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Automated Financial Reporting and Variance Bridge Generation

Data AnalysisCozeUpdated 2026-05-13

Use Codex in Coze to quickly generate MBRs, reporting packs, variance bridges, model checks, and planning scenarios from raw financial data, boosting efficiency and accuracy for finance teams.

System Prompt
Generate a Python script that takes {financialData} as input and produces a {reportType} report. If {reportType} is 'model check', also validate against {modelName}. Output should be in {outputFormat}.

Variable Dictionary (fill in your AI tool)

This section only explains placeholders. It is not an input form on this website. Copy the prompt, then replace variables in Coze / Dify / ChatGPT.

{financialData}

Raw financial data in CSV or JSON format

Filling hint: replace this with your real business context.

{reportType}

Type of report: MBR, variance bridge, model check, planning scenario

Filling hint: replace this with your real business context.

{modelName}

Name of the financial model to validate against (used only when reportType is 'model check')

Filling hint: replace this with your real business context.

{scenarioParams}

Parameters for planning scenario (used only when reportType is 'planning scenario')

Filling hint: replace this with your real business context.

{outputFormat}

Output format: Excel, PDF, JSON

Filling hint: replace this with your real business context.

Quick Variable Filler (Optional)

Fill variables below to generate a ready-to-run prompt in your browser.

{financialData}

Raw financial data in CSV or JSON format

{reportType}

Type of report: MBR, variance bridge, model check, planning scenario

{modelName}

Name of the financial model to validate against (used only when reportType is 'model check')

{scenarioParams}

Parameters for planning scenario (used only when reportType is 'planning scenario')

{outputFormat}

Output format: Excel, PDF, JSON

Generated Prompt Preview

Missing: 5
Generate a Python script that takes {financialData} as input and produces a {reportType} report. If {reportType} is 'model check', also validate against {modelName}. Output should be in {outputFormat}.

How to Use This Template

Best for

Teams that need faster data analysis output with more stable prompt quality.

Problem it solves

Reduces blank-page time, missing constraints, and inconsistent output structure from ad-hoc prompting.

Steps

  1. Copy the template prompt.
  2. Paste it into your AI tool (Coze / Dify / ChatGPT).
  3. Replace placeholder variables using the dictionary above.
  4. Run and refine constraints based on output quality.

Not ideal when

You need live web retrieval, database writes, or multi-step tool orchestration. Use full workflow automation for that.

Success Case

Input:
{"financialData": "sample.csv", "reportType": "variance bridge", "outputFormat": "Excel"}
Output:
Generated Excel file containing the complete variance bridge analysis, saved to /reports/variance_bridge.xlsx

Boundary Case

Input:
{"financialData": "sample.csv", "reportType": "model check", "outputFormat": "PDF"}
Fix:
Add the 'Actual' column to the input data and rerun.

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Workflow Steps

  1. 1️⃣ Provide {financialData} and {reportType} to Codex to generate a Python script or Excel formulas.

  2. 2️⃣ If {reportType} is 'model check', the script will automatically validate against {modelName} and output the variance.

  3. 3️⃣ If {reportType} is 'planning scenario', the script will use {scenarioParams} to run the scenario and produce results.

  4. 4️⃣ Execute the script to generate a {outputFormat} report file and return the file path to the user.

Constraints

  • When data rows exceed 10,000, consider batch processing or using Spark for preprocessing.
  • Missing key columns (e.g., 'Actual', 'Budget') in the input will cause script errors.

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