Applied machine learning for spreading financial statements
"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 ca...
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| Formato: | Vídeo online |
| Idioma: | Inglés |
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[Austin, Texas] :
Data Science Salon
2020.
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822821406719 |
| Sumario: | "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. |
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| Notas: | Title from resource description page (Safari, viewed November 3, 2020). Place of publication from title screen. |
| Descripción Física: | 1 online resource (1 streaming video file (22 min., 18 sec.)) : digital, sound, color |
