PriMera Scientific Engineering (ISSN: 2834-2550)

Research Article

Volume 7 Issue 4

Modeling Financial Stress Induced by Cyber Threats: An AI-Driven Predictive Approach

Udodirim Ogwo-Ude* and Ethel Wandeka N

September 23, 2025

DOI : 10.56831/PSEN-07-224

View PDF

Abstract

The increasing fusion of the global economy with digital infrastructure has elevated cyber threats from isolated technical issues to significant drivers of financial instability. Despite this, a measurement gap persists, as conventional financial risk models are ill-equipped to handle the high-dimensional and non-linear nature of Cyber Threat Intelligence (CTI). This study bridges this gap by developing and validating a predictive framework that translates global CTI into quantitative forecasts of systemic financial risk. Using a comprehensive dataset of over 77,000 daily cyber threat observations across 225 countries from 2015 to 2024, we forecast the U.S. St. Louis Fed Financial Stress Index (STLFSI). We conduct a comparative analysis of advanced deep learning architectures, including a Temporal Fusion Transformer (TFT), against canonical machine learning ensembles. Our results show that a gradient-boosted model (XGBoost) decisively outperforms other models, achieving an R² of 0.9883 and RMSE of 0.0321 on the hold-out test set. Employing Explainable AI (XAI) techniques, we deconstruct the model's predictions and find that its success stems from capturing the complex, non-linear interaction between cyber threat levels and the pre-existing state of financial market stress. This research provides robust empirical evidence of the cyber-financial nexus, offering a novel, data-driven methodology for asset managers, regulators, and security leaders to proactively quantify and manage a critical 21st-century risk.

Keywords: Cyber-Financial Risk; Systemic Risk; Cyber Threat Intelligence (CTI); Predictive Modeling; Machine Learning; Time-Series Forecasting; XGBoost; Explainable AI (XAI)

References

  1. Alkhadra R., et al. “Solar winds hack: In-depth analysis and coun-termeasures”. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE (2021): 1-7.
  2. Beerman J., et al. “A review of colonial pipeline ransomware attack”. In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE (2023): 8-15.
  3. Bouchetara M, Zerouti M and Zouambi AR. “Leveraging artificial intelligence (AI) in public sector financial risk management: Innovations, challenges, and future directions”. EDPACS 69.9 (2024): 124-144.
  4. Sun N., et al. “Cyber threat intelligence mining for proactive cyber-security defense: A survey and new perspectives”. IEEE Communications Surveys & Tutorials 25.3 (2023): 1748-1774.
  5. Meisner M. “Financial consequences of cyber attacks leading to data breaches in healthcare sector”. Copernican Journal of Finance & Accounting 6.3 (2017): 63-73.
  6. Juma'h AH and Alnsour Y. “The effect of data breaches on company performance”. International Journal of Accounting & Information Management 28.2 (2020): 275-301.
  7. Rodrigues GAP., et al. “Impact, compliance, and countermeasures in relation to data breaches in publicly traded US companies”. Future Internet 16.6 (2024): 201.
  8. IBM (2024). Cost of a data breach report 2024. [online] IBM. https://www.ibm.com/reports/data-breach
  9. Verizon (2023). DBIR 2023 Data Breach Investigations Report Public Sector Snapshot. [online] https://www.verizon.com/business/resources/Ta5a/reports/2023-dbir-public-sector-snapshot.pdf
  10. Furnell S., et al. “Understanding the full cost of cyber security breaches”. Computer fraud & security 2020.12 (2020): 6-12.
  11. Akerlof GA. “The market for “lemons”: Quality uncertainty and the market mechanism”. In Uncertainty in economics. Academic Press (1978): 235-251.
  12. Deane JK., et al. “The effect of information security certification announcements on the market value of the firm”. Information Technology and Management 20.3 (2019): 107-121.
  13. Tounsi W. “What is cyber threat intelligence and how is it evolving?”. Cyber‐Vigilance and Digital Trust: Cyber Security in the Era of Cloud Computing and IoT (2019): 1-49.
  14. Ţiţan AG. “The efficient market hypothesis: Review of specialized literature and empirical research”. Procedia Economics and Finance 32 (2015): 442-449.
  15. Henderson C. X-Force Threat Intelligence Index (2024). [online] Ibm.com. https://www.ibm.com/think/x-force/2024-x-force-threat-intelligence-index
  16. Curran D. “Connecting risk: Systemic risk from finance to the digital”. Economy and Society, 49(2), (2020): 239-264.
  17. Trevino I. “Informational channels of financial contagion”. Econometrica 88.1 (2020): 297-335.
  18. Staudemeyer RC and Morris ER. “Understanding LSTM--a tutorial into long short-term memory recurrent neural networks”. arXiv preprint arXiv:1909.09586 (2019).
  19. Salem FM. “Gated RNN: the gated recurrent unit (GRU) RNN. In Recurrent neural networks: from simple to gated architectures”. Cham: Springer International Publishing (2021): 85-100.
  20. Vaswani A., et al. “Attention is all you need”. Advances in neural information processing systems 30 (2017).
  21. Lim B., et al. “Temporal fusion transformers for interpretable multi-horizon time series forecasting”. International journal of forecasting 37.4 (2021): 1748-1764.
  22. U.S Federal Reserve (2011). SR 11-7: Guidance on Model Risk Management. Supervision and Regulation Letters. -https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm
  23. Lundberg SM and Lee SI. “A unified approach to interpreting model predictions”. Advances in neural information processing systems 30 (2017).
  24. DrSufi (2024). GitHub - DrSufi/CyberData: A comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries. [online] GitHub. https://github.com/DrSufi/CyberData/
  25. Qureshi M., et al. “Global Financial Stability Report, April 2024. International Monetary Fund eBooks”. International Monetary Fund (2024).
  26. Corbet S and Gurdgiev C. “What the hack: Systematic risk contagion from cyber events”. International Review of Financial Analysis 65 (2019): 101386.
  27. Kim S, Chida M and Yoshino N. Cybersecurity and Macroeconomy with Neoclassical Growth Model (2025). [online]
  28. Angrist JD. “Empirical strategies in economics: Illuminating the path from cause to effect”. Econometrica 90.6 (2022): 2509-2539.
  29. Hünermund P and Bareinboim E. “Causal inference and data fusion in econometrics”. The Econometrics Journal 28.1 (2025): 41-82.