With predictive analytics, controllers can better assess future financial developments and make strategic decisions early on. Through the use of machine learning, historical data is analyzed to make precise forecasts. Employees benefit from data-based insights that minimize risks and identify opportunities. This makes controlling more proactive, efficient and makes a significant contribution to long-term corporate planning.

Predictive analytics in controlling uses machine learning and historical data to predict future trends and financial developments. This enables a proactive management of corporate resources and the timely identification of risks and opportunities.

Benefits for you as a company:

• Proactive planning: Early identification of trends and risks for better budgeting and resource allocation
• More accurate forecasts: Using algorithms to create accurate financial forecasts and avoid surprises.
• Optimising liquidity: Early identification of financial bottlenecks and timely adjustments to the financial strategy.
• Improved decision-making: Data-driven insights for well-founded, future-oriented business strategies.
• Increased efficiency: Automating analysis processes saves time and resources in financial planning.

1. Introduction to predictive analytics in controlling

• What is predictive analytics?: Basics and definition of predictive analytics and how it is used in controlling
• Why predictive analytics in controlling?: Benefits and benefits of predictive analytics for controlling, e.g. for forecasting and early detection of deviations
• Machine learning and controlling: How machine learning can be used to create forecasts and optimize controlling processes
• Integration into existing processes: How predictive analytics can be integrated into existing controlling processes and which data sources are required

2. Basics of machine learning (machine learning)

• Machine Learning Overview: Introduction to the basic principles of machine learning and the various algorithms that can be used in controlling
• Supervised vs. unsupervised learning: Difference and application of supervised (e.g. linear regression) and unsupervised (e.g. cluster analysis) learning methods in controlling
• Models and Algorithms for Predictive Analytics: Important algorithms such as regression models, decision trees and time series analyses for forecasting in controlling

3. Data preparation and preparation

• Data sources for predictive analytics: Which data sources are relevant for predictive analytics in controlling (e.g. financial data, historical controlling data, external market or economic data)
• Data cleansing and preparation: Steps to prepare and clean data so that it can be used for machine learning models
• Feature Engineering: How to select the right features for the machine learning model to improve forecast accuracy
• Data quality and security: How the quality and security of data is ensured, particularly when analyzing sensitive financial data

4. Create and validate predictive models

• Model selection and training: Selecting the right machine learning model and training with historical data
• Evaluation of models: How models are validated to test forecast accuracy, e.g. through cross-validation and error metrics such as RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error)
• Overfitting and underfitting: What these terms mean and how they can be avoided when building models
• Hyperparameter tuning: Optimizing model parameters to get better predictions

5. Application of predictive analytics in controlling

• Sales forecasting and cash flow management: How predictive analytics can be used to predict revenue, spending, and cash flow to avoid financial bottlenecks
• Budget variances and early warning systems: How predictive analytics can help identify budget discrepancies at an early stage and take proactive measures
• Risk management and scenario analysis: Application of predictive analytics to model risks and create what-if scenarios to identify potential risks in controlling
• Optimizing financial processes: Improving the accuracy and efficiency of forecasts and reporting in controlling through the use of predictive analytics

6. Visualization of forecasts and results

• Predictive visualization in dashboards: How controllers can use Power BI and other visualization tools to clearly present forecasts and results
• Interactive reports and dashboards: Creating interactive dashboards to make forecasts understandable and accessible to different stakeholders
• Trend analyses and forecast graphics: Presentation of trends and forecasts in easy-to-understand charts and graphics

7. Integration of predictive analytics into controlling software

• Connecting machine learning with ERP systems: How predictive analytics can be integrated into ERP systems (e.g. SAP) to automatically create forecasts and analyze financial data
• Automate forecasts: How predictive analytics can be used to generate forecasts in real time and automatically integrate them into financial reports
• Workflow optimization through automation: Optimizing the controlling workflow through the use of machine learning and predictive analytics to minimize manual processes

8. Best practices and case studies

• Best practices in predictive analytics for controlling: Successful examples and methods that companies have used in controlling to improve their forecasts with predictive analytics
• Case study: Sales forecasting in practice: A case study that shows how predictive analytics was used to predict revenue in a company and what results were achieved
• Sources of error and opportunities for improvement: How to avoid common sources of error when using predictive analytics and continuously improve models

9. Challenges and risks of predictive analytics in controlling

• Data uncertainty and distortion: Challenges in handling inaccurate or distorted data and how to mitigate these risks
• Complexity of models: How to deal with the complexity of machine learning models and make them understandable and usable for everyday controlling
• Acceptance by the controlling team: How to promote the acceptance of predictive analytics in the controlling team and among stakeholders and ensure that forecasts are integrated into decision-making

10. Outlook and future of predictive analytics in controlling

• Future Trends in Predictive Analytics: How predictive analytics and machine learning are evolving in controlling and what new opportunities are emerging for controllers
• AI and automation in controlling: How artificial intelligence and automation further improve predictive analytics and make the controlling process more efficient
• Integrate big data and cloud analytics: How the use of big data and cloud technologies can increase the performance and scalability of predictive analytics in controlling

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