Graph Neural Networks for Systemic Financial Risk Forecasting: Modeling Cross-Market Contagion Between ‎Banking Systems and Cryptocurrency Markets

  • Authors

    • Md Zahidul Islam MBA in Business Analytics, Gannon University, Erie, PA
    • Md Sumsuzoha Master of Science in Business Analytics, Trine University
    • Md Rafiqul Islam DBA in Business Analytics, International American University
    • Mohammed Kawsar MSc, Analytics & Information Management, Duquesne University
    • Md Fazlul Huq Mithu MS in Finance, Stony Brook University
    • Santosh Pant BBA, Kantipur College of Management and Information Technology, Kathmandu, Nepal
    • Mohammad Nazmul Hossain Computer/Information Technology Services Administration and Management, St. Francis College
    • Md Abdullah Al Helal Master of Science in Business Analytics, Trine University
    https://doi.org/10.14419/mh97vb34

    Received date: January 24, 2026

    Accepted date: February 18, 2026

    Published date: February 24, 2026

  • Banking Networks; Cryptocurrency Markets; Dynamic Graphs; Graph Neural Networks; Systemic Risk‎.

  • Abstract

    Evolving interdependencies across institutions and markets drive systemic financial risk, yet most forecasting models either treat assets independently or rely on static correlation structures. This limitation becomes particularly salient as cryptocurrency markets increasingly interact with traditional banking systems amid financial stress. Ignoring time-varying cross-market network structure risks understating tail risk ‎precisely during periods when accurate systemic risk assessment is most critical. This study proposes a dynamic graph neural network ‎‎(GNN) framework for systemic risk forecasting that models time-varying financial networks spanning banking institutions and major cryptocurrency assets. Nodes represent financial entities, while edges are constructed using rolling-window dependency measures that adapt to ‎changing market conditions. Node dynamics are modeled through temporal neural architectures, and stress regimes are explicitly identified ‎to evaluate performance under market turmoil. The empirical design includes strong temporal baselines, static-graph ablations, and cross-market removal experiments to isolate the contribution of network dynamics and crypto-market integration. Results indicate that a strong ‎LSTM baseline achieves superior volatility forecasting accuracy in both overall and stress-period evaluations, demonstrating the competitiveness of purely temporal models. However, within the class of graph-based models, dynamic GNNs substantially outperform static-graph variants during stress periods, demonstrating the importance of time-varying network structure for capturing volatility amplification. ‎Bank-only and full-system dynamic GNNs exhibit comparable stress-period performance, suggesting that cryptocurrency assets contribute ‎limited incremental information to bank-specific forecasts, while remaining informative for system-level stress characterization. The findings ‎suggest that dynamic graph representations enhance stress sensitivity and structural interpretability relative to static network models, even ‎when they do not surpass strong temporal baselines in raw predictive accuracy. The results support a restrained view of crypto–banking ‎contagion, emphasizing its conditional relevance during periods of market stress rather than unconditional systemic dominance.

  • References

    1. Aashish, K. C., Zamil, M. Z. H., Mridul, M. S. I., Akter, L., Sharmin, F., Ayon, E. H., … Malla10, S. (2025). Towards eco‑friendly cybersecurity: Machine learning‑based anomaly detection with carbon and energy metrics. International Journal of Applied Mathematics, 38(9s). https://doi.org/10.12732/ijam.v38i9s.822.
    2. Acemoglu, D., Ozdaglar, A., & Tahbaz‑Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. https://doi.org/10.1257/aer.20130456.
    3. Arnosti, N., Colliard, J.-E., & Hoffmann, P. (2022). Mapping microscopic and systemic risks in TradFi and DeFi. arXiv preprint arXiv:2209.13114.
    4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
    5. Chouksey, A., Dola, A., Antara, U. K., Begum, S., Ahmed, T., Sultana, T., & Zabin, N. (2025). AI‑driven early warning system for financial risk in the US digital economy. International Journal of Applied Mathematics, 38(9s). https://doi.org/10.12732/ijam.v38i9s.838.
    6. Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003.
    7. Das, B. C., et al. (2025). AI‑driven cybersecurity threat detection: Building resilient defense systems using predictive analytics. arXiv preprint arXiv:2508.01422. https://doi.org/10.14419/hysdg957.
    8. Debnath, S., et al. (2025). AI‑driven cybersecurity for renewable energy systems: Detecting anomalies with energy‑integrated defense data. Inter-national Journal of Applied Mathematics, 38(5s). https://doi.org/10.12732/ijam.v38i5s.367.
    9. Eisenberg, L., & Noe, T. H. (2001). Systemic risk in financial systems. Management Science, 47(2), 236–249. https://doi.org/10.1287/mnsc.47.2.236.9835.
    10. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487.
    11. Glasserman, P., & Young, H. P. (2016). Contagion in financial networks. Journal of Economic Literature, 54(3), 779–831. https://doi.org/10.1257/jel.20151228.
    12. Gonon, L., Meyer‑Brandis, T., & Weber, N. (2024). Computing systemic risk measures with graph neural networks. arXiv preprint arXiv:2410.07222.
    13. Hasan, M. R., Rahman, M. A., Gomes, C. A. H., Nitu, F. N., Gomes, C. A., Islam, M. R., & Shawon, R. E. R. (2025). Building robust AI and ma-chine learning models for supplier risk management: A data-driven strategy for enhancing supply chain resilience in the USA. Advances in Con-sumer Research, 2(4).
    14. Hasan, M. S., et al. (2025). Explainable AI for supplier credit approval in data‑sparse environments. International Journal of Applied Mathematics, 38(5s). https://doi.org/10.12732/ijam.v38i5s.380.
    15. Islam, M. Z., et al. (2025). Cryptocurrency price forecasting using machine learning: Building intelligent financial prediction models. arXiv preprint arXiv:2508.01419. https://doi.org/10.14419/s0pktr58.
    16. Jakir, T. (2025). Signal‑to‑noise analysis of crisis indicators in global finance using artificial intelligence. International Journal of Applied Mathemat-ics, 38(10s), 1815–1836. https://doi.org/10.12732/ijam.v38i10s.1075.
    17. Korablyov, M., Fomichov, O., Kobzev, I., Antonov, D., & Tkachuk, O. (2025). Stock market price forecasting using an evolving graph neural net-work. In Proceedings of the International Workshop on Computational Intelligence (IntSol‑2025) (pp. 1–15). CEUR‑WS.
    18. Kumar, P. N., Umeorah, N., & Alochukwu, A. (2024). Dynamic graph neural networks for enhanced volatility prediction in financial markets (arXiv:2410.16858). arXiv preprint.
    19. Patel, M., Jariwala, K., & Chattopadhyay, C. (2024). A systematic review of GNN‑based methods for stock market forecasting. ACM Computing Surveys, 57(2), 34:1–34:38. https://doi.org/10.1145/3696411.
    20. Rahman, M. S. (2025). Machine learning–enabled early warning system for detecting micro‑inflation clusters in the US economy. International Journal of Applied Mathematics, 38(12s), 2743–2769. https://doi.org/10.12732/ijam.v38i12s.1585.
    21. Ray, R. K. (2025). Multi‑market financial crisis prediction: A machine learning approach using stock, bond, and forex data. International Journal of Applied Mathematics, 38(8s), 706–738. https://doi.org/10.12732/ijam.v38i8s.602.
    22. Reza, S. A., et al. (2025). AI‑driven socioeconomic modeling: Income prediction and disparity detection among US citizens using machine learning. Advances in Consumer Research, 2(4).
    23. Reza, S. A., Rahman, M. K., Rahman, M. D., Sharmin, S., Mithu, M. F. H., Hasnain, K. N., ... & Kabir, R. (2025). Machine learning enabled early warning system for financial distress using real-time digital signals. arXiv preprint arXiv:2510.22287.
    24. Shawon, R. E. R., et al. (2025). Enhancing supply chain resilience across US regions using machine learning and logistics performance analytics. International Journal of Applied Mathematics, 38(4s). https://doi.org/10.12732/ijam.v38i4s.225.
    25. Shawon, R. E. R., Buiya, M. R., Pant, S., Al Jobaer, M. A., Chowdhury, M. S. R., Kawsar, M., ... & Ali, M. (2025). Detecting illicit cross‑chain fund movement: Behavioral machine learning models for bridge‑based laundering patterns. International Journal of Applied Mathematics, 38(12s). https://doi.org/10.12732/ijam.v38i12s.1399.
    26. Shivogo, J. (2025). Fair and explainable credit‑scoring under concept drift: Adaptive explanation frameworks for evolving populations. arXiv pre-print arXiv:2511.03807.
    27. Shovon, M. S. S. (2025). Towards sustainable urban energy systems: A machine learning approach with low‑voltage smart grid planning data. In-ternational Journal of Applied Mathematics, 38(8s), 1115–1155. https://doi.org/10.12732/ijam.v38i8s.631.
    28. Sizan, M. M. H., et al. (2025). Machine learning‑based unsupervised ensemble approach for detecting new money laundering typologies in transac-tion graphs. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.88.
    29. Sonani, M. S., Badii, A., & Moin, A. (2025). Stock price prediction using a hybrid LSTM–GNN model. arXiv preprint arXiv:2502.15813.
    30. Vuković, D. B., Lyócsa, Š., & Todorović, N. (2025). Spillovers between cryptocurrencies and financial markets: Global evidence. Journal of Inter-national Money and Finance, 139, 102994. https://doi.org/10.1016/j.jimonfin.2024.103235.
    31. Wang, J., Zhang, S., Xiao, Y., & Song, R. (2022). A review on graph neural network methods in financial applications. Journal of Data Science, 20(2), 111–134. https://doi.org/10.6339/22-JDS1047.
    32. Zamanian, A., Aslani, M., & Hematfar, M. (2025). A spatial–deep learning hybrid model for cryptocurrency market prediction with nonlinear spa-tial contagion analysis. Blockchain and Financial Markets Open, 1, 328. https://doi.org/10.61838/bmfopen.328.

  • Downloads

  • How to Cite

    Islam, M. Z., Sumsuzoha, M., Islam , M. R., Kawsar, M., Mithu, M. F. H., Pant , S., Hossain, M. N., & Helal, M. A. A. (2026). Graph Neural Networks for Systemic Financial Risk Forecasting: Modeling Cross-Market Contagion Between ‎Banking Systems and Cryptocurrency Markets. International Journal of Accounting and Economics Studies, 13(2), 331-342. https://doi.org/10.14419/mh97vb34