In recent years, the fragility of the global financial system has been starkly exposed, most notably during the 2007-2008 financial crisis. However, a groundbreaking study conducted by researchers at the Complexity Science Hub (CSH) has unveiled a previously underestimated source of systemic risk: the profound interplay between real-world supply chain disruptions and the interbank credit networks that underpin modern banking. Through a sophisticated, data-driven modeling approach, the research reveals how shocks originating in the supply chains of individual firms can cascade through both economic and financial systems, exacerbating potential losses by a staggering 70%.
At the heart of this new analytical framework lies a dual-layer contagion model, meticulously designed to capture two intertwined phenomena. The first layer maps the economic dependencies arising from supply chain relationships among firms, where disruptions such as production halts, scarcity of inputs, or insolvencies propagate akin to shockwaves, initiating a domino effect across interconnected entities. The second layer models the interbank credit market, where financial institutions extend loans to each other, creating a web of mutual obligations. When one bank defaults due to losses linked to its corporate borrowers, it can precipitate losses for other banks—a process termed interbank contagion. By overlaying these layers, the model sheds light on how real economy shocks translate into amplified systemic risks within the financial sector.
This innovative model is grounded in an unprecedentedly detailed dataset from Hungary, encompassing over 240,000 firms and 19 banks, along with more than one million supply chain connections and over 40,000 bank-firm credit relationships. Such granularity is rare, as most countries lack unified data at the level of individual companies and banking institutions. By leveraging these robust datasets, the researchers could reconstruct the intricate architecture of supply networks and financial exposures with near-exact precision. This empirical grounding enables simulations that reflect true systemic configurations rather than relying on theoretical or aggregate assumptions common in typical risk models.
Initial simulations were designed to probe how the failure of a single firm could ripple through the supply chain and banking system. When a firm fails, the economic shock spreads downstream to its supply chain partners, inducing further disruptions and potential insolvencies—a cascade termed supply chain contagion. The model then assesses which banks financed the failing firms and tracks contagion further into the network of interbank loans. Strikingly, the results demonstrated that systemic financial losses were consistently amplified—on average by 12%, and in certain cases up to 28%—due to these interconnected feedback effects. This finding underscores the critical importance of accounting for multi-layered interdependencies in risk assessments.
To extend the analysis beyond isolated incidents, the researchers conducted extensive stress tests simulating 1,001 distinct pandemic-induced economic shock scenarios, drawing inspiration from the 2020 Covid-19 crisis. One simulation replicated the exact production declines experienced by Hungarian firms during the pandemic’s early stages, while the others varied which firms were initially affected but kept the shock intensity consistent. These simulations enabled statistically robust assessments of systemic risk under multifaceted conditions, highlighting the susceptibility of financial networks to widespread supply chain disturbances.
One of the most consequential revelations from these stress tests is that supply chain contagion does not simply add to interbank contagion losses — it fundamentally transforms the distribution of extreme loss events. In statistical terms, the model predicted substantially “fatter tails” in the loss distribution, meaning the probability of very severe financial crises is much higher than previously estimated. This non-linear amplification effect challenges traditional financial models, which often assume losses cluster around average values and may neglect such rare but catastrophic tail risks.
These findings carry profound implications for regulators, practitioners, and policymakers. Conventional credit risk models, which typically analyze financial exposures and firm defaults in isolation, are likely to dramatically underestimate systemic vulnerabilities by ignoring the complex interactions between real economy disruptions and financial interconnectedness. The new model offers a powerful lens to better comprehend these risks, improve stress testing protocols, and design more effective intervention strategies aimed at preventing or mitigating cascading failures in times of crisis.
Moreover, the study illuminates the necessity of cross-domain data integration—combining granular supply chain data with comprehensive banking exposure information—to build accurate and predictive systemic risk frameworks. This approach could enable supervisory authorities to identify hidden systemic threats before they amplify into widespread financial dislocations, allowing for preemptive measures that enhance economic resilience.
Beyond policy implications, this research underscores the evolving complexity of the global economic ecosystem where supply chains and financial systems have grown deeply interwoven. Events localized in seemingly distant industrial sectors or geographic regions can propagate rapidly through intertwined networks, converting local shocks into global financial hazards. Recognizing and modeling these intricate dependencies is essential for future financial stability.
The researchers also point to the crucial role of computational modeling and network science in decoding such multi-layered systems. By simulating countless scenarios within empirically grounded frameworks, they offer unprecedented insights into the dynamics of contagion processes across intertwined economic and financial webs. Their work exemplifies how interdisciplinary approaches at the intersection of physics, economics, and data science can drive innovation in risk assessment.
In conclusion, this pioneering study from the Complexity Science Hub charts a new frontier in systemic risk modeling, revealing the critical amplifying role of supply chain disruptions on banking sector vulnerabilities. As global supply chains face increasing geopolitical, environmental, and health-related challenges, incorporating multi-layered network interactions into financial risk frameworks will be indispensable for fostering economic stability and preparing for future systemic crises. The insights gained pave the way for more nuanced, realistic, and actionable risk management tools capable of safeguarding the intertwined fabric of the real economy and financial system.
Article Title: A data-driven econo-financial stress-testing framework to estimate the effect of supply chain networks on financial systemic risk
News Publication Date: 7-May-2026
Web References:
References:
Fialkowski, J., Diem, C., Borsos, A., & Thurner, S. (2026). A data-driven econo-financial stress-testing framework to estimate the effect of supply chain networks on financial systemic risk. Journal of Economic Dynamics & Control. https://doi.org/10.1016/j.jedc.2026.105333
Image Credits: © Complexity Science Hub (CSH)
Keywords: Network modeling, Supply chain contagion, Interbank contagion, Systemic risk, Financial stability, Econophysics, Computational modeling, Stress testing, Supply chain disruptions, Banking sector contagion, Complex systems, Data-driven modeling
