Financial institutions often sit on vast quantities of data which is bespoke to their business, including transactions, communications, and referential data sets. The challenge is to bring these disparate, fragmented data sets together in real-time to be normalised as clean, accurate, and complete for use within cutting edge machine learning AI models. Only then can you extract actionable, revenue-increasing insights at scale to supercharge your front office sales and trading teams to deliver greater value to your clients.