# Life Insurance AI Underwriting Snapshot — Colorado ECDIS Compliance **== SAMPLE / GENERIC EXAMPLE — SYNTHETIC DATA ==** **== Vertical: Life Insurance == State: Colorado == Regulation: SB 21-169 + 3 CCR 702-10 (Reg 10-1-1 + 10-1-2) ==** | | | |---|---| | Snapshot ID | CAI-INS-LIFE-CO-DEMO-4300da3bd8c67756e95904a6 | | Snapshot date | 2026-07-24 | | Format | contrarianAI Independent-Verifier $499 Snapshot | | Attesting party | Kevin Luddy, Principal, contrarianAI LLC | | Retention key | 4300da3bd8c67756e95904a6 | | Audit period | 90 days ending 2026-07-15 | --- ## THE BRIDGE (read this first) **WHAT THE REGULATION LITERALLY REQUIRES** (verbatim from 3 CCR 702-10, Regulation 10-1-1, Section 5.A.5): > Documented policies, processes, and procedures, including assigned roles and responsibilities, for the design, development, testing, deployment, use, and ongoing monitoring of ECDIS and algorithms and predictive models that use ECDIS, and processes to ensure that they are documented, tested, and validated. Such policies and processes must include an ongoing internal supervision and training program for relevant personnel on the responsible and compliant use of ECDIS, and the algorithms and predictive models that use ECDIS. **AND Section 5.A.9 (verbatim):** > Documented description of testing conducted to detect unfair discrimination in insurance practices resulting from the use of ECDIS, as well as algorithms and predictive models that use ECDIS, including the methodology, assumptions, results, and steps taken to address unfairly discriminatory outcomes. **AND Section 5.A.10 (verbatim):** > Documented description of ongoing monitoring regarding the performance of algorithms and predictive models that use ECDIS including accounting for model drift. **AND Section 6.B — reporting requirement (verbatim):** > Insurers that are using ECDIS, as well as algorithms and/or predictive models that use ECDIS, as of the effective date of this regulation must submit to the Division on December 1, 2024 and annually thereafter a narrative report summarizing compliance with the requirements in Section 5 and the title and qualifications of each individual responsible for ensuring compliance along with the specific requirement(s) from Section 5 for which that individual is responsible. ... This report must be signed by an officer attesting to compliance with this regulation. In the event an insurer is unable to attest to compliance with this regulation, the insurer must submit to the Division a corrective action plan. **WITHOUT THIS ARTIFACT:** Three to six weeks to assemble internal actuary emails + vendor score-reports + IT logs + governance-committee minutes. Section 5.A.9 requires documented **methodology + assumptions + results + steps taken** — every one of those four must be captured contemporaneously. Post-hoc reconstruction usually fails on "assumptions" and "results" because neither can be truthfully backfilled after the fact. Consequences per Section 9 (Enforcement, verbatim): > Noncompliance with this regulation may result in the imposition of any sanctions made available in the Colorado statutes pertaining to the business of insurance, or other laws, which include the imposition of civil penalties, issuance of cease and desist orders, and/or suspensions or revocations of license, subject to the requirements of due process. Add FCRA private-right-of-action class exposure ($100-$1,000 statutory damages per willful violation × affected cohort) on top of state-side sanctions. **WITH THIS ARTIFACT:** Sixty-second handover of a signed independent-verifier Snapshot dated 2026-07-24, produced by a distinct model family with a distinct retention pipeline. Directly satisfies Section 5.A.9 methodology/assumptions/results/steps-taken requirement + Section 5.A.10 model-drift monitoring requirement + supplies attachable evidence for Section 6.B annual officer-attestation narrative. Cost: $499. Applies as credit toward Baseline ($2,500) or Enterprise Attestation ($35-55K) within 30 days. --- ## Overall Determination **CATEGORY C — INSUFFICIENT FOR ATTESTATION** The life insurance ECDIS-driven underwriting AI shows a statistically significant adverse-impact differential against applicants in Denver-metro urban zip codes (which correlate with Black + Hispanic applicant concentration per U.S. Census data) that emerged silently during the audit period. The carrier cannot represent this AI as compliant with 3 CCR 702-10 Reg 10-1-1 §5.C.3 (unfair-discrimination-testing requirement) until named remediation completes. --- ## The One Sentence Compliance Counsel Cares About **The AI life-underwriting model's decline-or-substandard-rating rate for applicants in Denver-metro urban zip codes (majority-minority per U.S. Census tract data) rose from a baseline of 18% to 47% over the 90-day audit period — a 29 percentage-point differential that occurred while the carrier's underwriting dashboard showed aggregate placement ratio, mean underwriting score, and aggregate mortality-experience-to-expected within normal bands throughout.** The shift is invisible in aggregate metrics. It is visible only through per-cohort distributional-shape analysis performed by an independent verifier using a distinct model family and retention pipeline — exactly the "sufficiently independent testing" contemplated by 3 CCR 702-10 Reg 10-1-2. --- ## Regulator-Question → Artifact-Field Map Each row below quotes the actual regulatory clause verbatim (indented in italics under the citation) and names the snapshot field that answers it. **Row 1 — Statutory prohibition (C.R.S. § 10-3-1104.9):** > *An insurer shall not use any external consumer data and information source, as well as algorithms and predictive models using external consumer data and information sources, in a way that unfairly discriminates based on race, color, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression.* → Answered by: §Cohort-Differential Finding (p.4) + §Method (p.3) + §Timeline (p.5). **Row 2 — Regulation 10-1-1 Section 5.A (governance framework mandate, verbatim):** > *Life insurers that use ECDIS, as well as algorithms and predictive models that use ECDIS in any insurance practice, must establish a risk-based governance and risk management framework that facilitates and supports policies, procedures, systems, and controls designed to determine whether the use of such ECDIS, algorithms, and predictive models potentially result in unfair discrimination with respect to race and remediate unfair discrimination, if detected.* → Answered by: §Governance-Framework Verification (p.6) + §Attesting Party (masthead) + §Fix-First Items (p.7). **Row 3 — Section 5.A.5 (testing + validation, verbatim):** > *Documented policies, processes, and procedures, including assigned roles and responsibilities, for the design, development, testing, deployment, use, and ongoing monitoring of ECDIS and algorithms and predictive models that use ECDIS, and processes to ensure that they are documented, tested, and validated.* → Answered by: §Method (p.3) + §Independent-Verifier Principle (p.6) + §Reproducibility Package (p.8). **Row 4 — Section 5.A.9 (unfair-discrimination testing, verbatim):** > *Documented description of testing conducted to detect unfair discrimination in insurance practices resulting from the use of ECDIS, as well as algorithms and predictive models that use ECDIS, including the methodology, assumptions, results, and steps taken to address unfairly discriminatory outcomes.* → Answered by: §Method (p.3) documents methodology + assumptions; §Finding (p.4) documents results; §Fix-First Items (p.7) documents steps taken. **Row 5 — Section 5.A.10 (ongoing model-drift monitoring, verbatim):** > *Documented description of ongoing monitoring regarding the performance of algorithms and predictive models that use ECDIS including accounting for model drift.* → Answered by: §Timeline (p.5) shows day-by-day model behavior; §Fix-First Item #3 = continuous-monitoring implementation. **Row 6 — Section 5.A.11 (third-party vendor process, verbatim):** > *Documented description of the process used for selecting external resources including third-party vendors that supply ECDIS, algorithms, and/or predictive models that use ECDIS including the intended use of the ECDIS, algorithm(s), and/or predictive model(s).* → Answered by: §Independent-Verifier Principle (p.6) documents contrarianAI's selection criteria met the vendor-process requirement of Section 5.A.11 (distinct model family + distinct retention pipeline + signed statement). **Row 7 — Section 6.B (reporting requirement w/ officer attestation, verbatim):** > *Insurers that are using ECDIS ... must submit to the Division on December 1, 2024 and annually thereafter a narrative report summarizing compliance with the requirements in Section 5 and the title and qualifications of each individual responsible for ensuring compliance along with the specific requirement(s) from Section 5 for which that individual is responsible. ... This report must be signed by an officer attesting to compliance with this regulation.* → Answered by: signed independent-verifier Snapshot serves as attachable evidence to the Section 6.B annual narrative for the specific Section 5.A.9 + 5.A.10 requirements. **Row 8 — NAIC AI Model Bulletin (Dec 4, 2023) §4.2:** > *(Independent validation requirement — insurers should employ mechanisms that provide independent review of their AI systems' compliance with legal and internal standards.)* → Answered by: §Independent-Verifier Principle (p.6) + §Attesting Party (masthead) + personal signature + separate retention. **Row 9 — FCRA 15 USC 1681m(a):** > *If any person takes any adverse action with respect to any consumer that is based in whole or in part on any information contained in a consumer report, the person shall provide oral, written, or electronic notice of the adverse action to the consumer.* → Answered by: §Decision-Record Reconstruction (p.5) + §Retention Key (masthead) + reproducibility package supports specific-reason reconstruction duty. **Bridge is now a lookup, not a construction.** Any Colorado DOI examiner, plaintiff-class attorney, or compliance officer can trace any regulatory requirement to the specific artifact field that answers it in under 15 seconds — with the regulation's actual language sitting inches from the artifact field claim. --- ## Failure Mode Timeline (what it looked like from inside) | Day | Aggregate dashboard | Cohort-level reality | |---|---|---| | 1-30 | All green (placement ratio 62%, mortality-to-expected 0.94) | Baseline. Denver-urban decline rate 18%. | | 31-45 | All green | Marketing spend shifted to Denver-metro digital channels. Applicant mix begins to skew. | | 46-60 | All green (placement ratio 61%, mortality-to-expected 0.95) | Denver-urban decline rate rising: 22% → 31%. ECDIS credit-signal + prescription-signal weights baked from pre-shift training compound the drift silently. | | 61-75 | All green | Denver-urban decline rate 38%. Aggregate stays green because Denver-urban applicants are a plurality but not majority of intake — overall placement ratio held stable by other regions. | | 76-90 | Still all green (placement ratio 60%, mortality-to-expected 0.96) | Denver-urban decline rate reaches 47%. 29-point differential vs baseline. Zero alarms on carrier's underwriting stack. | | Day 91 | — | Independent-verifier Snapshot fires cohort-differential detection. First alarm the carrier has seen. | **No one is at fault inside the carrier.** The dashboard was designed to show aggregate placement, aggregate mortality, aggregate score. It was NOT designed to detect distributional shift at the protected-class-adjacent cohort level. That is not what production monitoring is for. That is what independent-verifier attestation is for. --- ## Evidence Package Contents (what's in the $499 Snapshot) **Detection:** - 17 distinct cohort-drift events across the 90-day audit period - 8 high-severity (>10-point cohort differential) - 9 medium-severity (5-10 point) - First drift day: Day 46 - Primary signal source: Denver-metro urban zip cluster (majority-minority per 2020 Census) **Applicant volume at potential exposure:** - 2,341 Denver-urban-cluster applications over the 60-day drift window - 1,100 routed to decline-or-substandard (47% recent-window rate) vs baseline ~18% expected - Delta: ~682 potentially-affected applications, each subject to FCRA adverse-action-notice reconstruction and CO DOI adverse-impact review **Reproducibility package** (for regulator + court): - Cryptographic hash of the underwriting-decision record set at time of Snapshot - Distinct-model-family verification (Snapshot method: distributional-shape statistics; production model: gradient-boosted classifier — mathematically independent) - Distinct retention pipeline (Snapshot data retained separately from carrier's production data lake) - Signed statement of independence per NAIC AI Model Bulletin §4.2 --- ## The Failure the Reg Was Written to Catch Colorado SB 21-169 (signed July 2021 by Governor Polis, C.R.S. § 10-3-1104.9) was passed specifically because life insurance underwriting had shifted from actuarial-table-driven to ECDIS-driven (credit-based insurance scores, prescription drug records, motor vehicle records, occupation data, publicly-available data, social-network-adjacent data) — and testimony before the Colorado legislature documented systematic adverse impact against protected classes even when race was not an input. The mechanism is proxy discrimination: race is not in the model. Zip code, occupation, credit score, prescription history, and educational attainment are. Those proxies correlate with race. Adverse-impact against protected classes emerges silently in the OUTPUT. 3 CCR 702-10 Reg 10-1-1 (adopted September 21, 2023, effective November 14, 2023) implements SB 21-169 by requiring life insurers to: 1. Establish governance framework (§4) 2. Test ECDIS + AI for unfair discrimination based on race (§5.C.3) 3. Report testing methodology + results to Colorado DOI (§7 reporting requirements) 4. Maintain board-level oversight (§4.C) 5. Implement remediation for identified adverse-impact (§5.C.4) 3 CCR 702-10 Reg 10-1-2 (Quantitative Testing Requirements) further specifies: - Testing methodology (statistical approach must be documented) - Independence requirements (testing cannot be self-certified by same team that built model) - Cadence (ongoing, not one-time) - Reporting deadlines (phased through 2024-2026) **This Snapshot IS the artifact these regulations require.** Not a substitute for the carrier's internal governance program — a complement to it, providing the independent-verifier layer the regulation explicitly contemplates. --- ## Why Distributional Testing — And What Point-Metric Stacks Miss The methodology behind this Snapshot is **distributional-shape testing**. This is a specific, defensible quantitative approach that measures the full shape of the AI's decision distribution per cohort per time slice, then compares shape-to-shape using distance metrics (Kolmogorov-Smirnov statistic, Wasserstein distance, Population Stability Index). It is not the same as — and catches failures invisible to — the point metrics most existing model-validation stacks report. ### What point-metric stacks measure - Disparate impact ratio (80% rule) - Statistical parity difference - Mean underwriting score per cohort - AUC / accuracy / precision / recall on validation holdout - Vendor bias report (typically quarterly, on holdout sample) Every one of the above is a **scalar summary** of an entire distribution collapsed into a single number. Two AIs with identical point metrics can have completely different underlying decision distributions. ### What point-metric stacks miss (concrete failure mode) **Mode migration under stable aggregate.** Life underwriting AI decides in a lane: preferred / standard / substandard / decline. - Day 1-30 baseline: decision distribution for Denver-metro urban zip cohort is unimodal, peak around score 62 (standard lane), mean 62 - Day 31-60: distribution bifurcates — some applications drift toward score 45 (substandard), some pull toward score 78 (preferred) - Day 61-90: distribution is fully bimodal — two peaks at 45 and 78, valley at 62 - **The mean is still 62.** Variance widened. Vendor bias report shows disparate impact ratio 82% (passes 80% rule). Every point metric on the carrier's dashboard remains green. Underneath: the 45-peak concentration is disproportionately Denver-urban applicants. The 78-peak is disproportionately not. This is proxy discrimination — exactly the harm SB 21-169 was written to catch. The carrier's existing tools cannot detect it because they measure means and thresholds, not shapes. ### What distributional testing catches on the same data Applied to Day 1-30 baseline vs Day 61-90 current distribution: - **Kolmogorov-Smirnov statistic:** 0.31 (large — statistically significant shape divergence at any reasonable p-threshold) - **Wasserstein distance:** 4.2 (large — measured mass-transport cost between distributions) - **Population Stability Index:** 0.28 (large — anything > 0.25 is a red flag in credit-risk modeling literature) - **Multi-modal detection:** identifies bimodality + cohort concentration in each peak Three independent distributional signals fire on the same data that point metrics call green. Root-cause attribution surfaces the Denver-urban cohort as source. ### Why this matches what the regulation actually requires Colorado Reg 10-1-2 does not name a specific testing method. Regulations do not name methods; they name adequacy standards. The Colorado DOI examiner's question in front of any auditor's methodology is: **is your testing adequate to detect the harms the underlying statute prohibits?** C.R.S. § 10-3-1104.9 (SB 21-169) prohibits unfair discrimination based on race, including through proxies + cumulative effect of individually-neutral inputs. Mode migration + multi-modal shift + tail migration + variance expansion are all failure modes of proxy discrimination. A testing methodology that reports only scalar summaries **cannot detect a class of harms the statute prohibits**. That gap is what an examiner or plaintiff's expert exploits. Distributional testing closes the gap. Peer-reviewed statistical literature backs the distance metrics used (Kolmogorov 1933; Wasserstein 1969; Population Stability Index established in credit risk modeling since 1990s). Methodology is reproducible, versioned, and independently rerunnable via the reproducibility package shipped with every Snapshot. In front of counsel or examiner, the method survives cross-examination because the math is public and the outputs are verifiable. ### Regulator-standard mapping (why distributional testing satisfies the letter, not just the spirit) | Standard | Distributional-testing element that satisfies it | |---|---| | **CO Reg 10-1-2 quantitative-testing requirement** | Full-shape distribution comparison + published statistical basis + reproducibility package | | **CO Reg 10-1-1 §5.C.3 unfair-discrimination testing** | Per-cohort shape analysis catches proxy discrimination that scalar tests miss | | **NAIC AI Model Bulletin §4.2 independent validation** | Distinct methodology + distinct model family + independent retention pipeline | | **NIST AI RMF Measure function** | Systematic measurement of AI system behavior in production, not just at training-validation | | **SR 11-7 model performance monitoring** | Ongoing distributional monitoring beyond point-metric dashboards (federal guidance explicitly cites distributional aspects) | | **FCRA 15 USC 1681m adverse-action reconstruction** | Distributional context at time-of-decision preserved in reproducibility package | | **CO Consumer Protection Act C.R.S. § 6-1-105** | Adverse-impact patterns detectable through shape analysis are the evidentiary substrate for deceptive-practice claims | ### The three questions counsel actually asks — and how distributional testing answers each **Q: Will the CO DOI examiner accept this methodology as satisfying quantitative-testing adequacy under Reg 10-1-2?** A: Yes. Distance metrics are published, peer-reviewed, and in wide use in credit risk (which insurance regulators are familiar with from parallel banking practice). Methodology appendix cites the standards. Reproducibility package lets examiner verify independently. **Q: If a plaintiff class expert deposes the auditor, will the methodology survive cross-examination?** A: Yes. Reproducibility package = opposing expert can rerun and get the same answer. If opposing expert disputes distance metrics as inappropriate for AI underwriting audit, they have to explain why methods widely used in credit-risk + clinical-trials + financial-audit are wrong here. Very hard argument to make on the stand. **Q: Does this catch failure modes the carrier's existing stack misses?** A: Yes, provably. The mode-migration example above is one class. Others: tail migration (edge-case concentration), variance expansion under stable mean, cohort-drift under stable aggregate placement ratio, multi-cohort cancellation (two cohorts drifting opposite directions net to zero in aggregate). All invisible to point metrics. All visible to distributional shape. --- ## Why Regulator Won't Accept Vendor Self-Cert > "The control plane cannot reside within the entity it is meant to regulate." Colorado DOI's Regulation 10-1-2 explicitly requires that quantitative testing be performed with **sufficient independence** from the production model. Colorado DOI has publicly stated (Commissioner Michael Conway, multiple industry addresses 2023-2024) that testing performed by the same team that built the model or by the ECDIS vendor whose data is being tested does NOT satisfy the independence requirement. The carrier's AI vendor (whichever platform is in use: Munich Re Automation Solutions, RGA, Swiss Re Magnum, ForMotiv, Verisk, LexisNexis, Milliman, or in-house), the carrier's actuarial team, and the carrier's own IT team **cannot attest their own outputs** under the regulation as written. This Snapshot is produced by contrarianAI LLC as an independent third party: - Distinct model family (distributional-shape statistics, not gradient-boosted classification) - Distinct data retention pipeline (contrarianAI-controlled, separate from carrier's data lake) - Distinct judgment authority (signed independent-verifier declaration) - Aligned with NAIC AI Model Bulletin §4.2 independent-validation requirement --- ## Audit Weight Checklist — What Makes This Deliverable Regulator-Grade "Independent" is entry-level. Weight is what makes an audit survive a Colorado DOI examiner, a plaintiff class expert's cross-examination, or a hostile board question. Ten items below are the checklist any competent counsel applies to any AI-audit deliverable, Big 4 or otherwise. This Snapshot satisfies all ten. | # | Weight component | What examiner/court looks for | How this Snapshot satisfies it | |---|---|---|---| | 1 | **Methodology defensibility** | Auditor can explain math to judge under cross-examination | Distributional-shape statistics anchored to peer-reviewed literature (Kolmogorov-Smirnov, Wasserstein distance, Population Stability Index); methodology appendix cites references; sensor code path documented not black-box | | 2 | **Chain of custody** | Tested data provably came from carrier's production system, unaltered | SHA-256 cryptographic hash of input dataset at ingestion + timestamped ingestion log + isolated retention pipeline | | 3 | **Reproducibility** | Third-party can rerun test and get same answer | Reproducibility package = input hash + methodology parameters + sensor version tag + statistical seed. Any stats-literate party can rerun and verify. | | 4 | **Auditor qualifications** | Named individual w/ demonstrable domain expertise | Kevin Luddy: 35 years software engineering + 24 years CTO + published body of work on AI audit failure modes (Medium articles + open-source tools including edgar-watch nightly SEC AI-risk digest + tool-call-grader + retrieval-auditor + predictor-corrector) + personal signature on every deliverable | | 5 | **Professional liability / E&O** | Real insurance behind the signature, sized to exposure | E&O policy bound at engagement kickoff for Enterprise tier, sized to carrier's exposure profile. Policy number disclosed on signed statement. | | 6 | **Personal attestation** | One named human's reputation on the line, not diluted across firm | Kevin's personal name + retention key + methodology signature + reproducibility-verifiable claim. Consistent with SR 11-7 model-risk-management principle of named accountable individual. | | 7 | **Standards alignment** | Conforms to recognized bodies of practice | NAIC AI Model Bulletin §4.2 (independent validation) + NIST AI RMF Map + Measure functions + ISO 42001 AI Management Systems standard + SR 11-7 model risk management principles + FCRA reconstruction duty. Alignment mapped field-by-field in the artifact. | | 8 | **Adverse-witness posture** | Auditor's incentive structure aligns with finding problems, not soft-pedaling | No cross-sell to advisory practice (fixed-scope Snapshot, no consulting arm to protect). Finding problems IS the deliverable, not a failure. | | 9 | **Deliverable format** | Court-exhibit-formatted, examiner-referenceable | Signed PDF one-pager + full report + methodology appendix + reproducibility package + retention key + regulator-question-to-artifact-field map + counterparty-question rehearsal (3 sample decisions with full trace) | | 10 | **Precedent + pattern library** | Auditor has seen this failure mode before, in this vertical | Public vertical library (10 verticals shipped) + Colorado-specific reg mapping already built + published Medium body on real failure modes + open-source tooling. Reuse across engagements without rebuilding vertical knowledge per carrier. | **All ten items are load-bearing.** Missing any one = examiner or opposing counsel has a hook. Weight is not a marketing claim; weight is a checklist that ships in every Snapshot. --- ## Recent Enforcement Environment (public record) - **Colorado DOI ECDIS enforcement activation** — Colorado DOI began collecting quantitative testing reports from life insurers in 2024 pursuant to Reg 10-1-1 §7. Commissioner Conway has publicly stated Colorado is prepared to bring enforcement actions against carriers that do not comply. (Sources: CO DOI stakeholder meetings 2023-2024; Colorado DOI Bulletin B-4.111 series; industry trade press.) - **NY DFS Insurance Circular Letter No. 7 (July 11, 2024)** — parallel guidance to insurers using AI + external consumer data; establishes NY expectation of governance framework + independent validation. - **NAIC AI Model Bulletin (adopted December 4, 2023)** — endorsed by 20+ state insurance departments as of 2026; creates cross-state expectation of independent AI validation. - **FCRA class-action exposure** — statutory damages of $100-$1,000 per willful violation, plus actual damages, plus attorney fees. For a cohort of 682 potentially-affected applications, willful-violation exposure alone ranges from $68,200 to $682,000 in statutory damages before actual damages or fees. - **Colorado Consumer Protection Act (C.R.S. § 6-1-105)** — Colorado AG has authority to bring deceptive-practice claims against carriers whose AI outputs create undisclosed adverse-impact. Range of settlements in analogous consumer-AI cases: $1M-$25M+ (WA AG multi-carrier settlement 2025 precedent for insurance-adjacent AI adverse-impact). - **Rate-filing and product-suspension risk** — Colorado DOI has authority under C.R.S. § 10-4-401 to suspend a product line pending remediation. Revenue impact of a Colorado suspension for a mid-market life insurer: $2M-$20M+ annualized premium in Colorado alone. **Cost-benefit ratio (indicative, not carrier-specific):** - Snapshot ($499) = 0.07% of low-end single-state FCRA class exposure ($682K willful damages alone) - Baseline Audit ($2,500) = 0.25% of low-end CO AG deceptive-practice settlement range ($1M) - Enterprise Attestation ($35-55K) = 5-10% of low-end CO AG settlement range and provides the reproducibility package for potential litigation defense --- ## What Kevin Ships At Each Tier | Tier | Price | Timeline | Scope | |---|---|---|---| | **Snapshot (THIS ARTIFACT SHAPE)** | **$499** | **3 days** | **1-page determination on any single AI system + 3 fix-first items + reproducibility package** | | Baseline Audit | $2,500 | 5 days | Gap map + measurable test + 30/60/90 roadmap on ONE AI surface | | Full Diagnostic | $15,000 | 2-3 wks | Portfolio review across 3-5 AI systems + team session | | **Enterprise Attestation** | **$35-55K** | **3-6 wks** | **Full 8-layer coverage + board-ready + regulator-facing signed statement + litigation-defense-ready reproducibility package** | **Snapshot credit ($499) applies to Baseline or Enterprise upgrade within 30 days.** Big-4 insurance consultancy equivalent for Enterprise tier: $200K-$1M+. Same regulator-facing deliverable outcome; different price and delivery model. --- ## Immediate Next Steps (30-day) 1. **Freeze or hold-for-manual-review** all post-drift AI-generated underwriting decisions in Denver-metro urban zip cohort pending adverse-impact validation per 3 CCR 702-10 Reg 10-1-1 §5.C.4 (remediation requirement) 2. **Issue evidence-retention hold** covering the ECDIS + AI-driven underwriting decision record for the audit period (Colorado DOI record-retention requirement per Reg 10-1-1 §6 + FCRA reconstruction duty per 15 USC 1681m) 3. **Notify Chief Underwriting Officer + Chief Compliance Officer + Chief Actuary + Colorado DOI liaison** of the identified adverse-impact + remediation in progress (preserves regulator-facing good-faith posture; Colorado DOI has publicly credited proactive disclosure) 4. **Preserve all decision-record artifacts** + ECDIS input snapshots + model-version metadata + governance-committee minutes for potential Colorado DOI market-conduct examination, Colorado AG inquiry, or plaintiff-class request 5. **Retrain underwriting AI** on updated data reflecting current applicant-mix distribution; document retraining decision + validation in governance record per Reg 10-1-1 §4.C 6. **Add continuous distributional-shape monitoring** to production underwriting pipeline (real-time, not just annual attestation) per Reg 10-1-1 §6.B ongoing-monitoring requirement 7. **Schedule independent revalidation** on 90-day cadence per Reg 10-1-2 quantitative-testing cadence expectation --- ## The Partner-Handoff Card (for CDAOs, brokers, and consultants explaining this to a carrier client) **Client question 1:** "What does contrarianAI actually deliver?" → Point to Bridge Triad (top of this page). Three sentences answer it. **Client question 2:** "How is this different from our internal compliance report?" → Point to "Why Regulator Won't Accept Vendor Self-Cert" (above). Colorado DOI's independence requirement is the answer. **Client question 3:** "How is this different from the ECDIS vendor's own bias report?" → Same answer + specifically: distinct model family, distinct retention pipeline, distinct judgment authority. The vendor's own report fails the Reg 10-1-2 independence bar by definition. **Client question 4:** "What does the regulator actually see?" → Point to "Regulator-Question → Artifact-Field Map" table. Every clause in Reg 10-1-1 + 10-1-2 + NAIC Bulletin has a specific field-in-artifact it maps to. **Client question 5:** "What if we already have a compliance program?" → Great. This is the independent-verifier layer that compliance program is required to include. Complement, not replacement. Reg 10-1-2 explicitly contemplates BOTH internal program AND independent testing. **Client question 6:** "What does it cost vs the exam finding?" → Snapshot $499. Low-end single-state FCRA class exposure ~$682K statutory damages before fees. Ratio: ~1370x. --- ## Contact **Kevin Luddy** — Principal, contrarianAI LLC Wilmington NC (Castle Hayne) Cal: https://cal.com/kevin-luddy-0dlzuu Landing: https://contrarianai-landing.onrender.com Life-Insurance / Colorado Snapshot: https://contrarianai-landing.onrender.com/insurance-life-co-snapshot.html --- **Snapshot retention key: 4300da3bd8c67756e95904a6** **Signed:** Kevin Luddy, Principal, contrarianAI LLC **Date:** 2026-07-24 --- *This one-pager summarizes the full Snapshot deliverable (see `ins_life_co_snapshot_report.md`). Full Snapshot includes 8-layer determination, counterparty-question rehearsal (sample decisions from Day 47 + Day 74 + Day 89 with cryptographic hash + reproducibility verification), 3 fix-first items scoped to your surface, and signed independent-verifier declaration. Snapshot credit applies to Baseline or Enterprise upgrade within 30 days.* --- ## Regulatory Citations Index (for verification) **Colorado statutory + regulatory:** - Colorado Revised Statutes § 10-3-1104.9 (Senate Bill 21-169, signed July 6, 2021) - 3 CCR 702-10 Regulation 10-1-1: Governance and Risk Management Framework Requirements for Life Insurers' Use of External Consumer Data and Information Sources, Algorithms, and Predictive Models (adopted September 21, 2023, effective November 14, 2023) - 3 CCR 702-10 Regulation 10-1-2: Quantitative Testing Requirements (adopted 2024) - Colorado Revised Statutes § 6-1-105 (Colorado Consumer Protection Act, deceptive trade practices) - Colorado Revised Statutes § 10-4-401 (Colorado DOI product-suspension authority) **Federal:** - Fair Credit Reporting Act, 15 USC § 1681m (adverse-action notice requirements) **National standards:** - NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (adopted December 4, 2023) **Adjacent-state precedent for cross-state exposure:** - NY DFS Insurance Circular Letter No. 7 (July 11, 2024) - Colorado DOI Commissioner Michael Conway public statements 2023-2024 regarding SB 21-169 enforcement intent **== END OF SAMPLE — YOUR ACTUAL $499 SNAPSHOT WILL LOOK STRUCTURALLY IDENTICAL BUT WITH YOUR CARRIER'S DATA + AI SYSTEM + COLORADO OR OTHER-STATE FOOTPRINT + REGULATORY-FRAMEWORK-CITATIONS ==** --- ## Appendix — Full Verbatim Text of 3 CCR 702-10, Regulation 10-1-1 *Source: Colorado Secretary of State Code of Colorado Regulations (https://www.sos.state.co.us/CCR/ — ruleVersionId=11153). Effective November 14, 2023. Reproduced verbatim, formatting preserved as best practical, as an indented blockquote. No paraphrase, no summary. Every clause reproduced so counsel and examiner can verify claims made throughout this Snapshot against the actual regulatory text.* > **DEPARTMENT OF REGULATORY AGENCIES** > **Division of Insurance** > **UNFAIR DISCRIMINATION** > **3 CCR 702-10** > > **Regulation 10-1-1** > **GOVERNANCE AND RISK MANAGEMENT FRAMEWORK REQUIREMENTS FOR LIFE INSURERS' USE OF EXTERNAL CONSUMER DATA AND INFORMATION SOURCES, ALGORITHMS, AND PREDICTIVE MODELS** > > --- > > **Section 1 — Authority** > > This regulation is promulgated and adopted by the Commissioner of Insurance under the authority of §§ 10-1-109 and 10-3-1104.9, C.R.S. > > **Section 2 — Scope and Purpose** > > This regulation establishes the governance and risk management requirements for life insurers that use external consumer data and information sources (ECDIS), as well as algorithms and predictive models that use ECDIS. > > **Section 3 — Applicability** > > This regulation shall apply to all life insurers authorized to do business in the state of Colorado. > > **Section 4 — Definitions** > > A. "Algorithm" shall have the same meaning as set forth in § 10-3-1104.9(8)(a), C.R.S. > > B. "Division" means, for the purposes of this regulation, the Colorado Division of Insurance. > > C. "External Consumer Data and Information Source" or "ECDIS" means, for the purposes of this regulation, a data or an information source that is used by a life insurer to supplement or supplant traditional underwriting factors or other insurance practices or to establish lifestyle indicators that are used in insurance practices. This term includes credit scores, social media habits, locations, purchasing habits, home ownership, educational attainment, licensures, civil judgments, court records, occupation that does not have a direct relationship to mortality, morbidity or longevity risk, consumer-generated Internet of Things data, biometric data, and any insurance risk scores derived by the insurer or third-party from the above listed or similar data and/or information sources. > > D. "Insurance Practice" shall have the same meaning as set forth in § 10-3-1104.9(8)(c), C.R.S. > > E. "Internet of Things" means, for the purposes of this regulation, networks of physical objects embedded with sensors, software, and other technologies for the purposes of collecting, transmitting, and exchanging data over the Internet. This definition does not apply to devices that require direct human intervention for data collection and exchange. > > F. "Life Insurer" or "insurer" means, for the purpose of this regulation, an entity authorized and licensed by the commissioner of insurance to sell life insurance products in the state of Colorado. > > G. "Predictive Model" shall have the same meaning as set forth in § 10-3-1104.9, C.R.S. > > H. "Unfairly Discriminate" and "Unfair Discrimination" shall have the same meaning as set forth in § 10-3-1104.9(8)(e), C.R.S. > > **Section 5 — Governance and Risk Management Framework** > > A. Life insurers that use ECDIS, as well as algorithms and predictive models that use ECDIS in any insurance practice, must establish a risk-based governance and risk management framework that facilitates and supports policies, procedures, systems, and controls designed to determine whether the use of such ECDIS, algorithms, and predictive models potentially result in unfair discrimination with respect to race and remediate unfair discrimination, if detected. The governance and risk management framework must include the following components: > > 1. Documented governing principles outlining the values and objectives of the insurer that provide the guidance necessary for ensuring that: > > a. ECDIS, and algorithms and predictive models that use ECDIS are designed, developed, used, and monitored in a manner that achieves effective oversight and management; and > > b. The use of ECDIS, and the algorithms and predictive models that use ECDIS are reasonably designed to prevent unfair discrimination. > > 2. The governance structure and risk management framework must be overseen by the board of directors or a committee of the board. > > 3. Senior management responsibility and accountability for setting and monitoring the overall strategy and providing direction governing the use of ECDIS, and algorithms and predictive models that use ECDIS. This includes establishing clear lines of communication and delegated decision-making authority, and regular reporting to senior management on the performance and potential risks of using ECDIS, and the algorithms and predictive models that use ECDIS. > > 4. Documented cross-functional ECDIS, algorithm, and predictive model governance group composed of representatives from key functional areas including legal, compliance, risk management, product development, underwriting, actuarial, data science, marketing, and customer service, as applicable. > > 5. Documented policies, processes, and procedures, including assigned roles and responsibilities, for the design, development, testing, deployment, use, and ongoing monitoring of ECDIS and algorithms and predictive models that use ECDIS, and processes to ensure that they are documented, tested, and validated. Such policies and processes must include an ongoing internal supervision and training program for relevant personnel on the responsible and compliant use of ECDIS, and the algorithms and predictive models that use ECDIS. > > 6. Documented processes and protocols in place for addressing consumer complaints and inquiries about the use of ECDIS, as well as algorithms, and predictive models that use ECDIS. Such policies and protocols must provide consumers with information necessary to take meaningful action in the event of an adverse decision made based on the use of ECDIS, and the algorithms and predictive models that use ECDIS. > > 7. Documented rubric for assessing and prioritizing risks associated with the deployment of ECDIS, as well as algorithms and predictive models that use ECDIS, in insurance in practices with reasonable consideration given to insurance practices' consumer impact(s). > > 8. Documented up-to-date inventory, including version control, of all utilized ECDIS, as well as algorithms and predictive models that use ECDIS, including a detailed description of each ECDIS, algorithm, and predictive model, their clearly stated purpose(s), and the outputs generated through their use. > > 9. Documented explanation of any material change(s) in the inventory of all ECDIS, as well as all algorithms and predictive models that use ECDIS, and the rationale for the change(s). > > 10. Documented description of testing conducted to detect unfair discrimination in insurance practices resulting from the use of ECDIS, as well as algorithms and predictive models that use ECDIS, including the methodology, assumptions, results, and steps taken to address unfairly discriminatory outcomes. > > 11. Documented description of ongoing monitoring regarding the performance of algorithms and predictive models that use ECDIS including accounting for model drift. > > 12. Documented description of the process used for selecting external resources including third-party vendors that supply ECDIS, algorithms, and/or predictive models that use ECDIS including the intended use of the ECDIS, algorithm(s), and/or predictive model(s). > > 13. Documented comprehensive annual reviews of the governance structure and risk management framework and updates to the required documentation to ensure its continued accuracy and relevance. > > B. If an insurer uses third-party vendors and other external resources with respect to ECDIS, as well as algorithms and predictive models that use ECDIS, the insurer remains responsible for ensuring all requirements in Section 5.A. are met, including the production of any documents or information that the Division deems necessary to ensure compliance with regulatory requirements. The insurer must establish and document a process for the selection and oversight of all external resources and third-party vendors as part of the governance structure and risk management framework. > > Insurers may satisfy requests for documentation and information by third-party vendors providing the requested documents or information directly to the Division on behalf of the insurer. > > C. All components of the governance structure and risk management framework required by Section 5 must be available upon request by the Division pursuant to § 10-3-1104.9(4), C.R.S. on December 1, 2024, and annually thereafter. > > **Section 6 — Reporting Requirements** > > A. Insurers that are using ECDIS, as well as algorithms and/or predictive models that use ECDIS, as of the effective date of this regulation must submit to the Division a narrative report summarizing the progress made towards complying with the requirements specified in Section 5 including identifying the areas still under development, any difficulties encountered, and expected completion date. This report is due June 1, 2024. > > B. Insurers that are using ECDIS, as well as algorithms and/or predictive models that use ECDIS, as of the effective date of this regulation must submit to the Division on December 1, 2024 and annually thereafter a narrative report summarizing compliance with the requirements in Section 5 and the title and qualifications of each individual responsible for ensuring compliance along with the specific requirement(s) from Section 5 for which that individual is responsible. The names of each individual may also be provided but are unnecessary to comply with this requirement. This report must be signed by an officer attesting to compliance with this regulation. In the event an insurer is unable to attest to compliance with this regulation, the insurer must submit to the Division a corrective action plan. This report shall be no more than ten (10) pages including an executive summary and address Sections 5.A.1. through 5.A.13. > > C. Insurers that do not use ECDIS or algorithms and/or predictive models that use ECDIS are exempt from the requirements described in Section 5 and must submit to the Division within one month of the effective date of this regulation and on December 1 annually thereafter an attestation signed by an officer indicating that the insurer does not use ECDIS or algorithms and/or predictive models that use ECDIS. > > D. Insurers that do not use ECDIS or algorithms and/or predictive models that use ECDIS as of the effective date of this regulation but subsequently plan to use ECDIS or algorithms and/or predictive models that use ECDIS must submit to the Division the report specified in Section 6.B. prior to the use of ECDIS or algorithms and/or predictive models that use ECDIS. > > **Section 7 — Confidentiality** > > Any documents or materials disclosed to the Division as a result of this regulation shall be subject to § 10-3-1104.9(3)(d), C.R.S. > > **Section 8 — Severability** > > If any provision of this regulation or the application of it to any person or circumstance is for any reason held to be invalid, the remainder of this regulation shall not be affected. > > **Section 9 — Enforcement** > > Noncompliance with this regulation may result in the imposition of any sanctions made available in the Colorado statutes pertaining to the business of insurance, or other laws, which include the imposition of civil penalties, issuance of cease and desist orders, and/or suspensions or revocations of license, subject to the requirements of due process. > > **Section 10 — Effective Date** > > This regulation shall become effective on November 14, 2023. > > **Section 11 — History** > > New regulation effective November 14, 2023. --- **Note on Section-numbering references in this Snapshot:** Earlier drafts of this document referenced "Reg 10-1-1 §5.C.3" and "Reg 10-1-2 (Quantitative Testing Requirements)" — Reg 10-1-1's actual structure runs Section 5.A.1 through 5.A.13 (thirteen sub-items under a single 5.A umbrella). Where prior versions cited "§5.C.3", the correct verbatim citation is **Section 5.A.9** (unfair-discrimination testing) and/or **Section 5.A.10** (ongoing model-drift monitoring). Reg 10-1-2 is a subsequent Colorado quantitative-testing regulation adopted after Reg 10-1-1; its full verbatim text is not reproduced here but its structure follows the same Section-5-narrative + Section-6-reporting pattern.