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Recently, financial institutions have come under fire for a not illegal, but potentially suspicious form of investing. And while not especially complex in its execution, this scheme exposes the lack of insight and oversight across large institutions, especially institutions that operate across different geographies. Of course, I’m talking about mirror trading.
‘Mirror trading’ is a legitimate strategy where a trader tracks and copies trades of another trading/investment entity. The original intent is to allow traders to automate trading decisions based on track-record proven, expert strategies. However, when done in large quantities, mirror trading can also be used to facilitate money laundering since these trades may bypass currency controls and anti-money laundering laws when moving money overseas.
Since mirror trades have only recently been used to circumvent money laundering controls, many financial institutions have not been proactive in monitoring for mirror trades.
Bigger you are, it’s harder to see
One of the main difficulties in detecting this kind of activity is the sheer size of institutions themselves. While some isolated aspects of these schemes could raise reasonable suspicion, captured together they would undoubtedly paint a clear picture of the unfolding arrangement and trigger a “red flag,” if not several. In a most recent case, these “flags” included:
- Reoccurring pattern: A steady flow of smaller trades, typically $2-3 million each, but totaling to billions of dollars over a few years
- Uneconomic trading: The parties lost money on the deals due to fees and commissions
- Common beneficial ownership: Clients were closely related and having a stake in the same corporations
- Suspicious trade instructions: An interest in offloading currency into any security, with little regard for the prospective outcome of the investment
As financial restrictions and sanctions are applied more broadly and rigorously around the world (under the current US administration, the Treasury Department has added over 700 people, companies, and government agencies to sanctions lists) money-launderers are becoming more creative and may utilize ever-increasing sophisticated trading schemes to hide their activities.
In addition, these trades typically result in lucrative commissions and fees for financial organizations, disincentivizing staff from raising alerts about the trades or applying vigorous oversight and controls. Furthermore, in many institutions, customer relations are poorly defined and the systems for storing such information are fragmented, leaving the institutions in the dark about who they are trading for and the origins of the money for the deals.
Laying the groundwork for consolidated surveillance
To effectively uncover such schemes, institutions need to build towards an ‘unbiased’ automated surveillance approach, deriving insights from customer data collected from both internal and external sources, as well as trading data and voice and electronic communications, to paint a full and accurate picture of activities and behavioral patterns across the organization.
While easier said than done, it is this convergence of detecting activity and understanding context that is at the heart of IBM’s approach to financial crime. Traditional solutions focus on statistical analysis and rules to find exceptions in transactional activity. However, focusing instead on better understanding relationships among entities, and incorporating contextual data from text, electronic communications, voice and trading data can provide the level of insight needed to spot these types of events with a holistic view across traditionally siloed data sources.
IBM provides financial services firms the ability to know every customer across their global footprint continuously through the customer lifecycle by tapping into structured and unstructured data sources within the firm. IBM enables our financial institutions to understand the demographics, the behaviors and the relationships of their customers to make better and unbiased surveillance decisions faster and more consistently. IBM helps financial institutions to increase the effectiveness of their surveillance program through effective use of advanced analytics, enterprise scale and continuous improvement using machine learning, leading to automation and the decreasing the costs of running such program.