Applied machine learning for spreading financial statements /

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Bibliographic Details
Imprint:[Austin, Texas] : Data Science Salon, 2020.
Description:1 online resource (1 streaming video file (22 min., 18 sec.))
Language:English
Subject:
Format: E-Resource Video Streaming Video
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13685065
Hidden Bibliographic Details
Other authors / contributors:Hadi, Moody, on-screen presenter.
Data Science Salon, publisher.
Notes:Title from resource description page (Safari, viewed November 3, 2020).
Place of publication from title screen.
Presenter, Moody Hadi.
Summary:"Presented by Moody Hadi, Group Manager, Financial Engineering at S & P Global Market Intelligence. Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the analysts to focus on their high value add operations. This involves combining Optical Character Recognition using pre-trained language neural networks, with context sensitive semantic matching. We will go over the developed ML pipleline and architecture."--Resource description page