Data analysis and finance reporting with AI

Further training according to KI-VO Art. 4 - Obligation to provide evidence of AI competence

Day 1: Direct application - Case study-based introduction with LLMs

Use LLMs directly in financial management:

• Introduction to ChatGPT Plus & Claude Pro for financial users.
• Best practices in prompt engineering for tables, time series and texts.
• Differences and synergies of both tools in accounting.

Practice-oriented micro case studies with real financial data

• Time series analysis (monthly sales, cost development).
• Table extraction from annual reports (PDF).
• Forecast creation with scenario logic.
• Sentiment analysis in texts (management report, analyst commentary).

Theory in the context of the case studies

• Structure, training logic and limits of LLMs.
• Quality assurance: How do you recognize incorrect results through plausibility checks?
• Data protection, governance and ethical aspects of LLM use.

Day 2: Training project "From raw data set to dashboard with LLMs"

Objective:
You create a strategic management dashboard that is automatically filled with the following content by LLMs:

• Key performance indicators (sales, margin, EBIT, cash flow, etc.).
• Forecast data for 3, 6, 12 months.
• Commented developments and outliers.
• Summarized assessment and recommendations for action.
• Visual structure for stakeholders (text structure by LLM).

Procedure:

1. Provide data basis (Excel & report file).
2. Extraction and interpretation with Claude & ChatGPT.
3. Perform and integrate forecast simulations.
4. Generate automatic text modules for management communication.
5. Compile output into a readable, presentable format (Word, PDF or slides).

In addition:

• Reflection: What tasks can an LLM take on in the dashboard process?
• Discussion: How much automation makes strategic sense?
• Transfer plan: How to introduce an AI dashboard into your business.

Note: For active collaboration, we strongly recommend an active subscription to ChatGPT Plus and Claude Pro.