{
  "platformId": "chatgpt-work",
  "roleSlug": "financial-and-investment-analysts",
  "directAnswer": "For financial analysts, ChatGPT Work is strongest on the computational spine of the job: scenario forecasts with every assumption on its own line, model red-teaming, comparison tables built only from the filings and feeds you supply, and report drafts where every market claim carries its source and date. Two honesty notes shape this guide. First, this guide holds the recommendation line human: what counts as advice and suitability is defined by your regulator and compliance function, not this page, so the agent frames and computes while you recommend. Second, O*NET has not yet published task ratings for this occupation, so the centrality figures below read Unavailable rather than invented; usage estimates, projected from economy-wide data rather than directly observed, put forecast analysis among the most-used AI tasks anywhere, and the field is growing.",
  "topUseCases": [
    {
      "interventionId": "task-21593:scenario-forecast-with-spine:recommend",
      "taskId": "task-21593",
      "task": "Inform investment decisions by analyzing financial information to forecast business, industry, or economic conditions.",
      "workflowId": "scenario-forecast-with-spine",
      "authorityMode": "recommend",
      "centralityScreen": {
        "value": null,
        "importance": null,
        "occupationalRelevance": null,
        "frequency": {
          "kind": "unavailable",
          "reason": "O*NET 30.3 publishes task statements for 13-2051.00 while task-rating collection remains in progress; no importance, relevance, or frequency rows are available.",
          "sourceField": "onet-30.3:Task Ratings[13-2051.00]=no-rows"
        }
      },
      "agentFit": {
        "value": 0.9025,
        "sourceField": "rule:digital-feasibility-and-capability-fit-single-count.v2"
      },
      "distribution": {
        "value": 0.9,
        "sourceField": "roles-kv:aiPlaybook.delegatedTasks[task-21593].observed-distribution"
      },
      "evidenceConfidence": "unavailable",
      "withinGateScore": null,
      "gate": {
        "status": "limited",
        "reason": "Authority remains below an approval-required intervention."
      },
      "capabilityIds": [
        "connected-apps-files",
        "deliverable-generation",
        "long-running-projects"
      ],
      "expectedArtifact": "Base, bull, and bear scenarios with named assumptions, sensitivities, and an as-of date on every input",
      "humanReviewPoint": "The investment judgment and any recommendation are yours; this guide keeps suitability human, with the specifics defined by your compliance function.",
      "riskLevel": "high"
    },
    {
      "interventionId": "task-21594:scenario-forecast-with-spine:assist",
      "taskId": "task-21594",
      "task": "Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.",
      "workflowId": "scenario-forecast-with-spine",
      "authorityMode": "assist",
      "centralityScreen": {
        "value": null,
        "importance": null,
        "occupationalRelevance": null,
        "frequency": {
          "kind": "unavailable",
          "reason": "O*NET 30.3 publishes task statements for 13-2051.00 while task-rating collection remains in progress; no importance, relevance, or frequency rows are available.",
          "sourceField": "onet-30.3:Task Ratings[13-2051.00]=no-rows"
        }
      },
      "agentFit": {
        "value": 0.9025,
        "sourceField": "rule:digital-feasibility-and-capability-fit-single-count.v2"
      },
      "distribution": {
        "value": 0.9,
        "sourceField": "roles-kv:aiPlaybook.delegatedTasks[task-21594].observed-distribution"
      },
      "evidenceConfidence": "unavailable",
      "withinGateScore": null,
      "gate": {
        "status": "limited",
        "reason": "Authority remains below an approval-required intervention."
      },
      "capabilityIds": [
        "connected-apps-files",
        "browser-research-actions"
      ],
      "expectedArtifact": "Interpretation walkthrough of supplied data with each reading tied to its series and window",
      "humanReviewPoint": "You judge what the data means for the program; the agent explains what it shows.",
      "riskLevel": "high"
    }
  ],
  "workflows": [
    {
      "id": "scenario-forecast-with-spine",
      "goal": "Build a scenario forecast whose assumption spine is fully visible, from data the analyst supplies, with the judgment left human.",
      "inputs": [
        "Your data: series, filings, model inputs, with their vintages",
        "Your priors and constraints for the scenarios"
      ],
      "connectedSystems": [
        "Spreadsheet or model surface",
        "Document repository"
      ],
      "steps": [
        "Stamp every input with its as-of date and source; the occupational data here carries labor-market evidence, not live market data, and the draft says so.",
        "Build base, bull, and bear scenarios with every assumption on its own line.",
        "Run sensitivity on the three assumptions doing the most work.",
        "State in-band what the analysis cannot see; ship scenarios, never point predictions with false precision."
      ],
      "expectedArtifact": "Scenario set with assumption spine, sensitivities, and stated blind spots",
      "starterPrompt": "Here is my data with vintages, and my priors. Build base, bull, and bear scenarios with every assumption on its own line, run sensitivity on the three assumptions that matter most, and stamp an as-of date on every input. State what the analysis cannot see. If I ask you to predict what the Fed will do, decline and give me the scenario framework with my own priors instead.",
      "humanReviewPoint": "Scenario selection, the house view, and anything that becomes a recommendation are yours.",
      "commonFailure": "A forecast that hides stale inputs or ships a point prediction whose precision the data cannot support."
    },
    {
      "id": "report-draft-cited-world",
      "goal": "Turn finished analysis into a report draft where every market claim is tagged to its source and date.",
      "inputs": [
        "Your completed analysis and charts data",
        "The sources each claim may rest on"
      ],
      "connectedSystems": [
        "Document repository",
        "Chart data source"
      ],
      "steps": [
        "Draft the report with every market claim tagged to its source and date.",
        "Spec charts from the actual series, trends stated with their window.",
        "Flag anything asserted in the analysis that has no source yet; flags are for you, not for silent fixing.",
        "Stop at the draft: distribution and any recommendation language are human."
      ],
      "expectedArtifact": "Report draft with claim-level source tags and a no-source flag list",
      "starterPrompt": "Turn my analysis into the report draft: every market claim tagged to its source and date, charts specced from the actual series, trends stated with their window. Flag anything I asserted that has no source yet, and leave the flags for me.",
      "humanReviewPoint": "You clear the flags, own the recommendation language, and control distribution.",
      "commonFailure": "An unsourced assertion from the analyst's notes passing through the draft as a market fact."
    }
  ],
  "setupRecommendations": [
    {
      "item": "Connect your data feeds and document repository read-only; the agent computes from what you supply and never fetches numbers from memory.",
      "rationale": "As-of data discipline is the product: a governed analyst surface differs from a chatbot with a spreadsheet precisely in where its numbers come from.",
      "capabilityIds": [
        "connected-apps-files",
        "approval-enterprise-controls"
      ]
    }
  ],
  "prompts": [
    {
      "id": "forecast-spine",
      "title": "Build the forecast, show the spine",
      "prompt": "Here is my data. Build the base, bull, and bear scenarios with every assumption on its own line, sensitivity on the three that matter most, and an explicit as-of date on every input.",
      "expectedArtifact": "Scenario set with assumption spine",
      "humanReviewPoint": "The house view and any recommendation are yours."
    },
    {
      "id": "interrogate-model",
      "title": "Interrogate my model",
      "prompt": "Here is my model logic. Red-team it: which assumptions are doing the most work, what breaks first, and what would a skeptical investment-committee member ask?",
      "expectedArtifact": "Red-team memo on the model",
      "humanReviewPoint": "You decide what to change; the memo pressures, it does not decide."
    },
    {
      "id": "comp-the-sector",
      "title": "Comp the sector from supplied filings",
      "prompt": "From the filings and data I provide, build the comparison table for these securities: metrics, quality flags, and each figure's vintage. No numbers from memory.",
      "expectedArtifact": "Comparison table with vintage per figure",
      "humanReviewPoint": "Relative-value judgment is yours; the table carries no view."
    },
    {
      "id": "report-cite-world",
      "title": "Draft the report, cite the world",
      "prompt": "Turn my analysis into the report draft: every market claim tagged to its source and date, charts specced from the actual series, trends stated with their window. Flag anything I asserted that has no source yet.",
      "expectedArtifact": "Cited report draft with flag list",
      "humanReviewPoint": "You clear the flags and own the recommendation language."
    },
    {
      "id": "variance-walk",
      "title": "Walk the variance against prior period",
      "prompt": "Here are this period's numbers and the priors. Walk each material variance with its source spans, separate mechanical drivers from judgment calls, and leave the judgment calls to me with the evidence laid out.",
      "expectedArtifact": "Variance walk with drivers separated",
      "humanReviewPoint": "Judgment-call variances resolve on your read, not the draft's."
    },
    {
      "id": "honest-outlook",
      "title": "Give me the honest read on this occupation",
      "prompt": "Published exposure estimates for this role span a wide range across eras and methods, measured usage puts forecast analysis among the highest AI-use tasks anywhere, and the field is still growing. Explain what each measure captures and what that combination actually means for how I should work. Keep the measures separate, and note that task-level ratings for this occupation are not yet published.",
      "expectedArtifact": "Honest occupational outlook with measures kept separate",
      "humanReviewPoint": "Career decisions stay yours; this is orientation, not advice."
    }
  ],
  "humanOwnedResponsibilities": [
    "Recommendations and suitability: this guide keeps them human; your regulator and compliance function define the specifics",
    "Data vintage validation and live-market sourcing: the agent computes from your feeds and never from memory",
    "Trading, rebalancing, spending, and every external commitment",
    "The house view: scenarios frame it, sensitivity pressures it, you call it",
    "Clearing every no-source flag before a report moves"
  ],
  "risks": [
    {
      "id": "stale-or-false-precision",
      "level": "high",
      "description": "A model can hide stale inputs behind fresh formatting, or ship a point prediction whose implied precision the data cannot support, and a fluent draft can read as advice.",
      "mitigation": "As-of dates on every input, scenarios instead of point predictions, stated blind spots in-band, no numbers from memory, and the recommendation line held human."
    }
  ],
  "sources": [
    {
      "id": "occupation-financial-and-investment-analysts",
      "title": "Financial and Investment Analysts occupational render model",
      "url": "https://singulariki.com/roles/financial-and-investment-analysts",
      "publisher": "gold.onet_role_starter_surface",
      "lastReviewedAt": "2026-06-03"
    },
    {
      "id": "openai-chatgpt-work",
      "title": "ChatGPT Work and Codex",
      "url": "https://help.openai.com/en/articles/20001275-chatgpt-work-and-codex",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-work-deliverables",
      "title": "Creating and editing documents, spreadsheets, and presentations with ChatGPT Work",
      "url": "https://help.openai.com/en/articles/20001278-creating-and-editing-documents-spreadsheets-and-presentations-with-chatgpt-work",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-work-cloud-browser",
      "title": "Using cloud browser in ChatGPT",
      "url": "https://help.openai.com/en/articles/20001280-using-cloud-browser-in-chatgpt",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-work-desktop-browser",
      "title": "Using the built-in browser in the ChatGPT desktop app",
      "url": "https://help.openai.com/en/articles/20001277-using-the-built-in-browser-in-the-chatgpt-desktop-app",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-chatgpt-skills",
      "title": "Skills in ChatGPT",
      "url": "https://help.openai.com/en/articles/20001066",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-chatgpt-scheduled-tasks",
      "title": "Scheduled Tasks in ChatGPT",
      "url": "https://help.openai.com/en/articles/10291617-tasks-in-chatgpt",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    },
    {
      "id": "openai-workspace-agents",
      "title": "ChatGPT Workspace Agents for Enterprise and Business",
      "url": "https://help.openai.com/en/articles/20001143",
      "publisher": "OpenAI",
      "lastReviewedAt": "2026-07-11"
    }
  ],
  "freshness": {
    "platformVersion": "2026-07-09-launch",
    "platformReviewedAt": "2026-07-11",
    "occupationalDataVersion": "O*NET 30.3",
    "occupationalDataGeneratedAt": "2026-06-03T05:05:52.989984+00:00"
  },
  "quality": {
    "uniqueOccupationalOrTaskEvidence": false,
    "validPlatformSpecificWorkflow": true,
    "clearOutputArtifact": true,
    "realCapabilityMatch": true,
    "meaningfulHumanReviewSection": true,
    "sourcesAndFreshness": false,
    "distinctInternalRelationships": true,
    "noUnresolvedFactualOrProductVersionFailure": false,
    "indexable": false
  }
}