Upstart Innovation, Technology & Agility

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

At Upstart, ML and AI models are the heart of our product. In fact, you could argue that these models are our product. For example, our underwriting models drive the loan offers we provide on our platform and are designed to evaluate the credit risk of any given applicant. That is, they answer the question, “What is the likelihood this applicant pays back the loan?” For our product to succeed, we must do this with a high degree of accuracy. To achieve this, our underwriting models utilize state-of-the-art ML techniques trained using a vast amount of payment data and alternative credit variables that many lenders overlook. Beyond underwriting, we use ML models to optimize marketing spend and detect financial fraud. 

A major aspect of our product development is working on improving these models. This can take the form of integrating new data sources or developing new modeling techniques. We continuously update our models to keep up with economic trends and improve performance. The process involves ML researchers, ML engineers, risk reviewers and, of course, product managers. This results in higher approval rates and a seamless experience, with funds often reaching customers in a few days.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

First, we hire bright and highly motivated people. One of our core values is “Be smart, but know you might be wrong.” Our people are encouraged to try new things, such as new modeling techniques, implementing new training infrastructures and thinking outside of the box about how the product works. This encourages an environment of continual learning that allows us to keep our team’s knowledge at the forefront of AI and ML. 

Next, we’re provided with access to the best computational resources. Modern ML and AI can be constrained by the amount of compute required to implement it, both in training the models and serving them in production. This requires investing in compute resources to allow for fundamental research that strives to continuously improve our models. Finally, we have a dedicated set of engineers and risk professionals who support our AI and ML researchers in building robust frameworks for training, maintaining and deploying models in a cost-effective manner. 

Can you share some examples of how AI and ML has directly contributed to enhancing your product line or accelerating time to market?

Upstart takes a unique approach to underwriting where we rely primarily on sophisticated ML models to make decisions on how to price loans. Alternative approaches include using a human to manually review credit applications and apply predefined rules or applying a more traditional statistical model to assess credit risk. The first approach is clearly costly since it directly involves a trained person to review each loan application. It’s simply not scalable. The second approach, while closer to ours, doesn’t achieve the accuracy required to meaningfully expand access to credit to underserved consumers. By utilizing better, more accurate models, we can approve more consumers at lower rates all while maintaining loan performance.