Machine Learning: ERGO and TUM students benefit from joint workshop

Although machine learning as an important area of computer science is based on math-ematical theories, progress in this trend topic is driven primarily by practical requirements. In a three-day workshop students from the Technical University of Munich (TUM) and experts from ERGO therefore brought each other up to date with the latest research and insurance industry practice.

Computer programs based on machine learning use algorithms to independently find solutions for new and unknown problems, which makes machine learning an integral part of artificial intelligence. The fact that this specialist area also offers many promising starting points for an insurer was impressively demonstrated at the exchange in Düsseldorf at the end of August: by providing TUM students with data from the field of car insurance, for example, ERGO colleagues were able to develop methods for estimating loss frequencies for the young talents – and present them to their practical partners during the workshop.

Meanwhile, a TUM graduate demonstrated how machine learning can also help in health insurance as part of his master's thesis, the results of which he also presented at the workshop. The three-day exchange was led by machine learning expert Alik Sokolov, who as a lecturer at the University of Toronto and former Deloitte consultant also brings both perspectives to the workshop.

ERGO has been an active partner of the TUM Chair of Mathematics of Finance for many years and is strongly committed to innovation in the insurance and finance sectors through research projects, sponsorship of theses and guest lectures.