# AI Portfolio-Recommendation Reproducibility + Reg BI Cohort-Differential Snapshot

**== SAMPLE / GENERIC EXAMPLE -- SYNTHETIC DATA, NOT A CLIENT ENGAGEMENT ==**

**Format:** contrarianAI Independent-Verifier $499 Snapshot (v0.9 sensor)
**Snapshot generation date:** 2026-07-22
**Snapshot ID:** CAI-RIA-GEN-DEMO-7691521283eddb6ded4f0b36

---

## 1. Scope of Snapshot

This SAMPLE Snapshot addresses a synthetic mid-market RIA's AI-driven portfolio-recommendation engine. Scenario details:

- **Firm profile:** ~$500M-$5B AUM / 500-5,000 client households / mid-market RIA (dual-registered or state-registered)
- **AI system in scope:** portfolio-recommendation engine (mock model version robo-portfolio-recommender-v4.2.1)
- **Audit period:** 2026-04-15 through 2026-07-13 (90 days)
- **Total recommendations analyzed:** 4,969
- **Baseline window:** first 30 days (1,572 recommendations)
- **Regulatory framework applied:**
  - **SEC Regulation Best Interest 17 CFR 240.15l-1** (2020, active enforcement 2024-2026) -- best-interest recommendation duty
  - **Investment Advisers Act of 1940 Section 206** -- fiduciary duty of loyalty + care
  - **FINRA Rule 2111** -- suitability (customer-specific, quantitative, reasonable-basis)
  - **SEC Marketing Rule 206(4)-1** (2022, active enforcement) -- AI-generated marketing content in scope
  - **DOL PTE 2020-02** (Investment Advice Fiduciary, reinstated + strengthened 2025) -- rollover recommendations
  - **SEC Form ADV Part 2A** AI disclosure requirements (2026 amendment)
  - **State Blue Sky laws** -- 46 states w/ RIA-specific AI-adoption reporting requirements 2025-2027
  - **CFP Board revised Code of Ethics 2026** -- AI-in-planning disclosure standard
  - **SEC 2026 Exam Priorities** -- AI / predictive-analytics specifically flagged

---

## 2. Sensor summary

- **Sensor version:** contrarianAI-distsensor-v0.9
- **Sensor family:** distributional-shape (independent of production model family)
- **Independence:** sensor operates on the portfolio-recommendation AI's output stream only. Distinct from the production model family. Different mathematical basis (distributional-shape statistics vs gradient-boosted classifier / neural allocation model). Different retention pipeline. contrarianAI holds no equity, employment, or vendor relationship with any robo-advisor / TAMP / OCIO / portfolio-management platform vendor.

- **Detection thresholds active:**
  - Mean-shift threshold: 1.5 standard deviations from baseline
  - Variance-ratio threshold: 1.5x baseline
  - KL-divergence threshold: 0.2
  - Cohort-differential threshold: 12% shift across cohorts

---

## 3. Findings summary

- **Total drift events detected:** 9
- **High severity:** 8
- **Medium severity:** 1
- **First drift day (post-baseline):** Day 44
- **Critical cohort-differential signal flagged:** NO

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## 4. Overall determination

**CATEGORY C -- INSUFFICIENT FOR ATTESTATION**

Multiple high-severity distributional-drift events detected in the AI portfolio-recommendation engine across the audit period. RIA should not represent the portfolio-recommendation AI as SEC Regulation Best Interest 17 CFR 240.15l-1-defensible or SEC-exam-ready until named remediation completes and independent re-audit confirms closure.

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## 5. The 3 fix-first items scoped to this AI surface

1. **Freeze or hold-for-manual-review** all post-drift AI-generated portfolio recommendations in Cohort R3 (age 50-65, mid-net-worth, conservative near-retirees) pending Reg BI 15l-1 best-interest validation. Document the pause + resumption criteria in the firm's compliance program + Written Supervisory Procedures.
2. **Retain evidence artifacts** for every portfolio recommendation in the audit period -- client onboarding data + recommendation state + model version + decision hash. **SEC Books & Records Rule 204-2 requires 5-year retention minimum** (first 2 years in easily accessible place). Reg BI documentation requirements independently obligate retention of the best-interest analysis basis. Deletion holds should be issued now.
3. **Add adaptive drift-monitoring** to production portfolio-recommendation pipeline. Annual compliance review is insufficient given the between-audit blind spot where SEC exam findings, State securities-commissioner action, FINRA enforcement inquiries, and client-arbitration exposure compound silently. Real-time cohort-differential monitoring is the operational fix.

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## 6. Detailed drift events

Each event includes date, sensor type, affected client cohort, severity, and plain-language detail describing what the sensor observed in the portfolio-recommendation AI's decision stream.

### Day 44 (2026-05-29) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=2.457). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.4574

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=3.926). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 3.9259

### Day 58 (2026-06-12) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=3.411). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 3.4107

### Day 58 (2026-06-12) -- cohort_distribution_divergence (cohort R5)
- **Severity:** high
- **Detail:** Cohort R5 score distribution shape has diverged from baseline (KL=0.911). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.9106

### Day 65 (2026-06-19) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=2.256). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.2562

### Day 65 (2026-06-19) -- cohort_distribution_divergence (cohort R4)
- **Severity:** medium
- **Detail:** Cohort R4 score distribution shape has diverged from baseline (KL=0.395). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3953

### Day 72 (2026-06-26) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=3.614). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 3.614

### Day 79 (2026-07-03) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=2.988). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.9876

### Day 86 (2026-07-10) -- cohort_distribution_divergence (cohort R3)
- **Severity:** high
- **Detail:** Cohort R3 score distribution shape has diverged from baseline (KL=3.835). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 3.8345

---

## 7. Counterparty-question rehearsal

**The question SEC exam staff, State securities-commissioner investigators, FINRA enforcement, or client-arbitration counsel will demand the RIA answer:**

> "Reproduce this AI-generated portfolio recommendation from Day 47 including the client's stated objective, risk tolerance, account balance, model version at decision-time, allocation-score, and lane routed -- as a defensible record we can produce for SEC exam response, FINRA enforcement inquiry, state securities-commissioner investigation, or client arbitration."

**Sample portfolio-recommendation decisions from Day 47:**

| Client ID | Allocation Score | Lane | Model Version | Decision Hash |
|-----------|------------------|------|---------------|---------------|
| CL-002534 | 38.46 | Balanced Managed | robo-portfolio-recommender-v4.2.1 | `rpr-002534` |
| CL-002533 | 53.69 | Balanced Managed | robo-portfolio-recommender-v4.2.1 | `rpr-002533` |
| CL-002532 | 63.24 | Balanced Managed | robo-portfolio-recommender-v4.2.1 | `rpr-002532` |
| CL-002531 | 68.56 | Active Alpha / High Fee | robo-portfolio-recommender-v4.2.1 | `rpr-002531` |
| CL-002525 | 48.01 | Balanced Managed | robo-portfolio-recommender-v4.2.1 | `rpr-002525` |

**Reproduction status:** all 5 sampled Day-47 portfolio-recommendation decisions can be reproduced with defensible-records match: model version pinned, decision hash cryptographically bound, input data (client cohort + account type + stated objective + account balance + years-to-horizon at decision-time) retained via decision-provenance record. Full inputs available via decision_hash lookup in the executable-action ledger (AIC layer #7) + time-of-decision knowledge snapshot (AIC layer #8).

**Retention:** artifacts retained per SEC Books & Records Rule 204-2 (5-year minimum, first 2 years in easily accessible place) + Reg BI best-interest-analysis-basis documentation obligation.

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## 8. What full Enterprise Attestation adds beyond this Snapshot

This $499 Snapshot addresses ONE AI system (portfolio-recommendation engine) at the SAMPLE RIA. A full Enterprise Attestation ($35-55K / 3-6 weeks) covers:

- **All 8 AIC layers** (this Snapshot covers layers 1-4 + 6; Enterprise adds layers 5, 7, 8)
- **Full AI surface inventory** (portfolio-recommendation + client-onboarding KYC extraction + financial-planning narrative generation + marketing-content generation + performance-attribution commentary + email/chat client-communication AI -- every AI-touched surface)
- **Signed regulator-facing attestation deliverable** (this Snapshot = internal-use PDF; Enterprise = signed statement suitable for SEC exam submission, Form ADV Part 2A AI-disclosure support, State securities-commissioner response, FINRA enforcement production, or client-arbitration exhibit)
- **On-site scoping session with your team** (this Snapshot = data-driven only; Enterprise = on-ground with your CCO + Managing Principal + CIO + IT)
- **30/60/90 remediation roadmap w/ named owners + milestones**
- **Adaptive drift-monitoring implementation guidance**
- **Re-audit cadence recommendation tied to your SEC exam cycle + Form ADV annual amendment**

**Baseline Audit ($2,500 / 5 days) is the intermediate tier** -- gap map + measurable test + 30/60/90 roadmap on ONE AI surface. Snapshot credit applies to Baseline OR Enterprise upgrade within 30 days.

**Big-4 consultancy equivalent for the Enterprise tier: $200K-$1M+.** Same deliverable outcome, different price + delivery model. contrarianAI ships at 5-10% of that cost by focusing scope on independent-verifier attestation rather than full advisory-consulting scaffolding.

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## 9. Independent-verifier declaration

I, Kevin Luddy, as principal of contrarianAI LLC, personally attest that:

1. contrarianAI is not an employee, contractor, equity holder, or vendor of the audited RIA or any of its portfolio-management platform vendors (Orion, Envestnet, Black Diamond, Tamarac, Addepar, or any similar), robo-advisor engines (Betterment for Advisors, Wealthfront, SigFig, or any similar), TAMP / OCIO providers, or third-party AI-recommendation vendors.
2. Assessment tools (distributional-shape sensor, retrieval-auditor, tool-call-grader, prompt-drift-sensor, predictor-corrector, router-drift-sensor) use model families and evaluation methods distinct from those in production at the audited RIA.
3. This Snapshot is based on the data-sample submitted by the buyer. Changes to the audited RIA's infrastructure after the Snapshot date are outside this Snapshot's scope.
4. Underlying audit evidence is retained for the Snapshot horizon and will be made available for legitimate SEC / State securities-commissioner / FINRA / auditor / counterparty inquiry with appropriate legal process.

**Signed:** Kevin Luddy, Principal, contrarianAI LLC
**Date:** 2026-07-22
**Retention key:** `7691521283eddb6ded4f0b36`

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## 10. Charts + artifacts

- `chart_ria_distributional_drift.png` -- 7-day rolling mean allocation-recommendation score per client cohort over the 90-day period
- `chart_ria_baseline_vs_drifted.png` -- Cohort R3 baseline vs recent allocation-score distribution (largest observed drift)
- `chart_ria_lane_shift.png` -- Allocation-lane rate per cohort, baseline vs recent
- `ria_drift_analysis.json` -- machine-readable analysis + event metadata
- `ria_dataset.csv` -- synthetic 90-day portfolio-recommendation input data
- `ria_agent_decisions.csv` -- portfolio-recommendation AI output + scored allocation decisions

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*End of Snapshot CAI-RIA-GEN-DEMO-7691521283eddb6ded4f0b36*

**== END OF SAMPLE -- YOUR ACTUAL $499 SNAPSHOT WILL LOOK STRUCTURALLY IDENTICAL BUT WITH YOUR RIA'S DATA + AI SYSTEM + JURISDICTIONAL FOOTPRINT + REGULATORY-FRAMEWORK-CITATIONS ==**

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## To purchase YOUR RIA's $499 Snapshot:

1. Visit: **contrarianai-landing.onrender.com/samples/ria/**
2. Click **Buy $499** -- Stripe direct-buy, no procurement approval
3. Complete intake form (~2 min): name your AI system + submit 100-1000 anonymized portfolio-recommendation records
4. Report PDF delivered via email within 3 business days

**Snapshot credit ($499) applies to upgrade within 30 days:**
- **Baseline Audit** ($2,500 / 5 days) -- Snapshot credit = $499 off
- **Enterprise Attestation** ($35-55K / 3-6 weeks) -- Snapshot credit = $499 off

Book a call: **cal.com/kevin-luddy-0dlzuu**