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The Race is On: How lenders can seize the next trillion-dollar opportunity

  • There's a solid business case for sub-prime lending.
  • New advances in machine learning are enabling community banks and credit unions to work with thin-file customers without taking on more risk.
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The Race is On: How lenders can seize the next trillion-dollar opportunity

by Sean Kamkar, Vice President of Data Science, Zest AI 

The events of 2020 triggered major changes to the credit-risk environment and revealed new trends, challenges, and opportunities that will shape the industry for years to come. As lenders consider new post-COVID recovery strategies, what will they need to succeed, remain competitive, and drive growth? In this article, we’ll cover how banks and credit unions can position themselves to grow portfolios safely and seize new opportunities in the post-COVID era. Let’s dig in.    

Credit is Evolving

COVID-19 has changed the way lenders and consumers view traditional credit scores and is ushering in an era of new approaches. Lenders recognize they can’t rely on traditional credit scores to assess creditworthiness and consumers are increasingly more vocal about saying they don’t want to be defined by them. In fact, 62% of Americans wish there was another way to prove themselves to lenders outside the standard credit score and seven out of ten Americans would switch to a financial institution that has more inclusive lending practices, according to a recent Harris Poll Consumer credit survey. To adapt, we saw Bank of America, JP Morgan Chase, Wells Fargo, and Citi Group seek to augment credit reports and scores with real-time income or cash-flow data. This evolution will continue and is setting the stage for the next trillion dollar opportunity in financial services.     

The Trillion Dollar Opportunity: Serving the Underbanked

While the pandemic exposed a pressing need to find new approaches to assess credit risk, it also revealed a new opportunity: serving the underbanked. Research by the Consumer Financial Protection Bureau shows that 45 Million consumers are credit invisible. This group is disproportionately made up of young people, immigrants, minorities, and lower-income individuals. Compared with white credit applicants, people of color are almost twice as likely to be unscorable by traditional methods. So, what is the value of closing these gaps? 

According to Accenture, it is estimated that banks could generate up to $380 billion in annual revenue by closing the small business credit gap and bringing unbanked and underbanked adults into the formal financial system. And wider access to credit could boost global GDP by $3.7 trillion, and engender $4.2 trillion in new deposits and $2.1 trillion in additional loans, according to a report from McKinsey. Citibanks’s recent report echoed the business imperative and revealed that closing the racial inequality gaps could add $5 trillion of GDP to the U.S economy. The business case for sub-prime borrowers and increasing financial inclusion is solid — it’s now all about the execution.

Key Challenge: Loan Growth is Hard to Find 

In business, timing is everything and serving the underbanked is perfectly suited to help banks and credit unions solve the biggest post-COVID challenge — loan growth.  According to data collected by Bloomberg, the five largest U.S. regional banks all reported a drop in total loans for the first quarter compared with a year earlier. The situation is the same for regional banks and credit unions. CUNA has estimated that credit unions held $1.66 trillion in savings in February, 19.6% more than they held a year earlier. The gain of the previous 12 months ending February 2020 was 9.1%. Surplus funds (cash plus investments) rose 48% to $653 billion as of Feb. 28, an increase of $210.5 billion over the previous 12 months. 

At a recent credit union executive roundtable, Mike Dill, EVP and Chief Lending Officer at Royal Credit Union, said, “This is a big issue for us and we’re kicking around a lot of different technologies and strategies.” 

The central challenge is this: Lenders want to reach new customers but are wary of lending to borrowers they view as risky, specifically sub-prime and people with little credit history.  

This doesn’t have to be. A lack of credit history doesn’t make someone riskier than someone with a robust file. It just makes them harder to score using the traditional credit scoring system, which has been limited to a couple of dozen factors such as credit score, income and current debt outstanding. Limiting the factors ignores a good deal of information that can greatly impact a lender’s decision to approve a loan — and unfairly penalizes millions of Americans. Lenders don’t need to abandon this opportunity, they just need to leverage the power of ML for a more holistic picture of borrowers. Yes, technology has caught up to meet the moment. 

Machine Learning Breaks the Cycle

Lenders are switching to machine learning because it’s better at assessing risk than status quo models, credit scores and scorecards. Why? ML models can ingest 10 to 100 times more data than the logistic regression models traditionally used in lending. With AI, underwriters can use trended data and credit-adjacent data from checking accounts, rental history, and utility bills to supplement borrower profiles. 

Lenders Need a Holistic View of Borrowers

More data helps build a more holistic picture of a borrower by having millions of correlations to explore among the variables. What ML models are great at is replacing risky borrowers who may have looked good on paper with more good borrowers overlooked by traditional underwriting techniques.

The increased predictive power yields real economic gains. Lenders we’ve worked with that switched to ML underwriting typically generate 15% to 20% higher approvals and with that comes a jump in inclusion: More thin-file, underbanked, and protected-status applicants get approved — all with little to no impact on total portfolio risk.  

Jenny Vipperman, Chief Lending Officer at Vystar Credit Union, a Zest AI customer, commented on what the 22% increased approvals delivered for members: “That is thousands of people who otherwise would not have had access to a credit card.” 

With ML models, the path to serving the underbanked is realistic and no longer a catch-22.

Conclusion

The silver lining of the pandemic is that leaders and organizations are free to innovate and use new technology and processes to build a better and more vibrant financial system. For banks and credit unions, there is an urgent business need to find loan growth, and an opportunity to help traditionally overlooked borrowers get access to credit. ML unlocks this opportunity and provides the ability to use more data and better math in a meaningful way to safely widen the credit box for more Americans. Now is the time to think big and act.  As former Citigroup executive Ray McGuire said, “Corporate America needs to have courage in the months and years ahead.” The health of our society and economy depends on what you do next.

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