 Multilayer network analysis for improved credit risk predictionMaría Óskarsdóttir and Cristián Bravo Omega, 2021, vol. 105, issue C Abstract: We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between connected borrowers. We develop a multilayer personalized PageRank algorithm that allows quantifying the strength of the default exposure of any borrower in the network. We test our methodology in an agricultural lending framework, where it has been suspected for a long time default correlates between borrowers when they are subject to the same structural risks. Our results show there are significant predictive gains just by including centrality multilayer network information in the model, and these gains are increased by more complex information such as the multilayer PageRank variables. The results suggest default risk is highest when an individual is connected to many defaulters, but this risk is mitigated by the size of the neighbourhood of the individual, showing both default risk and financial stability propagate throughout the network. Keywords: Business analytics; Credit risk; Network Science; Multilayer Networks; Agricultural Lending (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:105:y:2021:i:c:s0305048321001298 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2021.102520 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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