Artificial Intelligence and the Quantitative Management of Financial Risk
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".
Deadline for manuscript submissions: 22 March 2026 | Viewed by 22
Special Issue Editor
Special Issue Information
Dear Colleagues,
Quantitative financial risk management has long been a cornerstone of modern financial engineering, aiming to systematically identify, measure, and mitigate diverse types of financial risks through mathematical modeling, statistical analysis, and computational techniques. With evolving financial market structures, diverse sources of risk, and the increasing prevalence of high-dimensional as well as unstructured data, traditional quantitative approaches are facing new challenges. These challenges underscore the need for greater precision, adaptability, and real-time responsiveness in risk management frameworks. At the same time, artificial intelligence (AI) has introduced novel tools and perspectives to the field. AI technologies offer powerful capabilities in processing large-scale and heterogeneous financial datasets, and they have shown significant promise in pattern recognition, dynamic forecasting, and anomaly detection. As such, AI has become a vital complement and extension to traditional quantitative methods.
This Special Issue focuses on the latest research in quantitative financial risk management, as well as innovative applications of AI techniques in this domain. We welcome both theoretical and applied contributions, ranging from the traditional quantitative modeling and evaluation of financial risk to the integration of AI methods into risk analysis, monitoring, and control systems.
This Special Issue will accept high-quality papers containing original research results and review articles of exceptional merit in the following fields:
- Research on systemic risk.
- Modeling of nonlinear and high-dimensional characteristics.
- Complex network analysis.
- Risk forecasting and early warning.
- Design of early warning systems.
- Evaluation of network resilience.
- Drivers of financial risk.
Prof. Dr. Zisheng Ouyang
Guest Editor
Manuscript Submission Information
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Keywords
- financial risk
- quantitative finance
- artificial intelligence
- complex networks
- machine learning
- risk resilience
- risk early warning
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