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Bigdata analytics to curb rate fixing scandal

Foreign exchange market is world's largest and least regulated financial market. Its estimated daily turnover is $5.35 trillion, according to the Bank for International Settlements’ triennial survey of 2013. Speculative trading dominates commercial transactions in the forex market, as the constant fluctuation of currency rates makes it an ideal venue for institutional players with deep pockets – such as large banks and hedge funds – to generate profits through speculative currency trading. 

Though other financial markets are not as big as Foreign exchange market, their daily turnover will be in multi billion dollars. 

The modus operandi of rate fixing scandal 

Let’s say a trader at the London branch of a large bank receives an order at 3:45 pm from a U.S. multinational to sell 1 billion euros in exchange for dollars at the 4 pm fix. The exchange rate at 3:45 p.m. is EUR 1 = USD 1.4000. 

As an order of that size could well move the market and put downward pressure on the euro, the trader can “front run” this trade and use the information to his own advantage. He therefore establishes a sizeable trading position of 250 million euros, which he sells at an exchange rate of EUR 1 = USD 1.3995. 

Since the trader now has a short on euro and long on dollar position, it is in his interest to ensure that the euro moves lower, so that he can close out his short position at a cheaper price and pocket the difference. He therefore spreads the word to other traders through emails, chat rooms & messages, that he has a large client order to sell euros, the implication being that he will be attempting to force the euro lower. Thus, he sets an platform to fix the euro & dollar exchange rate. 

At 30 seconds to 4 p.m., the trader and his/her counterparts at other banks - who presumably have also stockpiled their “sell euro” client orders - unleash a wave of selling in the euro, which results in the benchmark rate being set at EUR 1 = 1.3975. The trader closes out his/her trading position by buying back euros at 1.3975, netting a cool $500,000 in the process. Not bad for a few minutes work! 

The U.S. multinational that had put in the initial order loses out by getting a lower price for its euros than it would have if there had been no collusion. Let’s say for the sake of argument that the “fix” - if set fairly and not artificially - would have been at a level of EUR 1 = USD1.3990. As each move of one “pip” translates to $100,000 for an order of this size, that 15-pip adverse move in the euro (i.e. 1.3975, rather than 1.3990), ended up costing the U.S. company $1.5 million. 

The modus operand is similar for the other fixing scandals such as benchmark rate fixing (example LIBOR), interest rate fixing, derivatives price fixing and etc,. 

Impact of these scandals on banks

  • So far, banks have been fined to the extent of $15 billion dollars for various scams including LIBOR rate rigging, interest rate fixing, fx rate fixing & misselling of payment protection insurance, and etc, by various regulating authorities. Already, it has impacted banks’ top line & bottom line.
  • Banking authorities are proposing to make it as a criminal offence on these manipulations.
  • Most importantly customer confidence is getting lost on these banks and eventually these banks are loosing their & market share & growth. 

How to stop the bug

Ethics, ethic & ethics

Banks do have policies, procedures and surveillance systems in place that are supposed to stop these kinds of collusions. How ever, they are clearly ineffective when dealing with employees who have the skills to manipulate the system.

Also, some times the employees are driven by the greed of top executives and are forced to rig the benchmark rate to gain undue advantage and to make huge profit for the company at the cost of their clients.

Hence it is imperative to imbibe an ethical culture deep root in the organisation. 

Proactive fraud analytical system

Banks trading platform including forex operations involve thousands of email communications, chat room conversations & instant messages. Hence it is essential for banks to deploy a smart system that is capable of integrating various structured, semi structured & unstructured data, ability to mine voluminous & variety of data and possibly with machine learning capability to filter potential alerts in a real time basis. These alerts can be investigated further by any case management system to identify any potential fraud and avoid them proactively. 

Detecting fraudulent employees through social network analysis

It is impossible to rig the rate without collusion with other like minded traders.

For example, Forex social networks are a fast emerging trend in the world of FX trading. They allow their members to see what other traders are doing, individually and en masse, in order to gain a broader knowledge of the market and the trading strategies of others. They are similar forums for many other types of such financial instrument trading. Banks should adopt a technical solution to closely track & monitor suspicious employees, by analysing his & his associate’s financial transactions, trading transactions, his internal communications and more importantly his social network communications. It will be possible to integrate these data, analyse them and to detect and classify potentially fraudulent employees.


Comments: (1)

A Finextra member
A Finextra member 24 July, 2014, 17:10Be the first to give this comment the thumbs up 0 likes

Many Banks have now come up with blocking bloomberg chats between traders post LIBOR fixing. Defintely chat, email text analysis against the trader placements will give more indepth view of any collusion happening. Good points. 

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