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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Austin, Texas] : Data Science Salon, 2020.
Temas:
Machine learning.
Artificial intelligence > Data processing.
Neural networks (Computer science)
Financial statements.
Bank loans > Mathematical models.
Neural Networks, Computer
Apprentissage automatique.
Intelligence artificielle > Informatique.
Réseaux neuronaux (Informatique)
Prêts bancaires > Modèles mathématiques.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
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
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.))

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