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

 

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.