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Why Banks Aren’t Looking in the Right Places to Tackle the Profitability Problem

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By Jay Patani, Tech Evangelist at ITRS

Revenues at the world’s top investment banks are at their lowest levels since the financial crisis in 2008. According to research firm Coalition, the top 12 investment banks’ revenue fell to $150 billion in 2017, a 4% decrease from the previous year.

A sizeable part of the decreasing revenues is from declining income from trading in equities, fixed income, currencies and commodities. Declining revenues also meant that return on equity, a key indicator of banking profitability, fell to an average of 8.6% last year, a level at which most banks cannot even recover their cost of capital.

A more volatile start to 2018 has given investment banks renewed hope, with traders potentially capitalising on higher commissions and reaping greater profit from fluctuating prices. But while the front office gets all the attention, the underlying IT infrastructure propping up trading activity is often archaically perceived as a necessary, unchanging blotch on the budget. The only mention of trading technology is when things go wrong.

In reality, IT trading teams and systems are the unsung heroes of the banking world. Better trading technology can enable traders to reap more on the upside and mitigate losses when things don’t look so rosy. So, at a time when investment banks are faced with huge cost pressures and are looking hesitantly into the future, bankers can’t afford to just look in the obvious areas to seize opportunities. By making their trading technology more efficient and improving capacity procedures, investment banks can deliver more profits at lower costs.

What lies beneath

Trading involves a complex interaction of numerous systems including routers, execution management software, order management software, gateways, message queues, market data systems, risk management software and many more. The underlying infrastructure that supports trading is far from simple. It isn’t just the software responsible for executing the trades.

On a normal day, managing this infrastructure is difficult. Potential capacity bottlenecks are everywhere, which could impact latency or reliability and, in turn, cause client dissatisfaction – something that presents an obvious risk to revenue. Being able to manage and plan capacity therefore becomes crucial for banks to not only support current levels of trades, but to make informed infrastructure decisions based on expected future demand. Taking this strategic view of trading’s ‘pipes and plumbing’ can ensure a bank’s ability to exploit maximum revenue opportunities at times of peak demand.

On an unpredictable day, the problem of managing capacity becomes even more acute. Technology teams must tread a fine line between having enough spare capacity to cope with any unexpected fluctuations in demand and ensuring costs do not spiral out of control and become just another culprit of the profitability issues plaguing today’s market. Just as traders need to respond with agility to the whims of the markets, so too must IT infrastructure be able to scale in and out based on demand, without room for error. Reducing wasted capacity is only beneficial if it is paired with forward planning and quick scaling to meet demand when it returns.

One step ahead

Traditional, and now outdated, methods of capacity planning involve manually collecting all performance data and inputting it into spreadsheets to calculate capacity need. This is far too inefficient for an industry burdened by cost pressures and the need to deliver valuable insight quickly. Data can now be extracted in an automated way from a myriad of data systems that historically existed in siloes.

Identifying any spare IT capacity in an organisation requires analysing historical IT performance to highlight underutilised areas of the IT estate. The key objective for enterprise IT teams is to decommission or reassign under-used IT resources, while ensuring that existing infrastructure is still able to handle expected load with sufficient headroom for unexpected fluctuations.

With help of predictive algorithms, investment banks’ CIOs and IT teams can learn the exact relationship between changes in trade volume and the supporting IT infrastructure. They can then start to become more proactive in estimating capacity requirements to support future events before they hit. All in the name of maximising profit opportunities in the good times, while watching out for any wasted cost as soon as the tide turns.

Looking in the wrong places

Firms feeling the crunch of decreased profitability need to look beyond the obvious in their quest for a healthier bottom line. Staff cuts and reductions in risk have thus far done little to alleviate the pressure, so it’s time to look beneath the surface and rethink the infrastructure that props up trading. By connecting and analysing data from all corners of the business, banks can unearth significant untapped opportunities to streamline – not only to ensure rewards can be reaped from good times, but also to cut hidden waste on less sunny days.

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