Bias-Constraint Evidence for Underwriting
AI now touches underwriting, pricing, and claims — the decisions state regulators examine most closely. FERZ provides bias constraints enforced against documented thresholds, and tamper-evident, replayable decision records built for state regulatory examination contexts.
State Examination Is the Front Line
Insurance AI governance is shaped state by state, and examination practice moves faster than rulemaking. The direction is consistent: insurers are expected to show how AI-influenced decisions are governed, not merely explain them afterward.
AI Governance Expectations
State insurance regulators are adopting AI governance expectations for insurer use of AI systems, including accountability for outcomes of AI-influenced decisions across underwriting, pricing, and claims. State-level AI statutes increasingly reach insurance decisions directly.
Bias Evidence, Not Bias Assertions
Longstanding unfair-discrimination standards apply to AI-influenced decisions. What changes with AI is the evidentiary burden: insurers need documented thresholds and testable records, not narrative assurances.
High-Risk Insurance Pricing
AI systems used for risk assessment and pricing in life and health insurance are classified high-risk, with conformity assessment, documentation, human oversight, and robustness obligations for insurers with EU exposure.
Underwriting and Claims
The two surfaces regulators examine most closely have different failure modes and need the same evidence discipline.
Where bias exposure concentrates
Risk selection, rating, and pricing decisions influenced by AI carry unfair-discrimination exposure. Bias constraints enforced against documented thresholds produce evidence designed for regulator-facing review.
Where decisions become effects
Claims triage, fraud flags, and settlement recommendations are effect-bearing actions. Each governed action leaves a tamper-evident, replayable record of what was permitted and on what basis.
FERZ evaluates a proposed action against codified rules before it executes, issues a verdict of ALLOW, DENY, or ABSTAIN, and records a tamper-evident, replayable authorization artifact. Bias is not asserted away: it is constrained against documented thresholds, with the constraint and its evidence recorded in the same artifact the examiner reviews. When governance conditions are not met, execution is blocked, not allowed by default.
Evidence Built for State Examination
Documented thresholds
Bias diagnosed and constrained to documented thresholds, with regulator-traceable evidence rather than unsupported claims of bias-free operation.
Replayable by design
Tamper-evident, replayable authorization artifacts designed for independent reconstruction: what was permitted, under which policy version, on what basis.
Blocked, not assumed safe
Uncertainty resolves to ABSTAIN and a recorded human decision, not to silent execution. The override is itself a recorded authorization event.
Talk to FERZ about insurance AI governance
Tell us about your underwriting and claims AI context. Specific deployments and compliance posture are scoped during engagement.