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Leaders in Lending | Ep. 74

The Opportunity with AI Driven Lending

As AI and machine learning emerge in the consumer lending space, the technology has proven to provide dramatic wins for both lenders and consumers. On this episode of Leaders in Lending, we chat with our very own Paul Gu, Co-Founder and VP of Product at Upstart.

Paul Gu Headshot


Paul Gu

Paul Gu is the Co-Founder and Head of Product at Upstart. While pursuing a degree in Computer Science and Economics at Yale, he began to notice an entire community of people having a hard time getting access to credit, and thought he could do something about it - which lead to him co-founding Upstart over 9 years ago. He has been recognized as one of Peter Thiel’s 20 under 20 Fellows and Silicon Valley Business Journal’s 40 under 40.




Our mission is to enable effortless credit based on true risk. Upstart is a leading artificial intelligence (AI) lending platform designed to improve access to affordable credit while reducing the risk and costs of lending for our bank partners. By leveraging Upstart's AI platform, Upstart-powered banks can offer higher approval rates and experience lower loss rates, while simultaneously delivering the exceptional digital-first lending experience their customers demand.

Key Takeaways

  1. The role of AI and machine learning in lending

  2. Why unsecured loans are a good place to innovate

  3. The need to reduce friction and effort in the lending process

  4. How an organization made the shift from traditional lending to AI lending


As those of us in the lending industry know, unsecured loans inherently carry with them the highest level of risk. Because they’re not backed by any piece of collateral like an auto loan or a mortgage, they’re often an afterthought in the innovation process. 

In this episode, Upstart co-founder Paul Gu shares two reasons why unsecured loans were a great place to apply Artificial Intelligence:

1. To demonstrate efficacy

If you want to demonstrate the efficacy of machine learning when applied to something that’s been done for a very long time, like the lending industry, you want to apply it in the place where it’s going to matter the most - and be the hardest.

And that’s unsecured loans. 

Every other asset class like car loans, home loans, and even credit cards to some degree are backed by "something." For car and home loans, they’re of course backed by the physical assets of cars and homes. And the credit card is backed by the further utility of being able to use the card. 

But when you give someone $20,000 and say, “Please pay me back,” you’re not really backed by anything. And so you’ve got to be VERY good at deciding who does and does not get those loans.

2. It’s the Most Broad Loan

From the consumer perspective, it’s the most flexible kind of loan. Whereas a car loan or a home loan can only be used for certain things, the unsecured personal loan has no limits, and so it’s a natural starting place. 

Once you’ve nailed down secured loans, it’s a natural transition to other types of loans as well.

A Shift in Approach

So how did we Upstart shift from the traditional lending approach to a more AI and Machine Learning-driven approach? 

First, we use an immense quantity of data. Over 1,000 variables and millions of rows of repayment data are used to train our AI model to make the decisions needed, both through traditional and non-traditional data points. 

And secondly, we changed the way we learn from the data.

Traditionally, a person looks at the data and plots points on a line. That doesn’t work in the real world because we all know that the world isn’t a straight line. To actually make individualized predictions, we implemented state of the art Machine Learning algorithms, and combined the big data approach with the modern algorithm approach, resulting in what we call AI lending.

Reducing Friction

So why the focus on AI and Machine Learning? Wasn’t the old way of lending working just fine? Well, yes and no. 

At the end of the day, underwriting a loan is just figuring out everything needed to ascertain the risk of the loan. And there are multiple ways to do that. You can follow a person around all day and make sure that they have the ability to pay back the loan, but very few consumers are going to sign up for that. 

A highly developed AI and ML-driven lending process results in much lower friction for the end user, and in today’s world that cannot be overstated. The truth of the matter is that the more work you ask people to do, the less likely they are to complete your application process.

As soon as you ask for any kind of document upload, you’re going to lose a portion of people who don’t want to have to go through that process. 

So really, the AI and ML-driven process exists not only to make life easier for those of us in the lending industry, but to remove the friction for the end user, allowing more consumers to originate loans through our organizations. 

And isn’t making life easier for our consumers why we’re all in this business to begin with? 


Stay tuned for new episodes every week on the Leaders in Lending Podcast