# Multilayer Network Analysis for Improved Credit Risk Prediction

arXiv: https://arxiv.org/abs/2010.09559 · HTML: https://mlsanity.com/paper/2010.09559/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://mlsanity.com/paper/2402.00299/) (2024)
- [On the combination of graph data for assessing thin-file borrowers' creditworthiness](https://mlsanity.com/paper/2111.13666/) (2021)
- [Influencer Detection with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2211.09664/) (2022)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2307.08131/) (2023)
- [Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks](https://mlsanity.com/paper/2407.15532/) (2024)
- [A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks](https://mlsanity.com/paper/2510.09407/) (2025)
- [Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models](https://mlsanity.com/paper/2608.17715/) (2026)

## Abstract (arXiv)

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.