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Multilayer network analysis for improved credit risk prediction

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  • Óskarsdóttir, María
  • Bravo, Cristián

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.

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  • Óskarsdóttir, María & Bravo, Cristián, 2021. "Multilayer network analysis for improved credit risk prediction," Omega, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:jomega:v:105:y:2021:i:c:s0305048321001298
    DOI: 10.1016/j.omega.2021.102520
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    1. Wu, Fei & Xiao, Xuanqi & Zhou, Xinyu & Zhang, Dayong & Ji, Qiang, 2022. "Complex risk contagions among large international energy firms: A multi-layer network analysis," Energy Economics, Elsevier, vol. 114(C).
    2. Sanjiv Das & Xin Huang & Soji Adeshina & Patrick Yang & Leonardo Bachega, 2023. "Credit Risk Modeling with Graph Machine Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 197-217, October.
    3. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    4. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    5. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org.

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