SR-UW-0618 FLAG
On 2026-10-25, rx-score v3 (a third-party system from RxData Labs (fictional)) produced 'rate' in a TX life underwriting transaction. Inputs included rx history, credit, consumer report, application data. A consumer report was used. No human reviewed the output before it took effect. The outcome was adverse to the consumer; a notice was sent on 2026-10-27 with specific reasons. RegBench applied 10 rule(s) in force on 2026-10-25 and recorded 2 finding(s); the most serious is 'Consequential AI-supported decision taken without a person reviewing and agreeing' (high, L4).
FLAG: 2 finding(s) under rules 2026.10+c4982313, corpus 2026-10-30-9c5dad5a. Routed to the carrier's compliance reviewer with cited findings.
Evidence (missing). Missing evidence: ai_system.governance_attested, ai_system.model_doc_ref, ai_system.testing_report_ref. Supplied but unverified: inputs.data_source_refs.
Findings (2)
Consequential AI-supported decision taken without a person reviewing and agreeing
Finding. An AI system in a high-risk class produced an outcome adverse to the consumer, and no person is recorded as having reviewed and agreed with the decision before action was taken.
Basis. Commissioner's Bulletin B-0003-26 (12 June 2026) states that for consequential decisions "TDI expects a person to review and agree with all decisions before action is taken", that decisions must not be inaccurate, arbitrary, capricious or unfairly discriminatory, and that the expectation extends to third parties working with the regulated entity.
Required action. Route to the responsible reviewer before the outcome takes effect; record the reviewer on the transaction.
Excerpt: tx-tdi-b-0003-26-human-review · regulatory bulletin addressed to all regulated entities and their agents (government edict) · in force from 2026-06-12
"TDI expects a person to review and agree with all decisions before action is taken" on consequential decisions made or supported by AI; decisions must not be "inaccurate, arbitrary, capricious, or unfairly discriminatory"; "These expectations extend to any third party working with a regulated entity." The bulletin cites Insurance Code chapters 541, 542, 544, 560, 751, 401, 831, 4001, 4101 and 4201.
Excerpt: tx-tdi-b-0003-26-governance · regulatory bulletin addressed to all regulated entities and their agents (government edict) · in force from 2026-06-12
"All regulated entities using AI should be able to furnish procedures and protections upon request by TDI." TDI "will monitor the use of AI through examinations and product filings", will investigate consumer complaints about AI, and may inquire into governance frameworks, risk management, data and privacy protections, internal controls and specific uses of AI. Under Insurance Code chapter 4201, utilization review agents may not use AI to make an adverse determination.
Adverse outcome from a high-risk AI system with no human review
Finding. A high-risk system produced an adverse outcome that took effect without human review.
Basis. Doctrine: examiners applying the NAIC bulletin's governance and control expectations, and the AI Risk Evaluation Supplement's Exhibit B question on whether there is a human in the loop for direct-consumer-impact uses, ask first about human oversight of adverse automated outcomes; an unreviewed adverse decision is the case file most likely to be sampled.
Required action. Route to the carrier's reviewer; record the review and any override on the transaction.
Excerpt: naic-ai-bulletin-s3-1-ais-program · NAIC model guidance; binding in a state only as adopted (see adoptions register) · in force from 2023-12-04
"all Insurers authorized to do business in this state are expected to develop, implement, and maintain a written program (an 'AIS Program') for the responsible use of AI Systems that make, or support decisions related to regulated insurance practices. The AIS Program should be designed to mitigate the risk of Adverse Consumer Outcomes ... The Department also encourages the development and use of verification and testing methods to identify errors and bias in Predictive Models and AI Systems, as well as the potential for unfair discrimination in the decisions and outcomes resulting from the use of Predictive Models and AI Systems."
Point in time
- Law as of
- 2026-10-25
- Rules version
- 2026.10+c4982313
- Corpus snapshot
- 2026-10-30-9c5dad5a
- Engine
- regbench-engine/0.5.0 · deterministic-1
- Fingerprint
- 63ff9c0cbeb5ab15883f4e21 (pattern grouping, not an integrity check)
- Input digest
- 866d7edab5a8941dafa824833ea085c3de955f81c6cad627b7e9cdfafc540d03
- Evaluated
- 2026-10-31 05:42:54 UTC
- Exhibit C case file
- yes
Ledger
- Entry
- #6121 · assessment · recorded 2026-10-25T19:46:41+00:00
- Hash
- f91aa5e36e9ed0f556cd4d43a27d6c950d1b2ff1d73b71d65db8844978094226
- Previous
- ed32b0872fa9a833e75c196588daaa4a037dc46db6b8f145cab53ce5407b74e2
- Stored copy intact
- yes, hashes match
- Assessed input intact
- yes, revision 1 matches the input digest
Carrier review
Resolved by underwriter · outcome remediated · 61.0 hours after it opened · Resolved in the carrier's review queue (synthetic).
Recorded facts revision 1
As asserted by the carrier. References are recorded, not verified: RegBench holds no copy of the documents they name.
- AI system
- rx-score v3 · third party (RxData Labs (fictional)) · high-risk class: underwriting
- Governance attested
- False
- Testing report
- none recorded
- Model documentation
- none recorded
- Feature classes
- rx_history, credit, consumer_report, application_data
- Protected factors used
- none
- External data (ECDIS)
- False
- Consumer report / credit
- True / True
- Data sources
- vendor://rxdata/score, cra://demo-bureau/attributes
- Decision
- rate · score 0.677 · premium +58.1%
- Human review
- none
- Adverse to consumer
- True
- Notice
- sent 2026-10-27 · reasons: True · specific: True · external data disclosed: False
- Appeal channel
- –
- Consumer token
- tok_b8c44a9ea2