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Developing AI ‘Behind the Curtain’ for Financial Services - with Nate Bell of Wells Fargo

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Manage episode 442940158 series 79750
Indhold leveret af Daniel Faggella. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Daniel Faggella eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Today’s guest is Nate Bell, Corporate Functions Business Data Leader at Wells Fargo. Nate joins us on today’s show to discuss the growing role of AI in the financial services sector, focusing on reconciling infrastructure investment with innovative use cases. With a unique vantage point from the data side, Nathaniel explores three main challenges in data management: the cost of good data management, navigating new AI-related risks, and the complexities of anthropomorphizing AI. These issues, he explains, can lead to misconceptions both within management and in customer-facing environments, where AI’s “people-pleasing” capabilities are often misunderstood. To discover more AI use cases, best practice guides, white papers, frameworks, and more, join Emerj Plus at emerj.com/p1.

  continue reading

870 episoder

Artwork
iconDel
 
Manage episode 442940158 series 79750
Indhold leveret af Daniel Faggella. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Daniel Faggella eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Today’s guest is Nate Bell, Corporate Functions Business Data Leader at Wells Fargo. Nate joins us on today’s show to discuss the growing role of AI in the financial services sector, focusing on reconciling infrastructure investment with innovative use cases. With a unique vantage point from the data side, Nathaniel explores three main challenges in data management: the cost of good data management, navigating new AI-related risks, and the complexities of anthropomorphizing AI. These issues, he explains, can lead to misconceptions both within management and in customer-facing environments, where AI’s “people-pleasing” capabilities are often misunderstood. To discover more AI use cases, best practice guides, white papers, frameworks, and more, join Emerj Plus at emerj.com/p1.

  continue reading

870 episoder

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