A NICE Systems subsidiary has unveiled a machine-learning approach to help financial firms deal with regulatory requirements mandating that they are able to provide past records of trades.
NICE Actimize, the company’s arm devoted to financial crime, risk and compliance solutions, announced yesterday that it is adding a “trade reconstruction module” to its ”holistic surveillance” portfolio.
The technology uses automation to recreate completed trades by bringing together various different streams of data — both structured and unstructured — to determine which information is relevant for trade reconstruction needs. Such capability is now essential for financial firms due to the emergence of regulations such as the U.S. Dodd-Frank Act, E.U. Markets in Financial Instruments Directive (MiFID II), and E.U. Market Abuse Regulation (MAR).
Beyond compliance, the practice is now also common among risk management and even operations departments for strategic analysis purposes. As the evolution of automated big data continues, the company expects trade reconstruction to be employed more often. “The solution automatically pulls that information back in a fraction of the time, so firms can improve their responsiveness to regulators and internal stakeholders,” said the company in a statement.
The module also allows users to search for all elements of the reconstruction easily, which promotes efficiency since analysts don’t have to access multiple systems to find the relevant data.
“Financial services organizations need a better way to reconstruct the lifecycle of the trade,” said Joe Friscia, president of NICE Actimize.