AI Practice Controlling: AI Agents and Python

Fundamentals of AI in finance

• How it works: How AI models recognize patterns in financial data and use them for accurate forecasts.
• Potential analysis: Strengths and weaknesses of AI compared to traditional methods of income statement analysis.
• Risk management:identifying typical errors and ensuring the reliability of AI results.

Big Data and Python for controllers

• Mass data: How AI analyzes large data sets that conventional systems struggle to handle.
• No-Code Python: Using the Python programming language efficiently to speed up analyses without having to program yourself – that's what AI does.
• Versatility: Automation of data imports, cleansing, and predictive analytics for modern reporting.

AI agents in everyday controlling

• Definition: What are AI agents and how do they support you as a "digital business partner"?
• Customization: Create AI agents specifically for tasks such as variance analysis or investment controlling.
• Automation: Designing efficient controlling processes with Python and AI agents.

Legal guidelines: The EU AI Regulation

• Compliance: What companies can expect and why AI literacy is becoming a key skill.
• Best practice: Documentation requirements and security requirements for sensitive financial data in the context of risk management.
• Governance: important documents and audit processes for the legally compliant use of AI.