Leaders In Lending

Meet Three Lending Executives Who Are Mainstreaming AI In Underwriting

Sean McCarron

September 23, 2020

You take the measure of a strong credit culture through its people—from the bottom up and, especially, from the top down. I recently got the corner-office take on what it means to build a strong and innovative credit culture when I had the good fortune to host the first episode of Zest AI’s “Leaders In Lending” video series. The series convenes the best minds in the consumer credit industry to share how they got where they are today and how they’re approaching a landscape fundamentally altered by the pandemic and its aftermath.

We scored a trifecta in the first show. Our three guests have decades of experience among them: Denise Brown, former chief credit and risk officer at Harley Davidson Financial Services, Mihaela Kobjerowski, chief consumer credit officer at First National Bank of Omaha, and Jenny Vipperman, the chief lending officer at VyStar Credit Union.

Saying “Yes” to More Deserving Borrowers

Their institutions serve different customers and geographies, but one thing they have in common: They’re looking for more ways to say “yes” to deserving borrowers while improving the efficiency of their organizations. VyStar’s Vipperman sees the next chapter of underwriting moving away from the overarching credit culture the industry settled into back in the 1980s, which revolves around pulling credit scores and deciding yes or no based on that limited information. “It sounds great in theory, but we went from being the industry that helped the little guy to being, unfortunately, the industry that was very, very conservative in our decisioning. How do you find a way to say yes, and what things can you do to mitigate the risks that you have?”

Machine Learning Adoption is a Continuum

One of the tools they’re adopting to deliver higher productivity and safe growth is machine learning-powered underwriting. “I think that the world is going to AI and machine learning,” says Vipperman. VyStar is one of the first credit unions in the U.S. to adopt ML in underwriting. “It’s going to be everywhere, not just in our financial institutions. It’s going to be in every industry everywhere.”

FNBO’s Kobjerowski sees the adoption of machine learning as part of the natural evolution of lending, echoing the transition from human judgment to standardized credit scores in the 1980s. “It’s happening now with the transition from traditional credit scoring to machine learning models. And you have a new generation of tools and a new generation of thinkers that sometimes clash with very talented regression-based statisticians that have spent their life building traditional scorecards. So it’s a continuum.”

Denise Brown, who’s been coaching her teams to higher levels of process automation and adoption of advanced analytics, sees many use-case targets for ML, not just in underwriting. Holistic thinking is required. “I think creating a company strategy around that is important,” she says, adding that it’s also important to create a culture of experimentation and smart risk-taking. “You have to give people permission to fail. I remember saying to a team, ‘All right, what’s our failure goal for the year? And they kind of just looked at me and I said, ‘Well, is it 5%? Is it 10%? What do you guys think?’ And I said, ‘If you have no failures, maybe you’re not acting fast enough, right?’

All three lenders say that ML adoption is going to happen on a continuum, taking into account the varying comfort levels teams have with it. Brown ensured that her organization ironed out those cultural conflicts from the start by sharing the outcomes of what the data scientists and risk managers were doing and running it by underwriting. “We achieved alignment between modeling and the underwriting floor because we all agreed to it. Creating that consensus helps a lot. You’ll see some younger risk managers jump on the bandwagon and embrace [ML], and sometimes some of the more established folks who’ve been working for a while say, “Well, I don’t trust it yet.”

Like The Jump To Cellular, Machine Learning Offers A Level Playing Field

According to this group, change is not just on the horizon, but on our doorsteps. One of the key ways AI/ML will reshape the industry is by enabling a level playing field between smaller institutions and the ones with billion-dollar IT budgets. Says Brown, “It’s a little bit like many years ago, some countries that didn’t have landlines jumped right to cellular. Those companies that don’t have much prior investment in analytics, that don’t have custom scores and maybe just use FICO, they have the opportunity of a level playing field… That’s what AI/ML is bringing to the industry as well.”

And do the humans in lending need to fear the robot? Not if the models are produced with the utmost rigor, transparency, and fairness. “The role of the underwriter doesn’t go away,” says Vipperman. “It just becomes something new, like it became something new 12 years ago, just like it became something new before that.”

Says FNBO’s Kobjerowski, “I’m convinced that AI and machine learning can provide very impactful business results, and what I’m hoping happens with this new technology and new types of analytics is we consider the customer experience and the customer in this journey much more so than we did with previous types of analytics. I think we need to be very mindful of the experience we want to create and the human on the other side as we continue on this path.”

Watch the complete video here.

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