Zurich AI

Following the successful 2024 conference with participants from around the world, we are pleased to announce the 2nd Modern Finance Conference (MFC 2025). We invite researchers, academics, and practitioners in finance to present their original research and engage in stimulating discussions to advance financial knowledge and research excellence. This conference aims to provide a platform for the dissemination of cutting-edge research, innovative ideas, and novel approaches across all areas of finance.

The program will include keynote addresses, research papers presented in concurrent sessions, poster presentations, and panels with journal editors. The conference will be held in a hybrid format, with separate in-person and online sessions.

The training week provides a comprehensive introduction to Explainable Artificial Intelligence (XAI), emphasizing the methodologies and practical applications of cutting-edge models such as PDP, ICE plots, LIME, SHAP, and others. Participants will explore how these techniques enhance interpretability and transparency in AI systems, along with the challenges they face, such as scalability, interpretability trade-offs, and accuracy limitations.
The course also investigates the limitations and reliability of XAI models when applied to complex datasets, with advanced discussions on their performance and practical constraints. A distinctive focus is placed on financial applications, examining how XAI can address the unique challenges and regulatory requirements of the financial sector.
By the end of the course, participants will gain the expertise to implement XAI models, critically evaluate their effectiveness, and apply them responsibly within financial systems, fostering trust and compliance with regulatory standards.

Prior Knowledge: Participants are expected to have a foundational understanding of machine learning concepts, including supervised and unsupervised learning, common algorithms, and evaluation metrics. Certain familiarity with Python programming is needed, with experience using libraries such as scikit-learn, pandas, and NumPy. Familiarity with basic statistics and linear algebra will also be helpful for understanding the mathematical foundations of explainability methods.