Written by Jeff Keltner, SVP Business Development
In today's market, there are more credit-worthy consumers than approved borrowers. Consider the set of subprime and potentially risky portfolios that most banks and credit unions would deny without a second look.
Those portfolios labeled as subprime, or risky, generally have a default rate of 20% to 30%. As high as this sounds at first, it translates to a repayment rate of 70% to 80%. This means lenders are misjudging more often than they are correct when they call a consumer "risky."1
What’s more, research from Upstart and TransUnion found that although 80% of consumers have never defaulted on credit, fewer than half qualify if assessed using traditional metrics.1
These borrowers are what we call the hidden prime. They fail to qualify by traditional metrics, but are creditworthy in practice. Plus, they have the potential to be grateful customers and avid promoters, since most lenders will deny them credit.
We believe that combining three techniques can help lenders better identify and serve hidden prime consumers.
Traditionally, qualified borrowers do fine in a manually processed system, where humans check applications against predetermined metrics. But, to reach the hidden prime, lenders need a different and more sophisticated kind of analysis.
To do that, lenders have to automate. This creates a more systematized approach, and allows for the use of more sophisticated calculation techniques. Most importantly, it enables lenders to consider data points that traditional calculations would ignore.
Most lenders rely primarily rely on credit scores and related metrics for decisioning.
To identify the hidden prime, lenders need to consider the entire picture that a credit profile presents. That means looking at the applicant’s entire borrowing history and trade lines, instead of just aggregated credit scores.
Each credit bureau has numerous data points available for each borrower. By zeroing in on specific relevant activity, we believe lenders can learn much more about a borrower’s creditworthiness. This detailed information is also more relevant to the approval question at hand.
This is especially true when a lender is considering an applicant for a specific kind of credit. A person’s creditworthiness can be much different for a small personal or payday loan versus a larger auto loan or even a mortgage.
3. Augment with Alternative DataCredit profiles are data-rich, but they don’t provide all of the information that could help a lender make a decision. There’s even more data available in the borrower’s customer record. Transaction data and occupational information, for example, can provide a lot insight into a borrower’s financial habits.
To identify the hidden prime, lenders need to consider the entire picture that a credit profile presents. That means looking at the applicant’s entire borrowing history and trade lines, instead of just aggregated credit scores.
The Takeaway
Most consumer banking professionals get into the industry to help people. They want to match borrowers with credit options that can help those borrowers improve their situations.
With the techniques described here, lenders can identify a large percentage of the hidden prime — borrowers that are creditworthy but may not look that way at first glance. Considering the borrower’s entire credit picture can bring them out of obscurity and help them get the credit they need and deserve.
1According to an Upstart retrospective study completed in December 2019. https://www.upstart.com/about#credit-limits-2