A network approach to understanding insurance fraud: Project receives DFG-funding

23 December 2020, by Martin Spindler

Photo: Gerd Altmann/Pixabay

Insurance fraud is often committed by networks such as mechanics that work together with fraudsters and lawyers, or doctors who collude with pharmacies. But understanding and analyzing these network structures is often difficult using standardized methods.

In the project "Mathematical methods and algorithms for learning-effective embeddings of semi-structured information for anomaly detection problems", which was granted German Research Foundation (DFG) funding for 2021-2024, deep-learning architectures are being developed that can use network-structures/graphs as data.

Prof. Dr. Martin Spindler and Prof. Dr. Evgeny Burnaev from the Skolov Institute of Technology in Russia submitted the proposal as part of a joint call by the DFG and the Russian Foundation for Basic Research (RFBR).