Northern Trust Launches Alternative Asset Servicing Digitization Initiative
Intelligent document capture is key step in digital transformation built on machine learning, artificial intelligence and cloud computing solutions
CHICAGO–(BUSINESS WIRE)–$ntrs #alternativeassets–Northern Trust has launched a machine learning-powered document capture capability as the foundation of a multi-year investment to digitize alternative asset servicing and enhance the experience for asset owner clients that invest in complex private market and unlisted assets.
Digital document capture enables Northern Trust to streamline historically manual workflows by automating the receipt and processing of alternative asset documents and fund manager reports on holdings and performance of hedge funds, private equity and other alternative assets. Northern Trust’s proprietary solution combines robotic process automation and cloud-based technology to provide transparency and data standardization that enables greater understanding of portfolio risk and performance.
“As alternative asset classes continue to grow in importance to institutional investors, Northern Trust is committed to driving efficiency and reducing operational risk through the use of emerging technologies,” said Pete Cherecwich, President of Corporate & Institutional Services at Northern Trust. “Digital document capture is a huge step forward, and only the start of our larger plan to enhance alternative asset servicing for the benefit of our clients.”
Northern Trust has more than US$1.6 trillion in alternative assets under custody and administration (as of December 31, 2020) and processes more than 1.5 million alternative asset documents each year. The intelligent document capture solution deploys custom-built robotic process automation that enables self-service operations to collect documents from emails, whether in the form of download link or within the email text. Documents are stored on a cloud-based drive where intelligent tools extract identifying details such as the type of document (e.g., statements, capital call notices, and distribution notices) and the name of the fund company, tasks previously performed manually.
Automated document capture enables Northern Trust’s alternative asset servicing teams to focus on more strategic aspects of the process and reduces the need for manual intervention when coordinating saving, storage and categorization. Since alternative assets are often valued on only a monthly or quarterly basis, asset owners can also benefit from faster servicing of their assets and deeper data insight provided through artificial intelligence.
The proprietary document capture tool is the first in a series of releases supporting Northern Trust’s strategy to harness emerging technologies to digitize alternative asset servicing, a growing sector of its asset servicing business.
About Northern Trust
Northern Trust Corporation (Nasdaq: NTRS) is a leading provider of wealth management, asset servicing, asset management and banking to corporations, institutions, affluent families and individuals. Founded in Chicago in 1889, Northern Trust has a global presence with offices in 22 U.S. states and Washington, D.C., and across 23 locations in Canada, Europe, the Middle East and the Asia-Pacific region. As of March 31, 2021, Northern Trust had assets under custody/administration of US$14.8 trillion, and assets under management of US$1.4 trillion. For more than 130 years, Northern Trust has earned distinction as an industry leader for exceptional service, financial expertise, integrity and innovation. Please visit our website or follow us on Twitter.
Northern Trust Corporation, Head Office: 50 South La Salle Street, Chicago, Illinois 60603 U.S.A., incorporated with limited liability in the U.S. Please read our global and regulatory information.
Contacts
Europe, Middle East, Africa & Asia-Pacific:
Camilla Greene
+44 (0) 20 7982 2176
Camilla_Greene@ntrs.com
Marcel Klebba
+ 44 (0) 20 7982 1994
Marcel_Klebba@ntrs.com
US & Canada:
John O’Connell
+1 312 444 2388
John_O’Connell@ntrs.com