BB-UW-0070 HOLD
On 2026-10-13, uw-triage v2.4 (an internally developed system) produced 'decline' in a TX auto underwriting transaction. Inputs included application data, claims history, consumer report, credit, geography. 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 2026-10-17 with reasons stated. RegBench applied 11 rule(s) in force on 2026-10-13 and recorded 4 finding(s); the most serious is 'Credit-based adverse effect without the § 559.054 notice of reasons' (high, L2).
HOLD: 4 finding(s) under rules 2026.10+c4982313, corpus 2026-10-30-9c5dad5a. Recommended hold for the carrier's compliance review before the outcome is communicated; the carrier decides.
Evidence (carrier asserted). Supplied by the carrier and not verified by RegBench: ai_system.governance_attested, ai_system.model_doc_ref, ai_system.testing_report_ref.
Findings (4)
Credit-based adverse effect without the § 559.054 notice of reasons
Finding. Credit information contributed to an adverse effect on a personal-lines applicant or insured, and the transaction does not record a notice stating the reasons with the primary credit factors.
Basis. § 559.054 requires, within 30 days, notice of the action and the reasons, the consumer reporting agency's details, and the consumer's rights; the notice must describe "not more than four factors that were the primary influences", and generalized terms such as "poor credit history" are not sufficient. "Adverse effect" is defined in § 559.001(1).
Required action. Hold until a notice stating the primary credit factors is generated and recorded within the 30-day window. 30 days
Excerpt: tx-ins-559-054 · state statute (government edict) · in force from 2005-09-01
"(a) If, based in whole or in part on information contained in a credit report, an insurer takes an action resulting in an adverse effect with respect to an applicant for insurance coverage or insured, the insurer shall provide to the applicant or insured within 30 days: (1) written or electronic notice of the action resulting in an adverse effect and the reasons for that action; (2) the name, address, and telephone number of the consumer reporting agency ...; (3) ... notice that the consumer reporting agency did not make the decision ...; (4) ... notice of the applicant's or insured's right to: (A) obtain a free copy of the consumer's credit report ... during the 60-day period after the date of the notice; and (B) dispute ... the accuracy or completeness of any information ... (b) In the notice described by Subsection (a)(1), an insurer shall include a description of not more than four factors that were the primary influences of the action resulting in the adverse effect. (c) The use by an insurer of a generalized term such as 'poor credit history,' 'poor credit rating,' or 'poor credit score' does not constitute sufficient notice under this section ..."
Excerpt: tx-ins-559-001 · state statute (government edict) · in force from 2005-09-01
"'Adverse effect' means an action taken by an insurer in connection with the underwriting of insurance for a consumer that results in the denial of coverage, the cancellation or nonrenewal of coverage, or the offer to and acceptance by a consumer of a policy form, premium rate, or deductible other than the policy form, premium rate, or deductible for which the consumer specifically applied."
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."
Geography and credit used together in an adverse decision (proxy pattern to test at portfolio level)
Finding. The decision used both geographic and credit inputs and was adverse to the consumer; the interaction of these inputs is a recognised proxy pattern for protected characteristics.
Basis. Doctrine: the bulletin's concern with unfair discrimination extends to proxies, and the Supplement's Exhibit C item 10a asks whether inputs, derived variables and outputs are evaluated "for potential proxy relationships"; a geography-by-credit interaction should be covered by portfolio-level outcome testing and the result referenced from the case file.
Required action. Confirm the system's portfolio outcome testing covers the geography-credit interaction; link the result.
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
- 2026-10-13
- Rules version
- 2026.10+c4982313
- Corpus snapshot
- 2026-10-30-9c5dad5a
- Engine
- regbench-engine/0.5.0 · deterministic-1
- Fingerprint
- eb4284862dbe9755434e2cc3 (pattern grouping, not an integrity check)
- Input digest
- d769301738eeb9f1da2823594efac67be0f5c5ac73c80231556375439d2e1f7c
- Evaluated
- 2026-10-31 05:42:52 UTC
- Exhibit C case file
- yes
Ledger
- Entry
- #5573 · assessment · recorded 2026-10-13T01:20:09+00:00
- Hash
- 6d1ac5049004c764cf89dc7d892a65423a7be5ab3079235f3bfaf5c05db70238
- Previous
- a83cf22a069b7eb3c53741b096503ef508d23ed20a61bf3b74f7c386fb003e98
- Stored copy intact
- yes, hashes match
- Assessed input intact
- yes, revision 1 matches the input digest
Carrier review
Resolved by underwriter · outcome remediated · 3.9 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
- uw-triage v2.4 · internal · high-risk class: underwriting
- Governance attested
- True
- Testing report
- test://bluebonnet/uw-triage/2026Q3
- Model documentation
- doc://bluebonnet/uw-triage/2.4
- Feature classes
- application_data, claims_history, consumer_report, credit, geography
- Protected factors used
- none
- External data (ECDIS)
- False
- Consumer report / credit
- True / True
- Data sources
- cra://demo-bureau/report
- Decision
- decline · score 0.897 · codes R12, R31
- Human review
- none
- Adverse to consumer
- True
- Notice
- sent 2026-10-17 · reasons: True · specific: False · external data disclosed: False
- Appeal channel
- –
- Consumer token
- tok_defdeac4b1