Tag Archives: risk management

Taking a consolidated view of counterparty risk

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News of the huge loss as a result of unauthorized trading at UBS this week immediately impacted everyone holding positions with the firm, and  has placed the need for a single common legal entity identifier (LEI) beyond debate. For traders, their clients and regulators, an immediate,  consolidated view of counterparty risk across asset classes, desks and geographies is now a bare necessity.

LEI standards are being developed to replace the intricate patchwork of counterparties and ownership structures that currently comprise each transaction. But, introducing standards on a global basis across the financial services sector has never been easy and discussions on what the impact would be from a practical perspective continue to vex the data management industry.

Nobody yet knows what the final LEI standards will look like and how they will be implemented in practice. One thing that is certain however, is that standardization around legal entities will create a huge data management headache for firms running off creaky proprietary systems. Firms simply cannot afford to try and accommodate the onslaught of regulatory change, of which LEI is only one, from what is, essentially, a standing start.

Indeed, LEI isn’t just another box to tick on an audit or compliance form; it goes right to the heart of a firm’s counterparty risk management and, for fear of sounding melodramatic, being able to respond rapidly is essential to minimize losses, or even ensure survival.

Getting your house in order and putting the right system in place now is essential. Markets move too quickly for firms to respond via manual processes, and spreadsheets alone will be left behind. Moreover,  If you invest in the infrastructure to spot these issues, and take appropriate action quickly, the shape, size and format of LEI won’t be a cause for concern, which leaves you able to focus on even more complex regulatory requirements that continue to cloud the horizon.

Beyond the golden copy

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Far too many people in the data management industry think that there is a one-size-fits-all, static solution that will solve all data-related problems – and they spend a lot of their time promoting this idea. In reality there’s no such thing. This tired old fallacy has been hauled out for far too long, and unfortunately there are too many organizations that are just beginning to realize that what they’ve bought isn’t a solution – it’s just another problem.

So let’s go back to basics. Any data management infrastructure has to be appropriate for the size of the firm and the type of operation. The solution that is right for a 40-person hedge fund is very different from the solution needed by a global custodian with thousands of customers and tens of thousands of employees.

Most firms have multiple business units, product lines, and investment strategies – all of which require different data sets used in different ways. Accounting and risk management will need different data sets than the trading desk. Operations want data on actual holdings, so analysts can use it for modeling ‘what if’ scenarios. The idea that you can impose a monolithic, inflexible data management structure with a single data set and a single management tool, onto the modern business with all its complexities is manifestly false.

So let’s have a more realistic conversation about data management. And let’s start by calling out old-fashioned ideas about data management, and exposing them for the myths that they really are.

An agile approach to accurate data

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Accurate information is the data management industry’s Holy Grail. It’s fundamental to what we do and what our clients do, and its importance is undeniable. But there is more to data than accuracy.

Accuracy by itself is useless if your people can’t get hold of the data they need when they need it. What might have been accurate this morning may not be at the end of the day. So accessibility is critical. There’s also no point in being accurate and accessible if your people can’t then act on that data.

In today’s world, most firms have multiple business units, product lines, and investment strategies – all of which require different data sets used in different ways. Compliance and risk management will need different data sets than the trading desk. Operations want data on actual holdings, while analysts use it for modeling, stress-testing and ‘what if’ scenarios. Clearly there’s no single solution for these distinct requirements.