Next-Gen Quantitative Risk Management: Models, Automation and Artificial Intelligence

Summary

Risk management is undergoing a quiet revolution. No longer confined to theoretical models and static assumptions, today’s leading practitioners are blending quantitative finance with cutting-edge technology — from modern IT infrastructure and automation to machine learning and real-time risk systems.

In this event, two experts from the frontlines of quantitative risk and investment management will share their personal and technical journeys:

• How risk models and quant roles have evolved — with a shift from Excel to code, from theory to implementation
• Why automation and performance optimization are reshaping how quants collaborate with IT, operations, and business
• How machine learning is accelerating model calibration and enabling intraday risk reporting
• What skills and mindsets are needed to thrive in a tech-driven, regulatory-conscious environment

Whether you work in risk, compliance, investment, or data science, this event will give you a grounded look at where the field is heading — and how to be part of the transformation.

We look forward to discussing these topics and sharing further insights.

Presenters

• Yves Allkofer, Modelling and Data Engineer at UBS
  Over the past two years, Yves has been working in Wealth Management at UBS in modeling and data engineering. Prior to this he holds the position of Head of Quantitative Strategies (QS) in Zurich and served as the global QS representative for commodities. His background is deeply rooted in quantitative finance across various asset classes, covering both front office and market risk. Yves holds a PhD in Physics at CERN from the University of Zurich and a master’s degree from EPF Lausanne on optimal stopping time.
• Andres Vargas Lugo Cantu, Director, Senior Data Scientist at Vontobel
  Andres Vargas is an experienced Data Scientist with extensive expertise in the agile development, deployment, and continuous improvement of machine learning-based products. As a Senior Data Scientist at Vontobel since 2020, he specializes in the end-to-end development of machine learning systems, from model research and validation to deployment, integration, and operationalization. His current focus is on developing neural network models for fast and accurate pricing of derivative products. At Vontobel, the models Andres has developed for the derivatives space are currently handling millions of predictions daily in production. Andres holds a PhD in Physics from ETH Zürich (2009–2014) and a Master’s degree in Nanotechnology from Delft University of Technology (2007–2008).

Introduced by Prof. Dr. Walter Farkas, Board Member Co-Chair Swiss Risk Association and professor at UZH and ETH and moderated by Urban Ulrych, Chapter Manager at Swiss Risk Association and Postdoctoral Researcher at ETHZ & EPFL.

Agenda

18:15 Arrival & Name Tag Collection
18:30 Welcome and Introduction (Walter Farkas)
18:35 Presentations (Yves Allkofer and Andres Vargas)
19:25 Panel Discussion followed by Q&A (Urban Ulrych and speakers)
19:45 Networking Drinks (all)
20:30 End

Chapter Event

As chapters evolve, synergies are found and the focus of our topics change, we will combine chapter “Risk Analytics and Models” and Stress Testing and Scenario Generation” into the new chapter “Analytical Methods”. We explore analytical approaches to risk management, with a focus on practical applications. Our chapter provides practitioners with a stage to share their latest ideas, tools and methods.