Return of THE Rogue Trader – but can the next generation be caught? (By Dave Tolladay, Alerts4 Financial Markets)
It’s a sequel worthy of any summer blockbuster. Five years on from his starring role in the scandal that cost UBS a box office-shattering £1.3 billion, Kweku Adoboli is back! Only this time, he brings a telling message for the City – claiming that banks need to do much more to stop fraudulent behaviour and restore public trust.
While at first this appears to be the financial markets equivalent of OJ Simpson advocating tighter gun controls – it is actually hard to be too cynical about these comments. After all, critics of financial regulation, of which there have been many, also point towards a lack of market expertise across the industry. Adoboli may have honourable intentions, but the reality is that from a surveillance and monitoring perspective, the City is a very different place to five years ago. Significant advances in technology have driven a major shift in behaviour during Adoboli’s time off the scene. With this in mind, what exactly are the signs that a bank could have the next rogue trader on its books?
Firstly, it’s important to note that the symptoms of a rogue trader are not standalone issues, which is what existing surveillance systems typically pick up. Much more often, it is a combination of a number of different events linked to multiple different sources of information that give a clearer indication of abnormal behaviour. The trouble is that these days, there’s a huge number of potential symptoms. Perhaps you’re seeing someone cancel an unusually high number of trades prior to settlement, or maybe it’s simply that they don’t seem to be taking enough holiday. And the tell-tale signs are certainly not confined to the trading floor. Some firms are even exploring the idea of building turnstile swipe pass systems into their compliance operation – all to see whether the trader is at his desk or not when making a trade.
In isolation, none of these events is likely to ring the alarm bell, but combinations of this sort of thing happening over a sustained period, could indicate a developing problem. After all, rogue trading is never just one trade, it is a pattern of trends over time where traders often try and disguise what they are doing once they’ve got themselves into a loss-making position. While cases like UBS have certainly led to boardroom big wigs throwing more money at the problem – rogue trading is still viewed as a siloed problem to address. The approach taken so far isn’t stopping the Adobolis of this world, which is why rogue trading should be seen as part of the broader world of trade surveillance. Ultimately, someone intent on breaching company controls as part of an unauthorised trade is likely to be involved in other forms of suspicious activity. From unusual interactions with the middle and back office, to IM conversations with fellow traders that suspiciously read like someone is covering tracks – activity can be wide-ranging.
Whether it’s Adoboli or even Nick Leeson of yesteryear, history tells us that rogue trading is a once in a generation risk that can bring down any firm. A fine for a spot of insider dealing or front running is not the same as a £1 billion fine for booking fictitious trades to mask gambling in the hope of bigger returns. Only by grasping the behavioural patterns by looking at trade surveillance and rogue trading together, can banks hope to avoid becoming the UBS of tomorrow. And let’s face it, in this world of low rates and low returns, no bank can afford to play the lead role in the next installment of a future rogue trading saga.
Dave Tolladay is Director at Alerts4 Financial Markets
With Joe Schifano, Global Head of Regulatory Affairs at Eventus Systems.
Exec notes growing interest in cross-market and cross-product surveillance.
A dynamic, proactive approach is needed to continuously improve surveillance.
Some material changes have come out of ESMA’s review of algorithmic trading.
Technology has enhanced capabilities of surveilling larger and more disparate data sets.