Investment banks deal primarily in information. It’s the lifeblood of the industry. The applications for data analytics in banking would therefore appear to be many. For instance, it can be used to analyze the effectiveness of a deal, and offer insights as to which trades they did or did not win on a client-by-client basis. It seems only logical, therefore, that banking would have been among the early adopters. And in some areas, it has been. It has proved invaluable for risk management in particular. However, there are a number of issues that have hindered its take up.
Firstly, having huge amounts of data is all well and good, but it requires an exceptional research structure to be in place if it is to be of any use. If your data seems to be telling you something but there’s no apparent reason for it, it tends to be because there’s some spurious event occurring, an aberration that’s contorting the data for a limited period of time. Without a research team in place to tell when this this in the case, banks could make investments based on something that will never happen again.
There is also the more practical issue of putting the systems in place to make use of the data. Unlike retail and technology giants such as Google, Facebook and Amazon, or any new startup or fintech company, the IT and data systems at most banks were not originally constructed to analyze structured and unstructured data. Banks must undergo the costly and time consuming process of updating and remodeling their entire IT and data systems if they are to accommodate the systems needed to generate a deep analysis of their data.
Another thing holding banks back is privacy issues. Research indicates that 62% of bankers are cautious in their use of big data due to privacy issues. There are also several regulatory restrictions that are having an effect on the use of big data in investment banking. Banks have to comply with the Basel Committee on Banking Supervision (BCBS) 239, which outlines 14 principles around risk data aggregation and risk reporting. According to Rupert Brown, CTO of Financial Services at MarkLogic, BCBS 239 is ‘focused on understanding the provenance, lineage and classification of data and is probably the most significant regulation’.
Banks must also abide by is the US Securities Exchange Commission’s Regulation Systems Compliance and Integrity (Reg SCI). This forces certain market participants that are necessary for the functioning of the US securities market to ‘have robust technology controls and promptly take corrective action when problems arise’, which has a huge impact on the operating procedures and management of banks’ datasets.
These are all valid concerns, however, banks are starting to realize that investment in data analytics is necessary, with 60% of financial institutions in North America saying that they believe big data analytics offers a significant competitive advantage, while 90% think that successful big data initiatives will define the future winners. Although regulations can be hard to comply with, big data and analytics can offer a cheaper way of paying compliance costs. It does, however, need to be used right, and this takes time and money.