Machine Learning Based Risk Management in Finance and Insurance

Dear Colleagues,

The finance and insurance industries are increasingly facing a rapidly changing landscape characterized by increased uncertainty, complex interdependencies, and the proliferation of high-dimensional and big data. Traditional risk management methods, while basic, are often challenged by these complexities, necessitating the integration of more sophisticated statistical and machine learning techniques. This Special Issue aims to cover the cutting edge of research and innovation in applying these advanced methodologies to quantitative risk management in finance and insurance.

We invite submissions that address a wide range of topics, including, but not limited to, portfolio optimization, credit risk modeling, insurance pricing, catastrophe modeling, systemic risk analysis, fraud detection, and algorithmic trading. Contributions to developing new models, improving existing techniques, or innovative applications of machine learning algorithms (e.g., deep learning, ensemble methods, and reinforcement learning) are strongly encouraged. In addition, we welcome research that addresses the challenges of model interpretation, robustness, and scalability and research that examines the ethical implications and regulatory aspects of implementing these techniques in real-world scenarios. This Special Issue also highlights interdisciplinary approaches that bridge the gap between finance, insurance, statistics, and computer science, providing new perspectives on managing and mitigating risk in an increasingly complex environment. Particular attention will be given to submissions that provide empirical evidence through case studies or propose methods/algorithms that combine machine learning with traditional risk management practices. By bringing together cutting-edge research and practical insights, this Special Issue aims to advance the field and serve as a critical resource for academics, industry professionals, and policymakers striving to improve risk management strategies in an era of unprecedented change.

Dr. Mohamed Chaouch
Prof. Dr. Thanasis Stengos
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Journal of Risk and Financial Management 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 1400 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.

• quantitative risk management
• financial risk
• insurance risk
• machine learning
• statistical modeling
• portfolio optimization
• credit risk modeling
• insurance pricing
• catastrophe modeling
• systemic risk
• predictive analytics
• deep learning
• ensemble methods
• reinforcement learning
• fraud detection
• algorithmic trading
• model interpretability
• regulatory compliance
• high-dimensional data
• model robustness
• risk mitigation
• ethical AI in finance