Rooting out Rogues – The Role of Analytics

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By Dan Olds * Get more from this author

The big business news this week is how a junior trader making unauthorized transactions cost banking giant UBS at least $2.3 billion in losses. The loss tally is still mounting as the bank works to figure out exactly how big a hit they’re going to take.

Given that UBS’s market capitalization is around $42 billion, and net income was around $7.5 billion in 2010, a $2.3 billion loss is more than just a haircut; it’s closer to a scalping.

So how could something like this happen? Easier than one would like to think. In this situation, the trader was taking speculative positions on index futures – simply put, he was betting on where the markets (S&P 500, DAX, EURO STOXX indexes) would be at some future date.

While it was OK for him to make these bets, he was supposed to place offsetting bets that would limit the risk exposure of the bank. So if he took a position betting that the S&P would be higher at a future date, he was supposed to hedge that position with another transaction to ensure that the bank didn’t take it in the shorts if he was wrong.

Did our guy do this? Noooo… he faked the offsetting transactions; his trades only looked as if they were nicely balanced. In reality, he exposed the bank to huge amounts of risk – bad dreams that seem to have come true in recent days.

There was a big hole in the UBS trader management and risk control systems; that’s pretty obvious. The best corporations have risk management mechanisms that allow management to see organization-wide exposure at any given moment and how changing conditions could make the picture better (or worse).

Designing and implementing this kind of system isn’t trivial, particularly for a multi-national corporation. Accurately analyzing the data and providing up-to-the-minute real results – and predictions of future results – is even more difficult.

But all of this highly elegant (or brute force) programming and analytics are wasted if the overall solution has huge holes in it. If the traders can enter false trades, then the system can’t be trusted – as UBS has discovered.

And UBS is not alone. Bernie Madoff was able to issue false statements and pass regulatory muster for decades. The models used by ratings agencies to rate mortgage debt didn’t take into account systemic mortgage default risk… the list goes on and on.

The bottom line is that the most sophisticated systems can be rendered worthless by low-level, basic oversights – like allowing false trades to masquerade as real transactions. The lesson is to poke holes in your analytic systems and question even the most basic assumptions. To me, it’s much better to be known as ‘the guy who asks the dumb questions’ than ‘the guy who is now a cautionary tale’.

Dan Olds is CEO of Gabriel Consulting Group and Chief Editor of inside-BigData.

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