Systemic Risk and Decision-Making: A Network Perspective

A Special Issue of Systems (ISSN 2079-8954) belonging to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 834

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Guest Editor
Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: international finance; international business; risk management in international finance & business; project finance; corporate finance; panel data analysis

Special Issue Information

Dear Colleagues,

Systemic risk emerges from the complex web of interactions among financial institutions, markets, and economic agents. From a network perspective, financial systems are highly interconnected structures where shocks can propagate through multiple channels, generating spillovers that amplify initial disturbances. Understanding how decisions made by individual actors influence the stability of the entire system is therefore essential. Network-based approaches provide tools to map interdependencies, identify critical nodes, and assess how risk spreads across financial and economic networks. Such perspectives move beyond isolated risk assessment and focus instead on the collective dynamics that shape resilience or fragility within the system. By integrating insights from finance, network science, and decision theory, this approach contributes to a deeper understanding of systemic vulnerabilities and supports the design of policies and decision-making frameworks aimed at strengthening financial stability.

Suggested Topics include, but are not limited to:

  1. Network-based approaches to measuring and modeling systemic risk in financial systems
  2. Interbank networks, financial interconnectedness and contagion dynamics
  3. Network spillovers across financial markets and institutions
  4. Critical nodes in financial networks
  5. Macroprudential regulation and policy responses in interconnected financial systems
  6. Decision-making under uncertainty in complex financial networks
  7. Early-warning indicators of financial instability using network analytics
  8. Dynamics of shock propagation and disruptions in financial networks
  9. Cross-border financial contagion and global financial interconnectedness
  10. Applications of network science and graph theory in financial economics
  11. Agent-based and simulation models of systemic risk and financial crises
  12. Network resilience, robustness, and policy tools for enhancing financial stability

Prof. Dr. Cristian Paun
Guest Editor

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Keywords

  • systemic risk
  • financial networks
  • network contagion
  • interconnectedness
  • macroprudential regulation
  • risk propagation
  • network spillovers
  • financial stability
  • banking system resilience
  • interbank markets

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Published Papers (1 paper)

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Research

20 pages, 2247 KB  
Article
Systems-Oriented Explainable AI for Corporate Bankruptcy Early Warning: A Deep Learning Framework for Risk-Attribution Analysis
by Chenxi Yang and Guangfan Sun
Systems 2026, 14(8), 923; https://doi.org/10.3390/systems14080923 - 1 Aug 2026
Viewed by 354
Abstract
Corporate bankruptcy early warning has often been treated as a binary classification task, yet financial distress is better understood as the outcome of interacting financial conditions that must be interpreted within practical risk-management contexts. To address this issue, this study proposes a systems-oriented [...] Read more.
Corporate bankruptcy early warning has often been treated as a binary classification task, yet financial distress is better understood as the outcome of interacting financial conditions that must be interpreted within practical risk-management contexts. To address this issue, this study proposes a systems-oriented explainable artificial intelligence framework for corporate bankruptcy early warning. The framework implements a Hierarchical LFRM-MACI architecture in which the Local Feature Refinement Module (LFRM) refines representations within profitability, solvency, liquidity, efficiency, and growth/shareholder performance subsystems, and cross-subsystem attention models their interactions. The framework is evaluated on the public UCI Taiwanese Bankruptcy Prediction dataset under balanced and moderately imbalanced training settings. For reporting clarity, the benchmark methods are classified into traditional machine learning methods and deep learning methods; traditional tabular learners are included in the formal single-split empirical comparison, while the proposed method’s contribution is positioned as a structured deep representation with an attribution workflow rather than as an overall superiority claim over traditional machine learning models. In the single 70%/30% validation split, the proposed model obtains ROC-AUC values of 0.9286 and 0.9269 under the 1:1.0 and 1:2.5 settings, respectively. In repeated 5-fold cross-validation with five repetitions, its ROC-AUC is 0.8940 [0.8777, 0.9103] under 1:1.0 and 0.9135 [0.9001, 0.9269] under 1:2.5. To examine the interpretability of the predictions, Permutation Feature Importance (PFI) and SHAP are applied to identify subsystem-level attribution patterns across major financial subsystems. The explanation results highlight influential predictors associated with leverage pressure, profitability and asset structure, liquidity, operating efficiency, and growth/shareholder performance. These findings indicate that explainable AI can support corporate bankruptcy early warning when predictive benchmarking is combined with transparent and auditable attribution analysis for financial decision-making. Full article
(This article belongs to the Special Issue Systemic Risk and Decision-Making: A Network Perspective)
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