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FICC: How to Profit from Necessity

First Published 9th May 2016

Regulation is at the top of many bank agendas these days, with multimillion dollar compliance projects now commonplace. But as Matthew Hodgson, CEO of Mosaic, and Andy Webb, Automated Trader's founder, explain, banks have yet to seize the opportunity to leverage this existing regulatory investment to generate profitability and competitive edge in FICC.

The scale of recent bank compliance investment is vast. In 2013, JPMorgan added 4,000 personnel to its compliance team and spent USD1bn on controls. In 2014, UBS spent USD1bn on meeting regulatory requirements, while more recently Citigroup reported that it was recycling USD2bn of USD3.4bn cost savings into compliance spending. Goldman Sachs has also been active, with more than half its new headcount of 2800 in 2015 being in compliance.

The motivation for this activity isn't hard to find. According to McKinsey1, among the largest 20 US and European banks, total operating income fell by ~10% between 2009 and 2014, while credit impairment costs fell by ~50%. But over the same period regulatory fines and settlements rose by ~800%+.

Data opportunity: compliance AND business

A substantial proportion of this compliance investment and headcount is currently going into data management projects that will consolidate client data (including trading activity) across all business units. Curiously, banks' fixed income businesses do not appear as yet to have picked up on the bigger opportunity here: business intelligence. If you're having to spend millions on consolidating data anyway, why not squeeze the maximum out of that major investment with a marginal incremental investment that will yield a large return?

Top flight business analytics that can deliver this return aren't just about a flexible and powerful GUI (though that's important too). They will also incorporate a real-time data normalisation and enrichment layer, which can correct or infill invalid or missing data. This homogenous data consistency can then be used to enhance the core compliance project AND deliver valuable business intelligence on top.

For instance, a benchmark rate might be missing from the bank's own internal resources, so creating a compliance report requested by a regulator would on each occasion require manually sourcing this data and inserting it into the report or report calculations. The normalisation and enrichment layer of the right business analytics package will identify, source and insert this missing data automatically, thus streamlining regulatory compliance and reducing its cost.

Boosting the bottom line

While value-add to an essential compliance project has the benefit of making this sort of solution a justifiable inclusion in the compliance project budget, the profit angle of such a solution comes from the business intelligence it can deliver. With high quality normalised data in place, the tool can provide users with maximum flexibility in slicing data to generate views that maximise insight and profitable opportunity identification.

A conservative estimate of the scale of this profit opportunity is shown in Figure 1. This example illustrates how just marginal improvements can sharply boost the bottom line in two stages. In the first stage, there are baseline improvements to participation rate, hit rate and spread retention. In the second stage, the quality of information now available enables an additional layer of management improvements in these areas, further boosting net revenue - in this case by a total of USD25mn.

In enterprise wide deployments, high quality business analytics can actually deliver far larger financial gains; the example in Figure 1 is just for a single Tier 1 client and a single major venue. Extrapolate this illustration across multiple FICC clients and venues and the transformative nature of the opportunity becomes obvious.

Figure 1: High quality business analytics: P&L benefits


Business intelligence

The positive buyside response that underpins the numbers in Figure 1 is borne out by independent research, with a recent Greenwich Associates report2 underlining the salient point:

"Understanding each individual client in detail - the contents of their portfolios, how they like to interact with the market, what news matters to them - is what really makes one broker stand out from another. ...as our interviews with the buy side demonstrate, they are willing to reward those best at providing such customized service."

The crux of delivering this type of service is an ability to make optimal use of historical and real time client data, at both a collective and individual level, to take the most profitable action. For instance, knowing how the flows of a particular German life insurance company differed over the past three months from its peers is useful, but being able to isolate the factors that drove that differential and act upon them to create additional revenue is invaluable.

As yet, this doesn't appear to be happening in FICC businesses, so there is a definite opportunity gap here. As the recent report pointed out:

"Although nearly every firm interviewed indicated they were collecting detailed data on their clients' trading patterns and investment portfolios, roughly half felt they were not utilizing the data effectively."

The key to using this data effectively is predictive analytics: given the normalised historical data now available, what might happen next and how best to profit from that? This will typically combine analysis and prediction of separate data points that collectively constitute the profit opportunity. For instance, combining predictions of which clients are likely to be in the market during the current session, what products they will be active in and whether they will be buyers or sellers. From that combination of predictions a wide variety of possible actions might be triggered. These could range from a sales trader's call highlighting a buy/sell opportunity in a particular issue that is likely to meet the client's needs, to simply adjusting a price on an electronic platform automatically.

The value of predictive analytics has been flagged for some time - a Gartner report from 2014 anticipated that firms using them would increase their profitability by 20% by 2017. Combining those predictive analytics with a high quality tool that already offers robust real-time data normalisation and enrichment pushes the return on existing compliance spending to the maximum.

Putting it all together: delivery and visibility

The scale of the challenge facing fixed income desks goes way beyond current compliance project implementations. The headline problem is profitability, or rather the lack of it. Figures from The Economist highlight that between 2009 and 2013 FICC revenues for the world's largest banks collapsed by 48%3, with further declines in 20144 and 20155. However, declines of this magnitude also suggest that profitability may not be the only issue here, outright survival (at least in the current form) may be becoming another.

The good news is that the core data required to turn this situation around is already becoming available (or is already available) through compliance projects. Therefore, those FICC businesses' willing and able to use that data intelligently, now have their destiny in their own hands.

The even better news is that the cost of implementing high quality business analytics capable of reversing the depressed FICC situation is marginal in the context of existing regulatory spend. Furthermore, the delivery options for leading analytics tools that can be used to maximise the FICC profit opportunity (while also enhancing regulatory performance) are almost limitless.

The data normalisation and enrichment layer of best in class solutions opens the door to 'source once, deploy many', where a flexible standards-based GUI delivers information consistently across multiple platforms (PCs, tablets, phones etc). However, the vital point is that this layer also lends itself to further profitable exploitation via API. Standardised data can thus be fed to in-house custom analytics and/or to a machine learning library for further analysis and action. This in turn enables many forms of automation, ranging from pricing engine adjustments, to automated generation of priority call lists for sales traders. These could range from general guidance, such as projected possible outcomes for a range of actions, to explicit instructions. This also represents the fifth and final step in building bottom line gain and business viability on the back of regulatory compliance. An illustration of all five steps can be seen in Figure 2.

Figure 2: Five steps to greater FICC profitability


Conclusion

Ultimately, FICC businesses now have the chance to completely reverse recent declines. The right business analytics solution coupled with existing regulatory data investment will make the business more competitive and profitable by growing market share and margins. But this is only the immediate benefit. In the longer term, this combination also opens the door to improved strategic analysis, plus better team morale and enhanced productivity. Furthermore, where an analytics solution also offers metadata analysis, the resulting understanding of how users interact with the technology can be recycled into making its use even more efficient. The end result is a virtuous business intelligence circle that can be leveraged to future proof FICC's profitability and survival.


[1] http://www.mckinsey.com/business-functions/risk/our-insights/a-best-practice-model-for-bank-compliance

[2] "The Big Sales Trading Upgrade", Greenwich Associates, Q1 2016

[3] http://www.economist.com/news/finance-and-economics/21600992-engine-investment-banking-spluttering-ficc-and-thin

[4] http://thetally.efinancialnews.com/2015/02/ficc-revenues-continue-plummet/

[5] http://www.reuters.com/article/us-banks-investment-trading-idUSKCN0VV007






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