Harnessing solutions to enhance the monitoring and use of early warning indicators (EWIs) for credit risk management
Early warning indicators can be a vital tool to ensure that banks are able to anticipate events which have a significant impact on their credit risk. However, there has been little standardisation in the regulation of early warning systems, and there remains a great deal of flexibility and divergence in terms of what individual firms have in place to monitor these EWIs. While some may have highly sophisticated techniques and large-scale systems in place, other institutions rely on much simpler frameworks. Utilising integrated systems for monitoring early warning indicators can help to resolve this issue. Currently, an exciting trend in this area is the use of machine learning techniques and text mining to analyse news data at large scale, a very time-consuming task if manpower was devoted to it.
· Evaluate the importance of EWIs within credit risk management and how they can help to anticipate shocks and drive credit risk strategy
· Determine the potential benefits of integrated early warning systems which utilise machine learning and text mining
· Discover patterns in corporate news data: for example, measure the effect of the COVID crisis on different industries, also including private firms
Speakers:
Nick Popov, Senior Credit Risk Expert, Rabobank
Markus Kantor, Credit Risk Manager, OP Financial Group
Dr. Janis Bauer, Risk Methods Senior Expert, RSU
Tobias Noll, Senior Relationship Manager, RSU
