Often the first thing that comes to mind when thinking about trade surveillance is illegal insider trading (i.e., when a security is traded based on confidential information). Illegal insider trading grabs headlines, but there are many other illegal trade practices, such as spoofing—putting in a bid or offer on a security with the intent of withdrawing it before execution as a way to create false demand—and pump and dump—artificially inflating the price of a stock through false and misleading statements. These instances are not easy to find. They are buried deep within daily trading volume and communications, like needles in a haystack. In the case of spoofing, illicit cancellations can be mixed within the many legitimate cancellations that are a normal part of trading activity, especially with low-cost, high-volume instruments. In the case of pump and dump, misleading statements could be hidden in public message boards that then get reported in press releases or newsletters.
This is a big data problem. Consider the vast amounts of data analyzed, the variety of sources, the high frequency of trades, the complexity of cross-border transactions, and the overlapping layers of client relationships. All of these elements need to be connected and correlated to identify both the trading activities and the intent of the traders. Compliance officers are on the front line of responding to trading compliance requests. They need the data and the tools that will enable them to get ahead of potential issues by being able to act on questionable trades, navigate large volumes of data, determine the regulatory impact (if any), and respond accordingly. The tools must enable a creative, iterative, and collaborative approach by visually linking orders, executions, and trades to new types of data like electronic communications, message boards, and lower tier news sources.
To find the needle in the haystack, you must think beyond traditional data sources. Paul Lashmet explains how alternative data sources enhance trade surveillance by providing a deeper understanding of the intent of trade activities. To give just one example, data generated by machine learning routines that can identify suspect trade flow patterns at a very granular level. Then, specific transactions can be linked to newsletters and messages that have been analyzed for misleading statements. These nontraditional data sources help provide a deeper understanding of both the event and the intent.
Join in to learn how to kickstart your use of alternative data as a compliance asset through practical examples that have been employed by your peers in the industry.
Paul Lashmet is practice lead and advisor for financial services at Arcadia Data, a company that provides visual big data analytics software that empowers business users to glean meaningful and real-time business insights from high-volume and varied data in a timely, secure, and collaborative way. Paul writes extensively about the practical applications of emerging and innovative technologies to regulatory compliance. Previously, he led programs at HSBC, Deutsche Bank, and Fannie Mae.
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