Northern Trust (NTRS) announced today it has developed an innovative pricing engine that utilizes machine learning and advanced statistical techniques to drive revenue growth for clients by more effectively forecasting the rate to loan securities in the securities lending market.
Built on a hybrid-cloud platform that allows for highly efficient processing of data, the algorithm leverages numerous strategic market data points from multiple asset classes and regions to project the demand for equities in the securities lending market. Northern Trust global securities lending traders are able to leverage these projections, together with their own market intelligence, to automatically broadcast lending rates for 34 global markets to Northern Trust’s extensive network of borrowers, thereby enhancing revenue opportunities for lending clients.
“Northern Trust continues to invest in emerging technologies to bring enhanced value to our clients,” said Pete Cherecwich, president of Corporate and Institutional Services at Northern Trust. “The use of machine learning in our global securities lending business enables greater pricing efficiency that helps clients improve revenue across portfolios. This enhances Northern Trust’s broad suite of securities financing capabilities, providing borrowers with highly automated, low transaction cost trade execution solutions in this cost-conscious market.”
From trading desks in Chicago, London, Hong Kong, Sydney and Toronto, Northern Trust’s securities lending team has been generating incremental revenue for clients using innovative solutions for over three decades. As of June 30, 2019, there was approximately US$1.2 trillion in lendable assets for more than 450 clients worldwide.
“With this latest advancement, we have created an infrastructure and analytical framework that can intelligently adapt to changing market conditions” said Dane Fannin, head of global securities lending at Northern Trust. “Our technology assesses market demand across thousands of securities and allows our traders to extract better returns for our clients. The potential benefits from machine learning techniques extend beyond this initial application, and we will continue exploring and developing solutions that drive value for our clients.”