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Checklist, in AI in Markets

LLM Model Risk Management Checklist

Regulators treat any model that informs a financial decision as a source of risk, and a large language model is no exception. This checklist adapts established model-risk-management principles to LLM deployments.

12 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Inventory and purpose

3 items

Use the Model Selector for Finance: Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.

Section 2

Phase 2: Assumptions and limitations

3 items

Use the LLM Finance Error Taxonomy: 12 documented LLM-on-finance failure modes (hallucinated ticker, stale price, units, currency, off-by-100, fictional source, more). Paste output, see flags.

Use the Agent Cost Envelope Calculator: Model an LLM research loop end-to-end — steps, tool calls, convergence checks, markets per day — and see per-loop, daily, and monthly cost with cost-cap.

Section 3

Phase 3: Independent validation

3 items

Use the Prompt Regression Tester: Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own keys. Diff outputs, score drift, catch regressions.

Use the Hallucination Detector: Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.

Section 4

Phase 4: Ongoing governance

3 items

Tips

Small moves that make the checklist easier to finish

Supervisory model-risk guidance was written before LLMs but applies cleanly to them. Treating a language model as somehow exempt from validation is the single biggest governance mistake.
Independent means independent. A validation run by the same person who built the prompt will find what they already expected and miss what they did not.
Pin the version and monitor for drift, because the model under your prompt is not a constant. A silent provider update can change behavior without a single line of your code changing.

Sources

  1. SR 11-7: Guidance on Model Risk Management , Board of Governors of the Federal Reserve System (2011)
  2. Artificial intelligence in UK financial services 2024 , Bank of England and Financial Conduct Authority (2024)

Keep the topic connected

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  • Learn Model Drift

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  • Learn Regulatory Cost of AI in Finance

    Regulatory cost as a function of jurisdiction, model class, and end-use: the FTC vs NLT distinction and the documentation burden by regime.