Recently, Nasdaq Inc. announced the launch of a new service that looks to put a subset of artificial intelligence technologies toward growing investor profit.
Called the Nasdaq Analytics Hub, the new service will leverage machine learning to sift through social media, central bank, retail sentiment and end-of-day trading data, deriving insight that fund managers and traders can then leverage to enhance investment strategies.
“We back test the data using a number of strategies,” Mike O’Rourke, global head of Machine Intelligence and Data Services at Nasdaq, told Reuters in an interview. “Then we use machine intelligence to add value-added analytics to the data that allows firms to make it more actionable.”
The newswire noted that financial technology startup Lucena Research scrutinizes the data, sourced from Nasdaq as well as a number of third-party providers. Nasdaq plans to continuously grow its number of data sets and sources as well as the variety insights and analytics the service is able to provide its users.
In a nutshell, machine learning is a form of AI that allows programs to change, adapt and, obviously, learn from new data without requiring additional programming effort and human intervention. These intuitive computing capabilities look to innovate and automate repetitive, highly difficult pattern-matching problems and analysis of massive volumes of data.
In a recent podcast, Sunil Madhu, CEO and president of digital identity verification company Socure told PYMNTS’ Karen Webster that machine learning automates rote, repetitive and manual data-driven processes. Along the way, the machine can extract information from the data and identify patterns on a real-time basis. Patterns that human counterparts may not able to intuit.
While Madhu spoke more specifically on the applications of machine learning in the realm of IDV and fraud prevention, the news from Nasdaq well illustrates another of the growing applications for this technology.