Define the operating boundary
A useful definition names the triggering event, required inputs, governing source, accountable owner, decision or action, exception path, evidence retained, and downstream handoff. Buyers should adapt those elements to their own population, jurisdictions, policies, systems, and control model before writing requirements.
The most important distinction is between a label and an operational capability. A provider may document machine-assisted extraction prediction or decision support while depending on customer-supplied policy, licensed content, third-party data, integration partners, manual review, or services. The demonstration should expose those dependencies rather than hiding them behind a completed interface.
What a demonstration should prove
- Begin with representative source records and a named policy, standard, or controlled rule.
- Show the normal path, an ambiguous case, missing data, an exception, an override, and a material source change.
- Identify who can change rules, who can approve or reject, and how accountability is preserved.
- Trace every output back to inputs, versions, timestamps, user actions, and governing evidence.
- Export the resulting record and reconcile it with downstream systems and retained obligations.
Authority and operating context
NAIC Insurance Fraud Prevention Model Act
The model act provides a model statutory structure for insurance fraud prevention, reporting, investigation, and related authority. Fraud technology must preserve jurisdiction, referral criteria, evidence, investigator authority, reason, action, privacy, and downstream decision rather than treating a model score as fraud.
NAIC AI Model Bulletin
The model bulletin reminds insurers that AI-supported consumer decisions remain subject to applicable insurance law and describes governance and documentation regulators may request. Underwriting, pricing, claims, fraud, and service technology buyers need inventories, purpose, data, controls, testing, monitoring, third-party oversight, consumer-impact review, and decision records.
NIST AI RMF
The AI RMF organizes voluntary AI risk-management work around Govern, Map, Measure, and Manage. Insurance model inventories, underwriting, pricing, fraud, claim guidance, document extraction, and communications need purpose, context, data, testing, monitoring, accountability, impact, and change controls.
Operating domains
Underwriting intake, risk, and authority
The controlled path from submission and data collection through enrichment, eligibility, referral, analysis, pricing, authority, decision, communication, and retained reason.
Claim intake, coverage context, and assignment
The operating discipline for receiving a loss or benefit event, identifying the policy and parties, preserving notice, gathering initial facts, establishing coverage context, segmenting the work, and assigning accountable ownership.
Damage estimation, repair, and service networks
The operating chain connecting images, measurements, inspections, parts, labor, repair methods, estimates, suppliers, providers, appointments, supplements, quality, and claim settlement.
Fraud investigation, subrogation, and litigation
The controlled escalation from anomaly or recovery signal through review, investigation, evidence, legal authority, referral, action, recovery, dispute, litigation, and outcome.
AI, automation, and consumer-decision governance
The controlled lifecycle for data, rules, models, extraction, generation, recommendation, automation, human authority, consumer impact, monitoring, change, and evidence across insurance decisions.
Market conduct, financial, and audit evidence
The retained and reproducible record of consumer transactions, policy and claim decisions, financial movements, communications, complaints, exceptions, model contributions, controls, and accountability required for oversight and independent review.
Evidence and comparison limits
Official provider documentation can establish product positioning. Provider confirmation can clarify package or availability. Independent observation requires a disclosed scenario, environment, date, inputs, and reproducible result. None of those sources alone establishes buyer-specific legal, clinical, regulatory, quality, or operational fitness.
Buyer questions
- What exact outcome and evidence should machine-assisted extraction prediction or decision support produce?
- Which source, version, and customer facts govern the workflow?
- Which decisions remain human and who is accountable for them?
- What is native, configured, integrated, service-delivered, or planned?
- How does a changed source affect open and historical records?
Recent changes
Claims Core Ledger records the claims-automation evidence crosswalk — The event changes the maintained authority, ownership, product, portfolio, financial-reporting, or operating context. Buyers should update affected records while keeping announcements separate from configured behavior, implementation, model performance, consumer impact, and claim outcome.
Duck Creek acquires Send Technology — The event changes the maintained authority, ownership, product, portfolio, financial-reporting, or operating context. Buyers should update affected records while keeping announcements separate from configured behavior, implementation, model performance, consumer impact, and claim outcome.
CCC completes the EvolutionIQ acquisition — The event changes the maintained authority, ownership, product, portfolio, financial-reporting, or operating context. Buyers should update affected records while keeping announcements separate from configured behavior, implementation, model performance, consumer impact, and claim outcome.
NAIC adopts the AI Model Bulletin — The event changes the maintained authority, ownership, product, portfolio, financial-reporting, or operating context. Buyers should update affected records while keeping announcements separate from configured behavior, implementation, model performance, consumer impact, and claim outcome.