Researchers in the »Financial Mathematics« department, together with industry partners, have developed a software for the detection of anomalies and fraud, especially in accounting. The product enables users to find and assess various types of anomalies (outliers) in very large data sets – usually, in accounting data.
Identifying Anomaly Types – Developing Efficient Algorithms
Our software lets us define various anomaly types tailored for the actual use case. The aim is to facilitate fraud detection, especially in the case of accounting. In almost all projects, we detect mathematically simple anomalies, such as duplicate statements. However, deviations from the Benford distribution are also found and examined. Furthermore, we implemented a number of clustering methods that find, for example, highly deviating invoices in a large set. We also apply machine learning methods to define detection algorithms. In all cases, the development of efficient algorithms is an essential research task in the associated projects.