By: Eric Siegel, Program Co-Chair, Predictive Analytics World for Financial
In anticipation of her upcoming conference presentation at Predictive Analytics World for Financial Las Vegas, May 31-June 4, 2020, we asked Leslie Barrett, Senior Software Engineer at
Bloomberg LP, a few questions about their deployment of predictive analytics. Catch a glimpse of her presentation, Supervised Learning for Information Extraction from Financial Filings, and see what’s in store at the PAW Financial conference in Las Vegas.
Q: In your work with predictive analytics, what behavior or outcome do your models predict?
A: The models that are the topic of this talk will identify sections of documents associated with particualar sub-events in a financial transaction.
Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?
A: Bloomberg delivers predictive analytics in a variety of products to help Legal Professionals and Financial Professionals access the information that drives decision making in their research and analysis.
Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?
A: The project described in this talk will streamline a manual data gathering process to improve the quality and timeliness of data aggregation.
Q: What surprising discovery or insight have you unearthed in your data?
A: Identifying certain variables in specific sections of text – despite the underlying concepts seeming clear – is surprisingly difficult for both humans and machines.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: If your training data is really good, you don’t need as much of it.
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Don’t miss Leslie’ presentation, Supervised Learning for Information Extraction from Financial Filings, at PAW Financial on Tuesday, June 2, 2020 from 3:05 to 3:25 PM. Click here to register for attendance.
By: Eric Siegel, Program Co-Chair, Predictive Analytics World for Financial
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