SteelEye Unveils Tool to Slash Trade Reconstruction Timeframes

London-based regtech provider SteelEye has rolled out an Auto-Trade Reconstruction tool that aggregates all structured and unstructured data relevant to a transaction, to help firms meet the compliance requirements and timeframes of regulatory reporting obligations.

The vendor originally planned to release Auto-Trade Reconstruction in the first quarter of this year, but delayed the launch until now after the Covid-19 outbreak, to focus on meeting demand for its existing services around trade surveillance and compliance.

The tool—which is automatically available to all clients of SteelEye’s Trade Surveillance product, a software-as-a-service platform that can be fully managed or deployed in a client’s own Amazon cloud—captures data such as trade and order activity, chat conversations and phone calls, tweets, emails, and meeting minutes to create a full view of any communication and activity relating to a trade or security. Dodd-Frank in the US, Mifid II in Europe, and the UK’s Market Abuse Regulation (MAR) all have provisions whereby regulators can demand all records relating to a transaction or series of trades. Dodd-Frank, for example, requires this data to be produced within 72 hours.

Given the breadth and complexity of data points and interconnections that must be captured and monitored, being able to collect and present the data to regulators within their specified timeframes has proved to be a challenge for many firms, says SteelEye CEO Matt Smith (pictured).

“Just getting the data together that’s needed to achieve reconstruction is a hugely-complicated feat—let alone connecting it all,” Smith says. “But the way we built SteelEye was to focus on bringing data together. SteelEye is effectively a data platform that aggregates unstructured data, such as emails, Slack conversations, voice, news, social media, order and trade data—we support more than 120 trade and order management platforms—as well as CRM data for companies and the people within them, counterparty data, employee data—such as from PeopleSoft or Microsoft Active Directorya—and more than 150 million instrument reference data points and market data from several major vendors.”

The vendor then uses artificial intelligence and supervised machine learning that is trained to make the correct associations between securities and other data in order to reconstruct any information relating to a trade. As a result, SteelEye is able to shorten the time required to produce trade reconstruction reports from days to “seconds,” Smith says.

“A firm wants to know every time within a set timeframe when someone references a security linked to a transaction,” he says. “You’re looking at transactions, human behavior, market movements, news, and everything else to find out what did your firm know, what did individuals within—and outside—your firm know, and what was going on in the market. It gives clients the ability at any point to say ‘Show me everything for the past 24 or 48 hours about a specific stock and an individual or trader,’ and then we show them that, together with news and trading activity.”

From Compliance to Competitive Advantage

Once a client has the data points required for trade reconstruction assembled and correlated, they could also use that to create additional value. Smith declines to detail any specific ways clients are using the data to gain a competitive advantage, but says that any firm could feed the results into their risk engine, or use it to run P&L reports to gain a more accurate overview of their activity.

“Why not use a solution originally developed for regulatory compliance to gain more commercial benefits?” he says, warning that you can’t expect to gain any advantage from data unless you have already invested time and effort in cleansing and structuring the data. “Unless you spend time looking at your data, you won’t be able to address these challenges. If you do, you can use data in ways you’ve never done before. But if you don’t get the data right, forget it.”