Interpretable and resilient AI for financial services

"Financial services are increasingly deploying AI models and services for a wide range of applications in the credit lifecycle, such as credit onboarding and identifying transaction fraud and identity fraud. These models must be interpretable, explainable, and resilient to adversarial attacks....

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Detalles Bibliográficos
Autor Corporativo: O'Reilly & Associates, publisher (publisher)
Otros Autores: Koister, Jari, on-screen presenter (onscreen presenter)
Formato: Vídeo online
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly Media 2019.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822791406719
Descripción
Sumario:"Financial services are increasingly deploying AI models and services for a wide range of applications in the credit lifecycle, such as credit onboarding and identifying transaction fraud and identity fraud. These models must be interpretable, explainable, and resilient to adversarial attacks. In some situations, regulatory requirements apply that prohibit black-box machine learning models. Jari Koister (FICO) shares forward-looking tools and infrastructure has developed to support these needs. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page.
Notas:Title from title screen (viewed January 10, 2020).
Descripción Física:1 online resource (1 streaming video file (47 min., 32 sec.)) : digital, sound, color