Machine Learning in Quant Finance

This marcus evans conference will offer business cases for machine learning to achieve process efficiency and improve analysis through the lens of quants in finance. Industry experts will come together to discuss how quants have acquired large data sets and developed the programming for machine learning with appropriate controls in order to extract meaningful insights for the business.

Using data to make smart business decisions through analysis is nothing new, and is something the leading market players have done to their advantage. But with data increasing in financial institutions, the ability to do this well and use all the factors and information to derive analysis becomes a harder task. Machine learning is stepping in to handle this problem with its ability to form patterns from structured and non-structured data. With this in mind, the march towards machine learning, with all its benefits and solutions it has to offer for optimisation and risk analysis, has been difficult to ignore, and quants are one of the key stakeholders flying the flag for this march. The skill set of quants - considering their experience in statistical analysis, quantum programming and maths - lends itself to driving forward machine learning projects, so it is only natural that financial institutions use existing resourcing to drive forward new projects.