Global

Looking at recent earnings for banks such as JP Morgan and Goldman Sachs, you would be mistaken for thinking the good old days of gangbuster returns are back for bankers. Despite lower returns on trading, the M&A boom has been good for the industry’s hottest dealmakers. With all that cash, how should they be deploying it? By Alexander Dorfmann, senior product manager at SIX

In recent years, banks have invested in internal operations, in particular for ensuring compliance with the many regulations that have come into law in the past decade. Anything deemed to be good for business or the image of the bank has been invested in, with high-tech next-gen projects being the latest craze, from blockchain decentralised finance (DeFi) systems to proprietary data analysis tools.  

But as the tsunami of regulation has momentarily ceased, and financial institutions are now focusing on their core business again, institutions need to ask themselves whether there is any need to invest in expensive in-house regulatory compliance projects.

Even if we see further evolution in regulations, such as the next iteration of Basel, these changes tend to be incremental, not all-encompassing. This means banks need to examine in a more subtle way of automating these processes to reduce costs, rather than putting large-scale projects in place to meet the requirements.

For those in the bulge bracket, the cost pressures are not quite as strict. JP Morgan can afford to spend more than $10bn a year on its own technology while continuing to sit close to the top of the tree. However, the situation is much different for those in the small and mid-range regional bank category, as these banks do not have the funds or the scale to competitively develop cutting edge technology. And nowhere is this need greater than regulatory data analytics.

While regulations are firmly in place — from the markets in financial instruments directive (MiFID) II, the fundamental review of the trading book, the Basel Committee on Banking Supervision’s 239 data standard and Securities Financing Transactions regulation — banks face several data challenges on multiple fronts. In most cases, the data needed to comply with a new regulation is already available internally, but requires new tools to find and analyse it before it is usable. The need to access fragmented data has led to new forms of flexible delivery for pricing and reference data, including mobile data solutions. Additionally, the growth of advanced analytics, such as artificial intelligence (AI), enables banks to better and faster identify meaningful correlations in data so that anything relevant for MiFID, for example, can also be deployed for Basel requirements.

A key requirement  

In this context, the further automation of previously manual workflows to be compliant with business needs is a key requirement as banks want to acquire new technology and invest in advanced analytics.

While necessary for smooth operations and enhanced compliance, banks would be mistaken for thinking that this is something that needs to be developed internally. Firstly, the IT power to do this effectively is significant, and far beyond what is currently capable at many financial institutions. Also, IT is developing too fast to stay on top of every new trend, making it risky to take a bet on proprietary technology development. Secondly, to get to this position, an equally significant amount of investment is required — something not available to a large proportion of mid-sized banks whose portfolios are too small to afford.

And it is not just with these mid-range banks where the spending conundrum comes up. Even for the bigger banks, are the strong revenues they are currently seeing best invested outside of their core business? If you think about it this way, there is no need for banks to invest in developing a canteen to feed their staff or visiting clients — it is without question that providing food is outside of a bank’s core business — especially as every office in every major financial hub is surrounded by cafes and restaurants that do this much better.

This applies equally to banks building their own regulatory data and analytics capabilities. Investing into proprietary analytics only makes sense if this offers a significant advantage over their competitors in the core business. For example, with advanced currency analytics capabilities for banks dominant in foreign exchange trading to use AI and similar tech to investigate, read and monitor alternative data and trading signals so they can offer bespoke services for their institutional clients. But this is not the case for small and medium-sized banks servicing retail customers, who need to implement daunting regulatory workflows.

Third-party providers are much better situated to deliver this, not only the more traditional regulatory analytics, such as pulling price and risk calculation from MiFID II data, but also more complex analytics that utilise AI and machine learning. As banks require the kind of real-time and complex data analytics available using these new technologies, so will the capability of third parties to meet these needs.

Anyone on the board at a large to medium-sized bank will need a persuasive argument as to why they should deploy their hard-earned cash on what many firms can do at a fraction of the price.

A Speakers’ Corner is an area where open-air public speaking, debate and discussion are allowed. The original and most noted is in the north-east of Hyde Park in London

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