09.17.2021

Northern Trust Automates Data Extraction for Alternatives

09.17.2021
Northern Trust Automates Data Extraction for Alternatives

As part of its initiative to digitize alternative asset servicing, Northern Trust has launched an artificial intelligence-powered solution to extract unstructured investment data from alternative asset documents and make it accessible and actionable for asset owner clients.

Built in collaboration with Microsoft Azure Applied AI Services, which accelerate time to value for enterprises building AI solutions, and business and consulting firm Neudesic, the proprietary solution transforms crucial information such as capital call notices, cash and stock distribution notices, and capital account statements from a variety of unstructured formats into digital, actionable insights for investment teams.

“The integration of unstructured data on alternative assets will fill a crucial missing piece for asset owners who have long sought an accurate view of performance, liquidity and risk analytics across multi-asset portfolios,” said Pete Cherecwich, president of Corporate and Institutional Services at Northern Trust. “Automating investment data is a focus for Northern Trust, particularly for alternative assets due to their complexity and growing investor demand. Our investments in cloud technology, artificial intelligence, blockchain and machine learning will help asset owners achieve a true understanding of their assets and portfolios as a whole.”

Data extraction and integration is the second stage of a digitization process that will help asset owners access more current, robust information regarding their alternative assets – highly manual investments which traditionally only report on a monthly or quarterly basis. Northern Trust previously announced a custom-built robotic process automation solution to capture, categorize and store alternative asset documents on a cloud-based drive.

The new AI-powered data extraction capabilities will read stored documents and fund manager reports on holdings and performance of hedge funds, private equity and other alternative assets and pull out data points including asset names, currencies and market value. The two functions – document capture and data extraction – create an end-to-end, scalable, cloud-based process capable of moving from document receipt notification to digitized, accounting-ready data in just minutes.

“AI and natural language processing offers a profound impact on business processes across industries, and this use case is a stellar example of their potential for alternative asset administration,” said Bharat Sandhu, senior director, Azure AI and Machine Learning  at Microsoft . “We’re proud to collaborate with Northern Trust to make these emerging technologies more accessible and put their benefits into the hands of institutional investors seeking greater efficiency and clearer insight into their investments.”

The digitization of alternative asset servicing is designed to provide asset owners with meaningful benefits, including:

  • A faster and better ramp-up experience when investing in new assets, empowering asset owners to be more agile about making strategy changes when needed
  • Faster delivery of accounting book of record services, including during the peak monthly and quarterly statement cycles that are typical for alternative investments
  • Enhanced accuracy rates by moving from manual to automated data entry and exception management
  • Increased data transparency

Northern Trust has more than US$1.6 trillion in alternative assets under custody and administration and processes more than 1.5 million alternative asset documents each year. Document capture and data extraction can be customized for other asset classes and trade lifecycle processes, such as derivatives and cash operations. The new solution marks another step in Northern Trust’s digitalization initiative to harness emerging technologies to automate the alternative asset servicing process, a growing sector of its asset servicing business.

Source: Northern Trust

 

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