Multilayer Network Analysis for Improved Credit Risk Prediction

TL;DR

This paper introduces a multilayer network model and a personalized PageRank algorithm to enhance credit risk prediction by capturing complex borrower interactions and structural risks, demonstrating significant predictive improvements in agricultural lending.

Contribution

The paper develops a novel multilayer network framework and a personalized PageRank method for credit risk assessment, integrating multiple borrower connections for improved prediction accuracy.

Findings

Including multilayer network information improves risk prediction.

Multilayer PageRank variables further enhance predictive performance.

Default risk propagates through borrower networks, influenced by connections to defaulters.

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.