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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18663046 |
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Table of Contents:
- <p>Digital lending platforms have achieved remarkable success in borrower-level risk assessment through sophisticated machine learning models, yet traditional portfolio monitoring remains fundamentally reactive and overlooks systemic vulnerabilities that emerge from network-level interdependencies. Current credit risk frameworks treat borrowers as independent entities and rely on lagging aggregate indicators, creating critical blind spots in detecting correlated defaults and systemic risk propagation. The Network-Level Credit Risk Navigator (NCRN) addresses these limitations by modeling digital lending ecosystems as dynamic, heterogeneous networks where borrowers, lenders, products, and economic factors form complex webs of interdependency. NCRN integrates graph neural networks for learning network-aware representations, contagion simulation engines for modeling distress propagation, and anomaly detection systems for identifying emerging vulnerabilities. The framework introduces Risk Propagation Paths as directed routes through the network that quantify specific transmission mechanisms for financial distress under various stress scenarios. Through comprehensive validation using synthetic datasets and historical backtesting, NCRN demonstrates the ability to detect systemic risk clusters months earlier than conventional delinquency-based monitoring systems. The implementation framework addresses practical challenges, including entity resolution at scale, real-time graph maintenance, computational optimization through sampling and hierarchical modeling, and integration with existing risk management workflows. NCRN transforms credit risk oversight from reactive portfolio monitoring to proactive network-level vulnerability detection, enabling digital lenders to identify and mitigate systemic risks before they manifest as portfolio-wide losses.</p>