Accelerate responsible insurance AI deployment. HAIEC connects every high-consequence decision to business authority, effective permissions, executable actions and evidence, enabling leaders to advance automation with clarity.
Insurance leaders do not need another list of abstract AI risks. They need a decision about what can move into production, what requires a control, and what evidence justifies wider deployment.
Turn an unresolved governance debate into a named action boundary, a clear evidence request and an accountable release decision.
Separate recommendations from settlement, denial, policy mutation and payment initiation. Identify which decisions require supervised approval.
Trace the policy, permission, action and observation behind a finding; keep missing evidence visible instead of treating a checklist as proof.
Investigate tool paths, identity grants and cross-system consequences. Reassess when code, permissions or operating scope change.
Define your initial baseline during the engagement, then measure control coverage, release readiness and decision-cycle improvement.
Connect assurance to the operating economics of an insurance workflow. Adjust transaction volume, incremental bounded automation and handling cost to frame a measurable opportunity for your leadership team.
10,000 additional controlled actions × $15 per action
A HAIEC pilot can establish the eligible workflow, delegated authority evidence, actual handling baseline and incremental control costs before leadership evaluates the commercial case.
Editable scenario, not a HAIEC client result or a savings forecast.Insurance decisions can alter contracts, financial obligations, medical journeys and customer trust. Model accuracy and governance policy alone do not establish what connected AI can execute.
Settlement, premium, payment and payout instructions
Policy issuance, exclusions, coverage and beneficiary changes
Medical information, care referrals and clinical escalation
Identity, consent, disclosures and customer decisions
Third-party tools, agents, APIs and delegated workflows
Explore claims, underwriting, care, servicing and vendor actions. Each example connects the business consequence to a qualified authority question and the evidence needed to resolve it.
An assistant evaluates a benefit and prepares an instruction for a downstream payment or claims system. Technical access alone does not establish authority to settle.
Delegated amount, eligible benefit, policyholder/beneficiary, escalation owner and human approval requirements
Claims records, approved authority, tool/code path, payment target, approval witness and target-side result
An inspectable basis for bounded claims automation and remediation
An AI claims copilot may correctly identify an eligible benefit yet attempt a payment outside its delegated financial authority or to an unverified recipient. HAIEC's consequence-first method asks the additional questions that matter before calling the action supported.
Inspect the sample claims scenarioMore automation should come with stronger, not merely longer, evidence. Explore what changes when an insurer expands an agent's delegated action scope.
The system can prepare and request a change. An appropriately scoped human or policy checkpoint is required before the action is committed.
For a new or restricted agentic workflow, replace open-ended “is it safe?” discussions with a defined operating envelope, a tested action path, named approval ownership and explicit evidence gaps. Approve, limit, remediate or defer on a documented basis.
Action · target · magnitude · data
Policy · principal · capability
Tests · provenance · missing facts
Review · remediation · reassessment
Technical consequence evidence can support enterprise AI assurance today and create a foundation for new vendor assurance offerings, portable evidence products and specialist insurance partnerships.
Give AI release boards, claims leaders and audit teams an evidence-based view of consequential actions. Start with one workflow and expand across approved sources and operating environments.
Explore a portable, versioned technical record of evaluated AI authority, action scope, controls, provenance and change history for procurement and vendor reviews.
Explore how technical evidence on AI authority, executable exposures and tested controls could inform a licensed insurer’s or reinsurer’s specialist underwriting process.
HAIEC brings together AI estate visibility, delegated action integrity, technical controls and review-ready evidence, with insurance workflows as an enterprise operating context.
Map AI applications, agents, connected tools, source-capable routes and ownership into one operational picture.
Compare business decisions, effective access and executable capability. Investigate tool boundaries and application exposures.
Run applicable assurance checks, qualify source evidence and produce inspectable findings for executive, risk and audit reviews.
Track important changes in permissions, code, tools and operating scope with source-aware reassessment and monitoring.
Align technical evidence with applicable NIST AI RMF, ISO/IEC 42001, ISO/IEC 27001, SOC 2 and insurance supervisory review objectives, while retaining source and decision-owner traceability.
Examine source-qualified findings, action-path analysis and real evaluation artifacts across agentic services and industrial systems. The same core discipline provides a strong starting point for insurance AI assurance.
Inspect a sanitized real-repository evaluation of an AI service agent. HAIEC traced 44 consequential code-capable paths and recorded a REVIEW disposition, distinguishing technical capability from observed execution.
View Kestrel evaluation PERSISTED DETERMINISTIC EVALUATIONExplore a work-order target divergence found by HAIEC's evaluation over an explicitly synthetic industrial record set, including the requested and recorded action targets.
Inspect manufacturing evidence NETWORK ASSURANCE DESIGN / WORKSHOPSee how identity, delegated authority, network actions and evidence form an inspectable assurance case in telecom architecture and workshop scenarios.
Explore telecom scenariosConnect the business owner, model risk, technical assurance, executive approval and ongoing oversight through a shared, review-ready understanding of consequential AI actions.
AIA or another insurer retains business approval and risk ownership. HAIEC delivers the technical evidence that helps those owners make clearer decisions.
Governance workflow and technical consequence evidence can reinforce each other. Both models preserve the insurer's accountability for policies, approvals and remediation.
Use HAIEC to investigate eligible action paths, compare authority and review scoped evidence directly. The insurer retains source access decisions, policy authority and remediation ownership.
Coverage depends on actual connected systems and granted evidence access.Start with the interactive insurance workbench. Explore claims, underwriting, policy changes, health data and connected agents using clear, scenario-based evidence views.
Walk through a claims decision, inspect the authority boundary and explore the evidence that would support a release or remediation decision.
Launch Insurance WorkbenchTwo HAIEC-prepared presentations for an AIA-specific executive or technical discussion. Enter the insurance client name to open either presentation.
The business opportunity, measurable executive outcomes, HAIEC's differentiator, a representative workflow and the proposed pilot decision.
Insurance scenarios, transparent economics, governance architecture, assurance methodology, pilot execution and strategic expansion options.
AIA-focused proposal materials prepared independently by HAIEC; examples are illustrative and do not imply an AIA evaluation or endorsement.
Start with an accountable system owner, one consequential decision and a defined evidence objective. HAIEC builds an action-level assurance case and provides an executive decision package.
Name the business decision, effect, scope, accountable owner and applicable policy.
Identify approved code, identity, permissions, workflow, evidence and target-system inputs.
Record bounded findings, control results, uncertainty and evidence gaps where supported.
Present scoped findings, action-path matrix, control gaps, open questions and prioritized remediation for the insurer’s accountable review.
Each item reflects the evidence actually qualified in scope; unknowns remain explicit.
An initial assessment is commonly scoped for 4–8 weeks after source readiness. Commercial terms and any insurance-specific integrations are defined in the enterprise proposal.
Choose a consequential insurance workflow; receive a defined evidence boundary, deliverables, ownership and engagement proposal.
HAIEC can scope consequential AI paths in claims, underwriting, policy servicing, health data, agency, fraud operations and third-party agents. An engagement selects the action, its owner, technical sources and evidence objective.
HAIEC connects what an AI system requested, what business policy authorized, what identities were granted, what executable paths exist and what source evidence demonstrates. This action-level comparison supports accountable AI release decisions.
A scoped consequence map, delegated authority matrix, control/evidence coverage, prioritized exception register, source-qualified findings and an executive readout with recommendations for release, remediation and ongoing review.
HAIEC operates as an independent technical evidence layer alongside an insurer’s existing business, risk, security and audit teams. ServiceNow workflow integration can be scoped as a customer-specific implementation.
Assess advisory, supervised and bounded-action designs by comparing decision rights, tool access, effective grants, executable paths, approvals and operational observations. Customer governance teams use that evidence to expand autonomy deliberately.
Explore the insurance supervisory and enterprise governance references that inform our assurance methods.
NAIC — Artificial Intelligence IAIS — AI Supervision ServiceNow — AI Passport