Data Science for Controlling

Statistical methods are very important tools for analyzing a great amount of data. Therefore, we support and assist you in the validation of your data by means of statistical modeling in order to detect conspicuities and anomalies.

Application of statistical validation of data can be found in many various fields of controlling, from classical risk management to detection of conspicuities and fraud cases. At the ITWM, we work with classical techniques of statistics such as regression models and cluster analysis which we combine with modern methods from Data Science and Machine Learning.

Example Projects

 

Fraunhofer Lighthouse Project ML4P

Machine Learning for Production

In the Fraunhofer Lighthouse Project, seven Fraunhofer Institutes bundle their extensive experience in the field of Machine Learning.

 

Billing Fraud in Health Care

Our goal is the evaluation of guaranteed damage in billing fraud in the health care sector.

 

Fraud and Anomaly Detection

Mathematical-statistical procedures make an important contribution to uncovering fraudulent activities and to quantify corresponding risks from fraud.

 

Credit Risk Management Based on News

Our system incorporates the latest news to improve forecasting quality. Machine Learning classifies the news.

 

Data Science in the Automotive Industry

Data science and machine learning are essential technologies for the optimization of processes and financial products in the automotive industry of the future.