Regulatory Assessment

SR-UW-0618 FLAG

TX · life · underwriting · decided 2026-10-25 19:46 UTC · Summit Range Life Assurance Company (fictional)

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+dbe5ddf6, corpus 2026-10-30-5f07c521. Routed to the carrier's compliance reviewer with cited findings.

Findings (2)

high L4 guidance flag exhibit B, C tx.tdi.consequential-decision-without-human-review

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.

medium L6 doctrine advisory flag exhibit B, C doctrine.unreviewed-adverse-high-risk

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+dbe5ddf6
Corpus snapshot
2026-10-30-5f07c521
Engine
regbench-engine/0.4.0 · deterministic-1
Fingerprint
0077b4903639b462573e1cac
Evaluated
2026-10-30 11:23:24 UTC
Exhibit C case file
yes

Ledger

Entry
#618 · assessment · recorded 2026-10-25T19:46:41+00:00
Hash
c3c0ee50043dc7a28063db5d21a8992704a9bb645dd0adfd8e53c7174e4952c8
Previous
0041771eb9d24a8f7a91d20d23780a936d8df93b62d4bae54a2c8895b981788e
Stored copy intact
yes, hashes match

Verify the whole chain Case file JSON

Carrier review

Resolved by underwriter · outcome remediated · 61.0 hours after it opened · Resolved in the carrier's review queue (synthetic).

Recorded facts

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