Data mining, machine learning and problems with autocalls - Risk.net

Experts warn ML should be used "for its correct purpose" – not for prying long-term strategies from sparse information Data is the latest of many hopes for banks and other investors looking for improved returns in a lacklustre environment. Several banks have begun to point their research teams at big data – using internal data, purchased databases or new research to collect huge quantities of data points, which can then be analysed using the new technology of machine learning (ML). UBS seems to be in the lead at present, but Morgan Stanley, BNP Paribas and many others are following. And this combination is being applied elsewhere as well; last week Risk looked at HSBC's client intelligence unit, which is aimed at using internal client data to generate new sales leads for existing customers. Standard Chartered's data analytics group earned a 2019 Risk Award for quant of the year for its head Alexei Kondratyev, based on the group's machine learning work.