
| Regulation | Requirement | AxonFlow Capability |
|---|---|---|
| NAIC AI Model Bulletin | State-adopted governance expectations may cover AI program controls, consumer outcomes, documentation, and regulatory examination | Policy enforcement, decision records, and evidence export can support an insurer's governance and examination evidence; state-specific applicability remains with legal and compliance teams |
| State CO SB 21-169 & similar | Human oversight of AI-driven insurance decisions; governance documentation. Colorado Regulation 10-1-1 now extends to auto and health insurers with annual compliance reports, and Texas TDI Bulletin B-0003-26 applies unfair-discrimination law to decisions made or supported by AI | HITL approval gates with configurable thresholds; decision records with timestamps and policy references that can feed the insurer's own annual reporting |
| NY NYDFS Circular Letter 7 | AI system inventory including retired models, change tracking with approvals, vendor accountability, and disclosure of the information an adverse decision relied on, with pre-deployment and at-least-annual fairness testing | Policy versioning, HITL approval records with approver identity, and per-decision records with evaluated policies support inventory, change, and disclosure evidence; the quantitative fairness analysis itself remains the insurer's own actuarial work |
| NAIC AI Risk Evaluation Supplement | Standardized examiner questionnaire quantifying AI usage, governance frameworks, high-risk system detail, and data inputs; piloting in 12 states with adoption targeted for late 2026 | Decision volumes, governance policies, per-system audit history, and override records provide source evidence for the supplement's exhibits; evidence export packages them for review |
| EU Solvency II | Governance, risk management, and internal-control obligations that may encompass material AI-enabled processes | Circuit breaker for emergency halt; policy versioning with rollback; execution logging for risk assessment |
| EU DORA | ICT risk management; third-party oversight; operational resilience testing | Provider inventory and routing controls, rate limits, runtime monitoring, and decision evidence can support ICT risk and third-party oversight |
| EU EU AI Act | Some insurance uses may fall into high-risk categories; classification and obligations depend on the specific system and role | HITL controls, policy-attributed decision records, and PII controls can support a broader compliance program. AxonFlow does not classify systems or perform statistical fairness testing. |
AxonFlow is not a compliance certification product. It provides runtime controls, audit evidence, deployment choices, and human approval paths that security, legal, and platform teams can review before AI reaches sensitive workflows.
Technical documentation to help you integrate AxonFlow into your insurance technology stack.
Start with Community to validate the fit. Move to Evaluation when you need HITL approval gates and evidence export. Talk to us when you need enterprise rollout support.
