A Millennial Population and Behavior Shift Underscores the Need to Re-Assess Decisioning Models
In 2019, according to the Pew Research Center, Millennials will assume the mantle of our largest generation by population volume. This population segment is expected to swell to 73 million while Boomers are expected to decline to 72 million.
Stating the obvious, this means Millennials will be the growth engine for lenders of all shapes and sizes. Unfortunately, it is likely that many lenders will be underwriting them by squeezing the proverbial round peg into a square hole.
Millennials are not behaving like any generation we’ve ever seen. One key finding is that a legacy assumption – that thicker credit files are less risky – may not hold with Millennials.
I’m speaking of the over-reliance on whether a consumer has a deep history of using credit and a wide mix of credit accounts, often referred to as “depth and breadth” of credit. By definition this arbitrarily penalizes those who are relatively new to credit or only use credit sparingly.
And to be sure, Millennials are put at a disadvantage.
It’s true that, all things being equal, someone with a long history of paying bills on time on a wide mix of different credit accounts may be less likely to default on a loan than someone with limited credit experience.
But that conventional approach does not work when the population seeking credit is anything but conventional.
Consider this: according to a recent analysis from VantageScore®, unlike any other generation before, a sizable portion of the Millennial population seems to willfully abstain or deemphasize credit despite having comparable income and assets to their to their thick file counterparts.
VantageScore’s analysis demonstrates that the dominant presence in most Millennials’ credit history is their student loan account. While this isn’t surprising, thinner file Millennials (who again have displayed similar income levels) choose to limit the number of credit accounts they open – presumably because they want to pay down their student loan debt.
This is a very healthy credit decision on their part, yet conventional models based on behavioral patterns from another credit environment penalizes them simply because they haven’t opened up new loan accounts.
One solution is to proactively re-assess current models and lending strategies. Lenders can benchmark current models against a menu of other models, which are based on more recent observation periods, use more sophisticated architecture and leverage new types of data.
Trended credit data, for example, can help better gauge the creditworthiness of Millennials in particular. With this relatively new data, credit behavior trends observed over a recent period of time are taken into greater consideration while the age of a person’s credit accounts and a “point-of-time” reading methodology employed by most scoring models becomes less important.
Comparing an earlier modeling approach to the new thinking within the credit score bands that most approval/decline decisions are made shows that tenure, length and depth of credit are deemphasized while more recent credit behaviors – where the richness of trended information allows for greater predictive insights – receive more weight. Fundamentally, those with less credit history can be better assessed.
Source: VantageScore Solutions, LLC.
Other alternative data such as rent, utility and telecom payments where the volume of reporting is improving may provide solutions to better assessing Millennial credit risk as well. A lender’s own proprietary information can also help bridge the gap.
This is just one example of how Millennials and their powerful market potential could reshape the consumer credit landscape. In order to keep pace and grow their businesses, lenders must evaluate their strategies and the models these strategies are based on to ensure that they keep up with the Millennial generation’s behavioral patterns.