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AI in Markets Checklist

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 ITEMSPublished May 26, 2026Live Content
By AI Fin Hub Research · AI Fin Hub Team

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Checklist Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Inventory and purpose

3 items
Use The ToolComparators

Model Selector for Finance

Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.

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Section 2

Phase 2: Assumptions and limitations

3 items
Use The ToolPlaygrounds

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.

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Use The ToolCalculators

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.

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Section 3

Phase 3: Independent validation

3 items
Use The ToolPlaygrounds

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.

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Use The ToolPlaygrounds

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.

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Section 4

Phase 4: Ongoing governance

3 items

Pro 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 & References

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Hallucination Detection

Detecting LLM hallucinations in financial outputs: the verifiable-claim approach, citation grounding, and cross-model agreement signals that work.

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Model Drift

Model drift: when an LLM's behavior changes between calls, versions, or weeks. The monitoring stack that catches it before production breaks.

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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.

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Planning estimates only — not financial, tax, or investment advice.