AI Meets Quantitative Finance

Stochastic Methods as a Two-Way Bridge

Organized by Patrick C. S. PUN (NTU, Singapore) • Phillip S. C. YAM (CUHK, Hong Kong)

Artificial Intelligence (AI) has transformed financial modeling from deep learning (DL) for pricing and forecasting to reinforcement learning (RL) for trading and execution. Yet the theoretical underpinnings remain underdeveloped for high-stakes finance. Quantitative Finance offers a rich toolkit, e.g., stochastic control, BSDEs, dynamic risk measures, that is interpretable and rigorous. These tools align closely with modern AI through perspectives like RL as control, mean-field games as multi-agent learning, and risk-aware learning via utility/risk functionals. We ask two core questions:

(i) how can advances in AI push the frontier of financial modeling and decision-making? and

(ii) how can tools from stochastic analysis offer principled insights into the behavior and improvement of AI methods themselves?

This workshop focuses on the synergistic relationship between AI and quantitative finance, emphasizing stochastic modeling techniques as both tools and objects of analysis. To this end, we explore a two-way dialogue: how advances in AI (e.g., DL, RL, generative models) can push the frontier of financial modeling, and how stochastic analysis tools (e.g., BSDEs, stochastic controls, mean-field games, risk-sensitive optimization) can improve the design, interpretability, and robustness of AI in finance.

Submit Your Paper via EasyChair

We invite high-quality research contributions to this ICAIF 2025 workshop "AI Meets Quantitative Finance: Stochastic Methods as a Two-Way Bridge." Submissions are handled via EasyChair and will be reviewed in a single-blind manner (author names and affiliations should be included in the manuscript). Best paper(s) will be selected and the submitting author will be invited to join our workshop to give a 20-min oral presentation. Other strong submissions will be invited for poster presentation.