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Investment management company Vanguard operates with some 20,000 employees, has more than 50 million investors, with more than $7 trillion dollars in assets under management.
Big money, big data, and a big responsibility to say the least. Financial institutions lean increasingly on data and technology to better navigate fluctuations of the market, which can see dramatic shifts as well as slow-burn trends. For instance, a few weeks ago LG AI Research and Qraft Technologies signed an agreement at the New York Stock Exchange to support efforts in AI applications and the creation of financial instruments. They are clearly not alone in the race to leverage AI and machine learning in conjunction with financial data.
With an operation of Vanguard’s size and scope, using data and analytics becomes a priority to help maximize investments. A combination of co-locating data and analytics teams to work with leaders while centralizing data is part of Vanguard’s approach to advising its clients on their investments and reducing risk.
Ryan Swann, Vanguard’s chief data analytics officer, shares some of the data strategies and structure employed by his team to identify ways to further assist customers who invest through Vanguard by identifying behaviors that might leave money on the table.
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About the Author(s)
Joao-Pierre S. Ruth covers tech policy, including ethics, privacy, legislation, and risk; fintech; code strategy; and cloud & edge computing for InformationWeek. He has been a journalist for more than 25 years, reporting on business and technology first in New Jersey, then covering the New York tech startup community, and later as a freelancer for such outlets as TheStreet, Investopedia, and Street Fight. Follow him on Twitter: @jpruth.
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