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Artificial Intelligence Risk Management
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Artificial Intelligence Risk Management

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 4934

Special Issue Editor

Prof. Dr. Paolo Giudici

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: financial data science; graphical models; network models; financial networks; systemic risk; financial risk management; fintech risk management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growth of artificial intelligence requires researchers to develop risk management models that balance opportunities with risks. This Special Issue welcomes studies that propose methods and applications showing how to measure, manage and mitigate the risks associated with artificial intelligence. We aim to make artificial intelligence responsible, safe and trustworthy.

Prof. Dr. Paolo Giudici
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
  • machine learning
  • risk measurement
  • risk management
  • risk mitigation
  • responsible AI
  • safe AI
  • trustworthy AI

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Published Papers (3 papers)

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Research

19 pages, 1317 KB  
Article
Metaheuristics for Portfolio Optimization: Application of NSGAII, SPEA2, and PSO Algorithms
by Ameni Ben Hadj Abdallah, Rihab Bedoui and Heni Boubaker
Risks 2025, 13(11), 227; https://doi.org/10.3390/risks13110227 - 19 Nov 2025
Viewed by 438
Abstract
This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the [...] Read more.
This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises. Full article
(This article belongs to the Special Issue Artificial Intelligence Risk Management)
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25 pages, 1453 KB  
Article
Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data
by Dorsa Farahmandazad, Kasra Danesh and Hossein Fazel Najaf Abadi
Risks 2025, 13(10), 198; https://doi.org/10.3390/risks13100198 - 13 Oct 2025
Viewed by 802
Abstract
Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as [...] Read more.
Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries. Full article
(This article belongs to the Special Issue Artificial Intelligence Risk Management)
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15 pages, 2189 KB  
Article
AI Risk Management: A Bibliometric Analysis
by Adelaide Emma Bernardelli and Paolo Giudici
Risks 2025, 13(7), 131; https://doi.org/10.3390/risks13070131 - 7 Jul 2025
Viewed by 3118
Abstract
The growth of Artificial Intelligence applications requires the development of risk management models that can balance opportunities with risks. This paper contributes to the development of Artificial Intelligence risk management models by means of a thorough bibliometric analysis. The analysis highlights the need [...] Read more.
The growth of Artificial Intelligence applications requires the development of risk management models that can balance opportunities with risks. This paper contributes to the development of Artificial Intelligence risk management models by means of a thorough bibliometric analysis. The analysis highlights the need to develop a quantitative AI risk management framework. Full article
(This article belongs to the Special Issue Artificial Intelligence Risk Management)
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