Northern Trust deploys machine learning for securities lending

Northern Trust will leverage machine learning to project demand for equities in the securities lending market and grow revenues.

Northern Trust has developed a new pricing engine by utilising machine learning and advanced statistical technology in a bid to drive securities lending revenues.

By using a hybrid cloud platform for highly efficient processing of data, Northern Trust will leverage a new algorithm that identifies strategic market data points from multiple asset classes and regions to project the demand for equities in the securities lending market.

Northern Trust’s securities lending traders will then be able use these projections, alongside their own market intelligence, to automatically broadcast lending rates for 34 global markets to its network of borrowers, and 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, 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.”

Northern Trust saw securities lending revenues decline slightly by 2% in the second quarter, year-on-year, to $21.8 million. Meanwhile, as of the end of June, there was around $1.2 trillion in lendable assets for more than 450 clients globally. According to Dane Fannin, head of global securities lending for Northern Trust, it has created an infrastructure and analytical framework that can intelligently adapt to changing market conditions.

“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,” said Fannin.

The Chicago-based global custodian is well underway with an ambitious three-year technology upgrade programme, and had budgeted $2.5 billion on technology between 2017 and 2019, up from $2.2 billion for the 2014-2016 period.