Remove Credit Risk Remove Economics Remove Math Remove Numbers
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Zest AI CEO: Artificial Intelligence Is Reshaping Lending and Credit

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In this case, the “cake” is the improved performance and economics that many banks experience when they update systems that in some cases have been in place since the 1950s. “On On average, our customers will see a 20 percent lift in approvals and a 30 percent reduction in charge-offs just by deploying better math,” de Vere said.

Math 138
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Transcript: Greg Davis, CIO Vanguard

Barry Ritholtz

And it worked out and had multiple job offers coming out of school from a number of different insurance companies. I had a number of relationships that I built up and had another job lined up in New York City. So a variety of risk meetings, a variety of economic meetings. I thought that was a really interesting story.

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Transcript: Kristen Bitterly Michell

Barry Ritholtz

And so, coming out of school, I studied Economics and Spanish Literature, and I applied to a — a program that actually targeted Liberal Arts majors. And so, with this gave me exposure to everything from investment banking to retail, looking at like checking account campaigns, like how do you get more assets in the door to credit risk.

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Transcript: Rick Rieder

Barry Ritholtz

And like I say, that’s part of why it’s translated to a number of people coming to BlackRock and be with me today. RIEDER: So I had known Larry Fink and Rob Caputo, our CEO and president, for a number of years. And we have a great team in Asia and Europe. So yeah, man, that was the idea. You said BlackRock absorbed R3.

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Transcript: Ted Seides

Barry Ritholtz

Or at least the top, pick a number, 30, 40%. I don’t remember the number. The challenge is unlike the S&P 500, hedge funds sit in a box that has underlying credit risk from prime brokers. So the credit markets froze. So you’re talking about an average of a large number. Less, 20, 30%?

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Transcript: Sean Dobson, Amherst Holdings

Barry Ritholtz

And up until that moment in time, we didn’t spend a lot of time on credit risk in mortgages. We didn’t really have to model credit risk because that was, that risk was taken by the agencies. But in these private labels, you had the, the market was taking the credit risk.