  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.
