The GFMI 2nd Edition Machine Learning in Quantitative Finance conference will explore best practices for scaling AI across quantitative finance, with a focus on moving AI from proof of concept to enterprise-wide deployment. Gain insights from real world case studies, expert led panels, and peer discussions on adopting Large Language Models (LLMs), Agentic AI, and machine learning across quantitative research, portfolio management, trading, and risk functions. Core discussions will focus on building AI-ready data foundations, deploying scalable AI infrastructure, strengthening model governance and validation, and integrating AI into investment workflows while maintaining appropriate human oversight.
Attending This Premier GFMI Conference Will Enable You to:
- Leverage LLMs and AI for quant model development and risk management
- Develop production-ready machine learning models for stronger investment performance
- Implement Agentic AI & LLMs across quantitative research workflows to accelerate research productivity
- Leverage AI for fast computing to accelerate quantitative finance
- Transform alternative & unstructured data into investment intelligence to improve alpha generation
- Implement explainable AI (XAI) to strengthen investment decisions and regulatory confidence
Best Practices and Case Studies from:
- Manlio Battaglia Trovato, Managing Director, Head of Quantitative Research, Lloyds Banking Group
- Vincenzo Pota, Head of EMEA Data Science, Barclays Investment Bank
- Alejandro Rodríguez, Head of Quantitative Analysis and Artificial Intelligence, Miraltabank
- Moez Mrad, Managing Director Deputy Head of GMD Quantitative Research, Credit Agricole
- Hamza Bahaji, Head of Financial Engineering and Investment Solutions, Amundi
- Guido Baltussen, Global Head of Quantitative Strategies, Northern Trust Asset Management
For more information please contact Ria Kiayia, Digital Media and PR Marketing Manager at riak@marcusevanscy.com or visit: https://shorturl.at/JpuiE
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