Northern Trust launches machine learning module for asset owner research management – Institutional Asset Manager

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As part of its initiative to bring greater efficiency and transparency to alternatives asset servicing, Northern Trust (Nasdaq: NTRS) has launched a feature within its Front Office Solutions platform that utilises machine learning to empower sophisticated asset allocators with better oversight of their research process.
Each month, asset allocators for endowments, foundations, family offices and other institutions receive thousands of files including newsletters, fund statements, quarterly updates, and legal documents that are manually filed for investment manager research purposes. The new feature leverages knowledge of a user’s past behaviour to tag research management documents with intelligently suggested attributes before uploading them to the Front Office Solutions platform.
Emerging technologies including artificial intelligence, blockchain and cloud technology have allowed a higher level of automation in alternative investments such as private equity, real estate and infrastructure, which have traditionally required complex, manual processing. Northern Trust previously announced document procurement and document digitisation solutions to provide more efficiency and improved oversight for asset owners investing in alternatives.
“Front Office Solutions embraces innovative technologies like machine learning to make investment teams more productive by providing faster access to the information they need to make portfolio decisions,” says Melanie Pickett, head of Northern Trust Front Office Solutions. “The new tagging feature is an important step toward our ultimate goal of a fully automated document management workflow for alternative assets, which will improve efficiency and transparency.”
The new tool is completely tailored by the asset allocator, since no two allocators have the same needs. Categories are generated based on historical activity and the machine learning model continues to refine suggestions as users interact with the feature. Learning occurs in real time, rather than less-frequent learned improvements offered by many tools currently available.
“Machine learning promotes significant efficiencies to the administrative work that goes into portfolio management, reducing staff time and improving research results. With the administrative burden lightened and the possibility of human error limited, complex asset owners can focus on making better allocation and trading decisions, increasing the likelihood of improved operational alpha,” says Pickett.

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