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

 

Loss extrapolation

The goal of loss extrapolation in billing fraud is to estimate a resulting damage as exact as possible without examining single cases in detail.

 

Fraud detection

Mathematical-statistical techniques are important tools in detecting fraudulent activities, in corroborating suspected cases and in quantifying corresponding risks from damages based on fraud.