2nd Annual Machine Learning in Quant Finance 2019 | Marcus Evans (Europe) Ltd | 20 November 2019 | TBA, London, UK

This marcus evans conference will offer case studies on how financial firms have overcome challenges to apply machine learning within the confines of the financial market. The biggest challenge addressed will be that of increasing data availability and how financial firms are doing this through construction, mining and sourcing. Drawing on the business needs that machine learning can solve in the financial market, the conference will showcase the very cutting edge of machine learning that is already yielding results. In addition to looking at the successes, cases will also cover the horror stories where quants can analyse what and why it all went wrong as well as how such cases can be avoided through more robust models as well as an enhanced risk management framework.

In today’s market, machine learning is everywhere and is making the most amount of noise under the scope of AI with its ability to provide enhanced risk analysis and process optimisation. However, the development of machine learning in financial firms is no easy task given data, risk management and regulatory hurdles. Another challenge is the sheer complexity of financial problems making it crucial to build an intuitive model that can supply for the business needs.

2019 Speakers

Abhinandan Deb
Head, Global X-asset Quantitative Investment Strategies, Bank of America Merrill Lynch

Alexander Giese
Head, Quantitative and Digital Development for Trading, Unicredit Bank AG

Alexei Kondratyev
MD, Financial Markets, Standard Chartered

Andrew Green
Managing Director and XVA Lead Quant, Scotiabank

Paul Ward
Head, European Quantitative Equity Research, Deutsche Bank

Roberto Medina
Senior Machine Learning Engineer, Energias de Portugal

Adhil Reghai
Head, Quantitative Research Equity Derivatives and Commodities, Natixis

Mugad Oumgari
Managing Director, Global Head of Model Validation, Lloyds Banking