Abstract
In banking risk management, there is a growing demand for considering network risks of customer entities. In this paper we introduce a method where we use classic epidemic modelling methods for predict the spread of defaults through the population. We also use modelling methods to describe the relations between clients which lead us to a graph with fuzzy edges where the edge vagueness represents the transmitting power of the infection. Testing new approach on real bank data of corporate entities (SMI segment) shows significant separating power of the new model. Moreover, new approach is also very useful to find the endangered entities in special economic situation.
Paper Type
Poster
DOI
10.62036/ISD.2022.16
Epidemic Risk Models on Graphs with Fuzzy Edges
In banking risk management, there is a growing demand for considering network risks of customer entities. In this paper we introduce a method where we use classic epidemic modelling methods for predict the spread of defaults through the population. We also use modelling methods to describe the relations between clients which lead us to a graph with fuzzy edges where the edge vagueness represents the transmitting power of the infection. Testing new approach on real bank data of corporate entities (SMI segment) shows significant separating power of the new model. Moreover, new approach is also very useful to find the endangered entities in special economic situation.
Recommended Citation
Darida, S. (2022). Poster: Epidemic Risk Models on Graphs with Fuzzy Edges. In R. A. Buchmann, G. C. Silaghi, D. Bufnea, V. Niculescu, G. Czibula, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Artificial Intelligence for Information Systems Development and Operations (ISD2022 Proceedings). Cluj-Napoca, Romania: Risoprint. ISBN: 978-973-53-2917-4. https://doi.org/10.62036/ISD.2022.16