Multilayer Network Analysis for Improved Credit Risk Prediction
This addresses credit risk assessment for lenders by improving prediction accuracy, though it is incremental as it builds on existing network methods.
The paper tackled credit risk prediction by developing a multilayer network model that accounts for multiple borrower connections, showing significant predictive gains from including centrality and multilayer PageRank variables in an agricultural lending context.
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.