Data analytics is a critical component of success for financial institutions, especially investment banks operating in today’s competitive FICC markets. According to Celent, the US rates business, for example, has shrunk by 40% over the last five years, while new liquidity providers and trading institutions compete harder for market share.
In this fiercely competitive market, banks are increasingly turning to advanced data analytics in order to give them a trading edge over their competitors and maximise the profitability of their current business.
In response, analytics, including advanced machine learning and artificial intelligence (AI), are being explored by banks to integrate risk considerations (market, positional, counterparty), inventory, wider market factors and demand flow to allow trading desks to maximise effectiveness.
To aid them in this task, banks need to harness the goldmine of raw data they are sitting on to give them insight into the activity of their clients and ultimately enable them to leverage AI to improve service delivery to those clients who are most profitable.
But for banks that have numerous desks across their sales and trading departments, each with their own systems and methods of capturing data, how do you lay the groundwork necessary to achieve the ultimate goal of building an enterprise-wide view of client trading behaviour?