Regulatory Assessment

BB-UW-0001 FLAG

TX · auto · underwriting · decided 2026-10-08 09:16 UTC · Bluebonnet Mutual Insurance Company (fictional)

On 2026-10-08, 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. A human reviewed the output by a underwriter. The outcome was adverse to the consumer; a notice was sent on 2026-10-14 with specific reasons. RegBench applied 11 rule(s) in force on 2026-10-08 and recorded 1 finding(s); the most serious is 'Geography and credit used together in an adverse decision (proxy pattern to test at portfolio level)' (medium, L6).

FLAG: 1 finding(s) under rules 2026.10+c4982313, corpus 2026-10-30-9c5dad5a. Routed to the carrier's compliance reviewer with cited findings.

Coverage: supported. 6 state-specific and 2 general black-letter rule(s) applied to this TX auto underwriting transaction. Only the configured checks were run; a result is not a legal opinion on compliance. 11 of 30 rules applied.

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 (1)

medium L6 doctrine advisory flag exhibit C doctrine.zip-credit-interaction-proxy

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.

NAIC Model Bulletin on the Use of AI Systems by Insurers (adopted 4 Dec 2023), Section 3, 1.0 General Guidelines — the written AIS Program verified

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: 2026-10-08
Rules version: 2026.10+c4982313
Corpus snapshot: 2026-10-30-9c5dad5a
Engine: regbench-engine/0.5.0 · deterministic-1
Fingerprint: 24b48952337e90de4a244870 (pattern grouping, not an integrity check)
Input digest: 88fe088523651832e629c3ec932923db7f67afb9cc10f59a6012768090daa545
Evaluated: 2026-10-31 05:42:52 UTC
Exhibit C case file: yes

Ledger

Entry: #5504 · assessment · recorded 2026-10-08T09:19:12+00:00
Hash: f9437fed165a45fe71baa959f775d3f1d776a979d3ce68f255b5d9afb0f3ec2f
Previous: 0000000000000000000000000000000000000000000000000000000000000000
Stored copy intact: yes, hashes match
Assessed input intact: yes, revision 1 matches the input digest

Verify the whole chain Case file JSON

Carrier review

Resolved by underwriter · outcome overridden · 5.7 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 0.874 · codes R12, R31
Human review: yes (underwriter)
Adverse to consumer: True
Notice: sent 2026-10-14 · reasons: True · specific: True · external data disclosed: False
Appeal channel: –
Consumer token: tok_dca5aec797