Artificial Intelligence-Driven Mathematical Methods for Financial Modeling and Optimization

Dear Colleagues,

Artificial intelligence is rapidly transforming the theory and practice of modern finance. With the increasing availability of high-frequency data, alternative data, textual information, blockchain records, and complex market indicators, AI-based methods have become powerful tools for financial modeling, prediction, decision-making, and risk control. At the same time, the successful application of artificial intelligence in finance relies fundamentally on rigorous mathematical foundations, including optimization theory, probability theory, stochastic processes, statistical learning, numerical analysis, game theory, graph theory, and dynamic systems.

Although AI models have demonstrated remarkable empirical performance in many financial applications, significant challenges remain in understanding their mathematical properties, interpretability, robustness, generalization ability, and reliability under real market conditions. The gap between AI-driven financial models and mathematically rigorous financial theory provides important opportunities for future research. In particular, there is a growing need for studies that combine advanced artificial intelligence techniques with solid mathematical modeling and real-world financial applications.

The purpose of this Special Issue is to provide a platform for high-quality research that explores the mathematical foundations, methodologies, and applications of artificial intelligence in finance. We welcome theoretical, methodological, computational, and empirical contributions that demonstrate how AI and mathematical methods can be integrated to address important problems in quantitative finance, risk management, portfolio optimization, financial forecasting, algorithmic trading, financial networks, green finance, credit risk analytics, and financial regulation.

Topics of interest include, but are not limited to, the following:

• Mathematical foundations of artificial intelligence in finance;
• Machine learning and deep learning methods for financial prediction;
• Reinforcement learning for portfolio management and algorithmic trading;
• Explainable AI and interpretable models in financial decision-making;
• Bayesian learning, probabilistic modeling, and uncertainty quantification in finance;
• Stochastic modeling and AI-based financial time series analysis;
• AI-driven risk management, systemic risk measurement, and financial stability;
• Portfolio optimization using artificial intelligence and mathematical programming;
• Graph neural networks and network models for financial markets;
• Natural language processing for financial news, reports, and sentiment analysis;
• High-frequency trading, market microstructure, and intelligent trading systems;
• AI applications in derivative pricing, hedging, and asset pricing;
• Robustness, model validation, and stress testing of AI-based financial models;
• Game theory, multi-agent learning, and strategic behavior in financial markets;
• AI methods for green finance, climate finance, ESG risk, and sustainable investment;
• Fraud detection, credit scoring, and financial risk classification;
• Blockchain finance, decentralized finance, and AI-based financial security;
• Hybrid models combining mathematical finance, econometrics, and artificial intelligence;
• Credit risk analytics.

We particularly encourage submissions that not only develop novel AI-based models but also provide rigorous mathematical analysis, transparent model interpretation, robust empirical validation, and meaningful implications for real financial markets. Contributions that bridge the gap between mathematical theory and financial practice are especially welcome.

Dr. Tianxiang Cui
Dr. Shusheng Ding
Dr. Anthony Graham Bellotti
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 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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• artificial intelligence in finance
• mathematical finance
• machine learning
• deep learning
• reinforcement learning
• financial time series analysis
• portfolio optimization
• algorithmic trading
• risk management
• explainable AI
• Bayesian learning
• stochastic modeling
• financial forecasting
• graph neural networks
• financial networks
• green finance
• ESG risk
• systemic risk
• quantitative finance
• mathematical optimization
• credit risk analytics