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Risks
Special Issues
AI for Financial Risk Perception
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AI for Financial Risk Perception

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3596

Special Issue Editor

Dr. He Wen

E-Mail Website
Guest Editor
Bailey College of Engineering & Technology, Indiana State University, Terre Haute, IN 47809, USA
Interests: AI safety; safety with explainable AI; risk simulation; causal inference
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) becomes increasingly embedded in financial systems, such as portfolio management, algorithmic trading, credit risk modeling, fraud detection, and insurance underwriting, its ability to perceive, model, and respond to risk is critical for ensuring robustness, transparency, and timely decision-making. Traditional financial risk analysis has relied on structured models and expert judgment; however, the growing complexity, speed, and interconnectedness of global financial markets necessitate more adaptive and intelligent approaches to assess risk.

This Special Issue focuses on the emerging paradigm of “AI risk perception” within finance and economics. We invite the submission of original research and reviews that explore how AI technologies enhance or transform financial risk perception, reasoning, and decision-making. Topics of interest include AI-driven risk quantification, market anomaly detection, uncertainty modeling in finance, AI-enabled stress testing, human–AI collaboration in financial decisions, and explainability in AI risk models. Research addressing the implications of AI in regulatory compliance, behavioral finance, and risk governance is also encouraged.

We aim to collate both theoretical advances and practical applications that illustrate the role of AI in managing financial uncertainty, improving risk-adjusted performance, and supporting resilient economic systems.

Dr. He Wen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • financial risk management
  • risk perception
  • machine learning in finance
  • credit risk modeling
  • algorithmic trading
  • risk-based decision-making
  • AI explainability
  • economic uncertainty
  • behavioral finance
  • human–AI collaboration

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Published Papers (3 papers)

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Research

12 pages, 7108 KB  
Article
Predicting Stock Market Risk Using Machine Learning Classification Models
by Seol-Hyun Noh
Risks 2026, 14(4), 92; https://doi.org/10.3390/risks14040092 - 17 Apr 2026
Viewed by 562
Abstract
This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was [...] Read more.
This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A day was labeled a risk event (1) if its return fell below the 5th percentile of the returns observed over the preceding 100 trading days, indicating a sharp decline. Nine classification models—Logistic Regression, k-nearest Neighbor, Decision Tree, Random Forest, Linear Discriminant Analysis, Naive Bayes, Quadratic Discriminant Analysis, AdaBoost, and Gradient Boosting—were trained and validated. Among these, Logistic Regression demonstrated the strongest overall performance across multiple evaluation metrics, including accuracy, non-risk F1 score, risk F1 score, and AUC. Full article
(This article belongs to the Special Issue AI for Financial Risk Perception)
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16 pages, 567 KB  
Article
Insuring Algorithmic Operations: Liability Risk, Pricing, and Risk Control
by Zhiyong (John) Liu, Jin Park, Mengying Wang and He Wen
Risks 2026, 14(2), 26; https://doi.org/10.3390/risks14020026 - 31 Jan 2026
Viewed by 1293
Abstract
Businesses increasingly rely on algorithmic systems and machine learning models to make operational decisions about customers, employees, and counterparties. These “algorithmic operations” can improve efficiency but also concentrate liability in a small number of technically complex, drifting models. Algorithmic operations liability (AOL) risk [...] Read more.
Businesses increasingly rely on algorithmic systems and machine learning models to make operational decisions about customers, employees, and counterparties. These “algorithmic operations” can improve efficiency but also concentrate liability in a small number of technically complex, drifting models. Algorithmic operations liability (AOL) risk arises when these systems generate legally cognizable harm. We develop a simple taxonomy of AOL risk sources: model error and bias, data quality failures, distribution shift and concept drift, miscalibration, machine learning operations (MLOps) and integration failures, governance gaps, and ecosystem-level externalities. Building on this taxonomy, we outline a simple analysis of AOL risk pricing using some basic actuarial building blocks: (i) a confusion-matrix-based expected-loss model for false positives and false negatives; (ii) drift-adjusted error rates and stress scenarios; and (iii) credibility-weighted rates when insureds have limited experience data. We then introduce capital and loss surcharges that incorporate distributional uncertainty and tail risk. Finally, we link the framework to AOL risk controls by identifying governance, documentation, model-monitoring, and MLOps practices that both reduce loss frequency and severity and serve as underwriting prerequisites. Full article
(This article belongs to the Special Issue AI for Financial Risk Perception)
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15 pages, 2051 KB  
Article
Interpretable Multi-Model Framework for Early Warning of SME Loan Delinquency
by Ardak Akhmetova, Assem Shayakhmetova and Nurken Abdurakhmanov
Risks 2026, 14(2), 25; https://doi.org/10.3390/risks14020025 - 31 Jan 2026
Viewed by 1024
Abstract
The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study proposes an interpretable multi-model [...] Read more.
The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study proposes an interpretable multi-model framework that integrates statistical (correlation screening and ordinary least squares regression), probabilistic (Gaussian Naïve Bayes), and classical time-series (SARIMA) methods to balance explanatory insight and predictive accuracy in delinquency forecasting. Ordinary least squares regression is used to quantify the direction and strength of each driver and yields statistically significant coefficients (β ≈ 1.336 for the overdue 15+ days bucket, p < 10−22). The Naïve Bayes classifier provides a probabilistic early-warning signal with an out-of-sample accuracy of 55%, precision of 43%, recall of 75%, and ROC AUC of 0.371. Finally, a seasonal ARIMA model fitted on the selected regressors achieves a mean absolute percentage error (MAPE) of 7.6% and an out-of-sample R2 of 0.49, demonstrating competitive forecasting performance while maintaining interpretability. The results show that the framework offers actionable insights for risk managers by identifying key risk drivers, providing probabilistic alarms, and generating calibrated point forecasts. The proposed approach contributes to the development of intelligent and explainable forecasting and control systems for modern financial institutions. Full article
(This article belongs to the Special Issue AI for Financial Risk Perception)
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