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In this conversation, we chat with Jeff Keltner, the SVP Business Development at AI-powered digital lending fintech, Upstart. Jeff joined Upstart in 2012 after spending 6 years at Google. He launched and built the Google Apps for Education business, growing market share from zero to almost 70% in its first four years. Jeff spearheaded marketing efforts for Google Apps in Global 2000 accounts and led sales, business development, and go-to-market strategy for the launch of Chrome devices in the education and enterprise sectors.
Jeff spent several years in direct sales at IBM, always exceeding quota, and was a founding engineer and lead UI developer at SSB Technologies. Jeff holds a BS in Computer Systems Engineering from Stanford University.
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Timestamps:
1’25”: Early experiences and career start
4’33”: Tech-natives with finance skills versus finance-natives with tech skills
7’07”: How to deal in a melting pot of professionals with differing skillsets
9’11”: Intro to Upstart (a pure play digital lender) and the early days of its formation
14’49”: Breaking down an AI-driven growth model and its value to digital lending
17’15”: Estimating the value of human life and typical earning power to more accurately refine the prediction model
20’06”: What is a machine learning model and how is it used in the context of digital lending?
26’31”: The evolution of machine learning in financial services and how artificial intelligence is applied and evolving in FinTech
31’41”: How Upstart relates to the financial industry (banks) and what the specific benefits are
35’12”: How does Upstart figure out where to expand given its business model?
38’40”: Artificial Intelligent models: Open source public goods or intellectual property?
41’51”: Macroeconomic Environment – the impact of interest rates on digital lenders
44’59”: Channels to use to connect with Jeff and/or to learn more about Upstart
Sneak Peek:
Jeff Keltner:
…We are always testing out new versions of models, new iterations, of course, some of them increase accuracy, some of them don't. Some increase accuracy at high processing costs that's not worth it. So, we're constantly pushing the boundaries of that. But the things these models are really being asked to do, I think it's important to think about the data you're bringing in, what the model can do and what you're asking it to predict.We're bringing in, if you think about a credit file, most people look at a handful of variables coming off of a credit file. We get almost a thousand from two different bureaus. And then the question becomes, okay, I've got a thousand variables. We ask a bunch of questions on the form that people don't ask that are more available as well, over 1600 points of data on an individual application file. And then the question is, well how does one use…
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