Data analysis and finance reporting with AI

Day 1: Theory and basics

Introduction to AI-supported data analysis in accounting

• Basics of artificial intelligence and its role in data analysis.
• Relevance of AI in accounting: Potential and possible applications.

Theoretical foundations of data analysis methods in accounting

• Statistical methods: Descriptive and inferential statistics.
• Regression analysis for financial data.
• Time series analysis for forecasting financial developments.

Application of AI methods in data analysis

• Supervised learning: classification algorithms and regression methods.
•  Unsupervised learning: cluster analysis and anomaly detection.
• Deep learning and neural networks.

Automated reporting with AI

• Natural Language Processing (NLP) and Natural Language Generation (NLG).
• Advanced data visualization techniques.
• Real-time reporting and automation of reports.

Data quality and data management

• Ensuring data quality in accounting.
• Strategies for effective data management.

Day 2: Practice and application

Hands-on implementation

• Financial data analysis with Python - data preparation, visualization and pattern recognition.
• AI-supported reporting with Power BI - creation of dynamic dashboards.
• Automated reporting with NLG tools - text generation from financial data.

Implementation of AI solutions in the company

• Strategic planning and project management for AI projects.
• Change management and the involvement of employees in the digital transformation.

Best practices and case studies

• Successful AI projects in accounting: Insights from the field.
• Trends and future developments in AI-supported data analysis.

Ethical and legal aspects

• data privacy and compliance in dealing with AI and financial data.
• Transparency and accountability in the use of AI.