Assessing Systemic Importance and Risk in Banks Using Multi-layer Financial Network Analysis

Siyuan Ahmed^*
*Correspondence: Siyuan Ahmed, Department of Economics, University of Macedonia,156 Egnatia Street, Thessaloniki 54636, Greece, Email:

Introduction

The stability of the global financial system depends heavily on the interconnectedness and risk dynamics of banks, particularly those classified as Systemically Important Financial Institutions (SIFIs). The 2008 financial crisis exposed the vulnerabilities of traditional risk assessment models, highlighting the need for a more comprehensive approach to understanding how financial institutions interact and propagate risk. Multi-layer financial network analysis has emerged as a powerful tool for assessing systemic importance and risk characteristics of banks by capturing the complex web of relationships that exist across multiple layers of financial markets, including interbank lending, derivatives exposure, and asset correlations. Unlike traditional single-layer models, multi-layer networks provide a holistic view of financial connectivity, allowing regulators, policymakers, and financial institutions to assess systemic risk more accurately. By analysing the structural properties of these networks, such as centrality measures, contagion pathways, and cross-market dependencies, this study aims to identify the most influential banks, detect potential risk concentrations, and evaluate how shocks propagate through the financial system [1].

Description

The multi-layer financial network approach extends traditional financial network analysis by incorporating different types of financial relationships within and across institutions. Banks do not operate in isolation; they engage in multiple financial activities, including interbank lending, securities trading, derivative transactions, and payment systems, each of which forms a unique network layer. By considering these layers simultaneously, researchers can uncover hidden dependencies and risk amplifications that would otherwise be overlooked in single-layer models. A key advantage of multi-layer network analysis is its ability to identify systemic importance using centrality measures, such as degree centrality, eigenvector centrality, and betweenness centrality. These measures help determine which banks act as critical nodes in the financial system. Highly connected banks, often referred to as global Systemically Important Banks (G-SIBs), play a crucial role in financial stability but also pose a significant risk in times of crisis. For example, if a highly interconnected bank faces distress, the likelihood of cascading failures across the network increases dramatically. This phenomenon, known as contagion risk, was evident during the Lehman Brothers collapse, which triggered a chain reaction in global markets [2]. Beyond centrality, network resilience and robustness are critical factors in assessing systemic risk. A well-diversified financial network, where risk is distributed across multiple institutions, is less susceptible to systemic crises. However, modern financial networks often exhibit a core-periphery structure, where a small number of central banks dominate interbank transactions while peripheral banks depend on them for liquidity. This structure creates a fragile dependency, where the failure of core institutions can destabilize the entire system. Multi-layer analysis helps quantify these vulnerabilities by mapping interdependencies across different financial layers and evaluating how shocks propagate through diverse financial channels. Another significant feature of multi-layer networks is the ability to model cross-border financial linkages, which have become increasingly important in todayâ??s globalized financial landscape. The interconnected nature of international banking means that financial shocks in one country can quickly spread to others, as seen during the European debt crisis and COVID-19 pandemic-induced market turmoil. By incorporating multiple jurisdictions into the network framework, policymakers can better understand how regulatory changes, interest rate movements, or economic disruptions affect banks at both national and global levels [3]. Furthermore, stress-testing frameworks can be enhanced using multi-layer networks by simulating different shock scenarios and assessing their systemic impact. Traditional stress tests often focus on individual balance sheet vulnerabilities, but network-based stress tests consider spillover effects and second-round contagion dynamics. For instance, if a bank experiences a liquidity crisis in the interbank market, the stress test can assess how this distress spreads through derivative markets or securities holdings, providing a realistic assessment of systemic risk exposure. This enables regulators to design targeted intervention strategies, such as capital buffers, liquidity support measures, or macro prudential policies, to mitigate financial instability before it escalates into a full-blown crisis. The role of market structure and behavioral dynamics also comes into play in systemic risk assessment. Market participants react to price fluctuations, regulatory changes, and macroeconomic conditions, often amplifying systemic risks through herd behavior, margin calls, or flight-to-safety movements. Multi-layer network analysis can integrate agent-based models that simulate how banks, investors, and central banks interact in different market environments. This approach provides insights into non-linear risk transmission mechanisms, allowing for a more adaptive regulatory response [4]. Advancements in big data analytics and Artificial Intelligence (AI) have further enhanced the capabilities of multi-layer financial network analysis. AI-driven models can process vast amounts of financial transaction data, regulatory filings, and real-time market movements, enabling dynamic risk assessment. Machine learning techniques can detect anomalies in network structures, identify early warning signals of financial distress, and optimize systemic risk monitoring frameworks. These innovations pave the way for proactive financial supervision, reducing the likelihood of systemic crises and improving overall market resilience. By analyzing centrality measures, network structures, and cross-market linkages, multi-layer financial networks provide a more comprehensive understanding of how financial shocks propagate and where vulnerabilities lie. The ability to model stress scenarios, cross-border dependencies, and behavioral dynamics enhances risk assessment frameworks, allowing for more informed policymaking and targeted regulatory measures [5].

Conclusion

The systemic importance and risk characteristics of banks can no longer be accurately assessed using traditional models that focus on isolated risk factors. The interconnected nature of modern financial markets requires a multi-layer financial network approach, which captures the complex web of relationships across multiple financial activities. This methodology enables regulators and financial institutions to identify critical nodes in the banking system, detect potential contagion risks, and design effective intervention strategies to safeguard financial stability. Ultimately, a multi-layer network-based approach to systemic risk assessment is essential for strengthening financial stability, preventing crises, and fostering a more robust global banking system. As financial markets continue to evolve, regulators, policymakers, and banking institutions must embrace network-driven methodologies to navigate the challenges of an interconnected financial world effectively.

Acknowledgement

None

Conflict of Interest

None

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