This white paper is based on primary research by A-Team interviewing senior IT and Data Strategy managers at tier 1 and tier 2 banks.
Risk management has been accepted as the new imperative for financial institutions of all types and sizes. But for Tier 1 and Tier 2 banks and brokerages, the complexity of their organisations is presenting risk professionals with a thorny data management challenge: How to gather, normalise and synchronise information from a broad array of internal and external sources that provide the data required to drive their risk analytics and management systems?
Big Data has emerged in recent months as a potential technology solution to the issue of dealing with vast amounts of data within the enterprise. As in other industries, financial services firms of all kinds are drowning in data, both in terms of the sheer volume of information they generate and / or have to deal with, and in terms of the growing and diverse types of data they confront in those efforts.
In the aftermath of the financial crisis, the period from 2009 through 2010 saw liquidity risk rise from relative obscurity to a position of prominence on regulatory agendas worldwide. One year on, liquidity risk management remains high on the agenda for institutions across the global financial services industry.
As elements of the global financial markets regulatory puzzle are slowly pieced together, the case for enterprise risk emerging in 2011 as the long-awaited ‘killer app’ for enterprise data management is growing.
It seems that risk management is never far from the minds of the boards of financial institutions at the moment, given the increase in regulatory and client scrutiny of this space in the post-financial crisis environment. In order to meet the slew of regulations on the horizon, firms are being compelled to invest in their risk management systems to keep on top of their exposures, be they credit, market or liquidity related.
