Northern Trust Launches Machine Learning Module for Asset Owner Research Management
Northern Trust has introduced a new feature in its Front Office Solutions platform that enhances efficiency in alternative asset servicing. This machine learning tool automates the tagging of research management documents, allowing asset allocators to manage thousands of files more effectively. The initiative aims to streamline portfolio management and reduce human error while improving operational alpha. With assets under custody reaching US$15.8 trillion, this innovation aligns with their commitment to leveraging advanced technologies in the asset management sector.
- Launch of a machine learning tool in Front Office Solutions for automated document management.
- Enhances efficiency in handling large volumes of investment research documents.
- Aims to reduce human error and improve operational alpha for asset owners.
- None.
Front
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 behavior 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.
“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,” said
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,” said Pickett.
Front
For more information, visit northerntrust.com/solutions.
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