Use AI agents for insurance decisions you can prove

01

AI-assisted underwriting & pricing decisions

Underwriting and pricing is the most heavily scrutinized AI use case in insurance. Carriers using AI systems and external consumer data and information sources (ECDIS) in underwriting and pricing operate under a converging regulatory stack: the NAIC Model Bulletin (adopted in 24+ states) and other state regulations specifically governing AI use in underwriting and pricing with its quantitative testing and annual attestation requirements. Jaxon encodes your underwriting guidelines, your filed rating plan, your tier-assignment logic, and the state-specific anti-discrimination and proxy-test obligations governing each line as formal DSAIL^® rules. ECDIS variables flagged as potential proxies for protected classes are surfaced for governance review, not silently consumed.

• ▪Filed rating plan encoded as enforceable rules — every AI-recommended rate verified against the filed plan.
• ▪ECDIS variable governance — external data sources flagged when used outside documented underwriting purpose.
• ▪NY DFS proxy-test logic encoded — model inputs and outputs checked against Article 26 protected classes.
• ▪Colorado SB21-169 attestation evidence generated continuously — not reconstructed at filing time.
• ▪Tier-assignment determination — same risk profile, same tier, same state, appropriate rate, every time.
• ▪Underwriting decision chain of custody — rule fired, variables consumed, governance review, examiner sign-off.

02

Claims adjudication governance & policy enforcement

Carriers increasingly deploy AI to assist in claims review, coverage determinations, medical-necessity assessments, and reimbursement analysis — across health, P&C, and life. The risk is not simply incorrect output. It is policy misapplication: approving or denying a claim in ways that deviate from your filed policy language, your state-required claims settlement standards, CMS guidance (health), or your documented internal criteria. Jaxon embeds a deterministic policy-enforcement layer between your AI workflow and the claim decision, encoding your filed forms, coverage exclusions, and medical-necessity thresholds as formal DSAIL^® logic. If an AI model recommends a denial that does not match the filed form, or approves a service outside plan parameters, Jaxon flags or blocks the output before adjudication is finalized.

• ▪Filed policy forms encoded as enforceable rules — every AI-generated coverage decision checked against the form on file.
• ▪Coverage exclusions and conditions encoded element-by-element with field-level traceability.
• ▪State-specific unfair-claims-settlement obligations (prompt-pay windows, written explanation requirements, appeal rights) verified per state of insured.
• ▪Medical-necessity criteria (health) encoded per line of business — commercial, Medicare Advantage, Medicaid managed care.
• ▪Pessimistic completion posture — when the claim file does not contain what a rule needs, the verdict is UNKNOWN rather than a guess. What you do with an UNKNOWN is your policy; either way the rule was exercised and the record shows it.
• ▪Per-decision chain of custody — rule fired, evidence extracted, examiner touch, timestamp — defensible in market conduct exam and litigation.

03

Coverage determinations & authorization consistency

Coverage determination and authorization workflows — health prior authorization, P&C coverage triggers, life policy condition verification — are a focal point of regulatory attention. Errors in interpretation of clinical guidelines, misalignment with documented medical policies, or inconsistency with filed coverage triggers expose carriers to fines, corrective action plans, federal CMS scrutiny (Medicare Advantage), and reputational harm. Jaxon operates as a governance layer that enforces adherence to your documented clinical criteria, evidence-based treatment pathways, internal medical-policy documentation, and filed coverage terms when AI systems generate authorization recommendations. The emphasis is not speed — it is defensibility in a workflow under continuous scrutiny.

• ▪Documented medical policies and evidence-based pathways encoded per line of business.
• ▪Required escalation triggers (physician review, peer-to-peer, medical director sign-off) enforced as rules, not relied on as procedure.
• ▪Filed coverage triggers (P&C) and policy conditions (life) encoded with field-level traceability.
• ▪Pessimistic posture — when an authorization request lacks sufficient information, the rule fires UNKNOWN rather than PASS; the carrier decides what happens next, never an auto-approval.
• ▪Same patient profile, same diagnosis, same plan, same answer — disparate-treatment risk eliminated by determinism.
• ▪Appeal-and-grievance audit trail per determination — rule fired, criteria cited, timestamped.

04

Member & policyholder communications control

Carriers deploy AI-powered chatbots, content-generation systems, and member-services automation to communicate with members and policyholders about benefits, coverage eligibility, network providers, cost-sharing, claim status, and policy changes. These communications must strictly adhere to filed plan documents, state insurance regulations, mandated disclosures, and — for health carriers — HIPAA protected health information rules. Jaxon operates as a conduct-compliance enforcement layer that encodes your benefit language, your copay structures, your deductible terms, your required disclosure language, and state-specific consumer-protection requirements into deterministic constraints governing AI-generated responses. If an AI-generated response misstates coverage eligibility, omits a required disclosure, or includes PHI outside the minimum-necessary standard, Jaxon intervenes before delivery.

• ▪Filed plan documents encoded as enforceable rules — every AI-generated member response checked against the filed document.
• ▪Required disclosure language enforced per channel, per state, per product line.
• ▪HIPAA minimum-necessary standard enforced as a rule (health) — PHI emission outside policy blocked before transmission.
• ▪Copay, deductible, OOP-max, and benefit-tier logic encoded — no AI hallucination of cost-sharing terms.
• ▪Pre-clearance chain of custody — every AI-generated communication carries a verification token.
• ▪Cross-channel consistency — chatbot, member portal, IVR, email — same encoded rules, same answers.

05

Regulatory reporting & risk adjustment documentation integrity

Carriers produce complex regulatory filings — NAIC annual financial statements, ORSA reports, state Market Conduct Annual Statements (MCAS), risk adjustment submissions to CMS (health), rate filings, and form filings — and increasingly use AI to reconcile data, summarize metrics, and draft explanatory narratives. Jaxon functions as a cross-document verification layer built on formal proof, ensuring AI-generated reporting content aligns with underlying datasets, actuarial models, and regulatory thresholds. For health carriers under CMS Risk Adjustment Data Validation (RADV) scrutiny, coding-narrative consistency is verified deterministically. For all carriers, your regulator receives a filing whose narrative and numbers cannot diverge.

• ▪AI-generated regulatory narrative checked against submitted data — discrepancies flagged before filing.
• ▪RADV coding-narrative consistency verified deterministically (health).
• ▪ORSA, MCAS, and statutory-statement claims cross-referenced against underlying actuarial and operational data.
• ▪Cross-filing consistency — same metric, same value, every form, every state.
• ▪Rate filing and form filing narratives checked against the rate plan and policy forms they reference.
• ▪Filing-ready audit trail — every claim in every narrative tied back to a source record and a verified rule.

06

Enterprise AI governance across business lines

Carriers operate across multiple lines — commercial, individual, Medicare Advantage, Medicaid managed care, group life, P&C personal, P&C commercial — and each is subject to distinct regulatory regimes and policy requirements. As AI tools embed across underwriting, care management, claims, actuarial modeling, customer service, and fraud, carriers need centralized oversight to ensure AI usage aligns with documented policies and the NAIC Model Bulletin’s expectations on governance, risk management, and third-party AI vendor oversight. Jaxon functions as the enterprise AI governance layer — encoding business-line-specific constraints, escalation protocols, vendor-AI usage boundaries, and regulatory obligations into enforceable policy controls.

• ▪Business-line-specific AI usage policies encoded as enforceable rules — same model, different policy controls per line.
• ▪Third-party AI vendor outputs verified against your governance framework before consumption.
• ▪NAIC Model Bulletin alignment — written AI Program evidence generated continuously, not assembled at exam time.
• ▪Cross-line consistency oversight — AI decisions across commercial, individual, Medicare Advantage, and managed Medicaid checked against the same encoded fairness and accuracy standards.
• ▪Board and audit committee reporting — verified record of AI usage, exceptions, and remediations.
• ▪Whole-enterprise policy versioning — when state guidance changes, rules are updated centrally, not chased model-by-model.