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

PYMNTS

“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. De Vere said part of the proceeds will fund a technology push to further develop software and make it more broadly available. 15M In New Funding .

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Why AI Risk Tech And Banks Could Be A Match Made In Heaven

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But it occurred to them that their solution was useful outside of HR — and that many of the things that made someone a good hire of over time could also make them a good credit risk over time, if the artificial intelligence (AI) model they were using to screen with were modified to that task.

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Letting Go Of ‘Set It And Forget It’ Decisioning

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If a business doesn’t have the robustness in terms of data sets, the data scientists needed to analyze that data or even the technology infrastructure to run analytics properly and perform real-time decisioning in house, then they may be better off leveraging the scale of a platform that does. The Rise Of Analytics As A Service .

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Transcript: Kristen Bitterly Michell

Barry Ritholtz

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. I — I loved math, but really, I was going to go down that literature route more than anything else and — and study Spanish literature.

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Transcript: Rick Rieder

Barry Ritholtz

But there are so many tools at your disposal, and let alone how much duration you’re taking, how much interest, how much credit risk you’re taking, illiquidity, et cetera. And how do you make the decision, I’m not comfortable with this credit risk relative to the return it’s going to throw off?

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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.

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Transcript: Jeffrey Sherman, DoubleLine

Barry Ritholtz

Jeffrey Sherman : Well, what it was was, so I, as I said, with applications, there’s many applications of math, and the usually obvious one is physics. Barry Ritholtz : It seems that some people are math people and some people are not. The, the math came easier. And I really hated physics, really. It’s so true.