Entire lending strategies and credit scoring models have typically been built assuming that those with thin files (consumers with less than 3 credit accounts) are higher risk than those with thicker files.
This stood to reason because consumers with deeper experience with credit, tended to exhibit other healthy credit behaviors and have more income at their disposal.
This is not the case with Millennials. Recent data shows they are writing their own story when it comes to using credit.
The first major indicator of a credit behavioral shift is that thin file Millennials actually, on average, have income levels similar to their thicker file counterparts, which runs contrary to every generation before:
If you peel back the onion, you will see that the dominant presence in a Millennial’s credit history is their student loan account. While this isn’t surprising, thinner file Millennials (who again have displayed relatively high-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 older models and lending strategies penalize them simply because they haven’t opened up new loan accounts.
That’s where the power of trended credit data comes into play. More recent credit behaviors over a time series are taken into greater consideration with trended credit data. And the age of a person’s credit accounts is less important than a point-of-time reading methodology employed by most scoring models.
A comparison between VantageScore 4.0, which includes trended data attributions, and VantageScore 3.0, which does not use these attributes, shows how newer factors become more influential in the calculation of a person’s credit score.
Trended data attributes change the focus of credit scoring models to better understand actual credit management behaviors over time versus static snapshots, where higher value is placed on tenure and types of credit used.
Obviously, in the sub-prime population segment, payment history and high utilizations continue to be the dominating characteristics to determine poor creditworthiness.
However, once these issues are cleared (i.e., no payment history issues on the consumer), a credit scoring model using trended credit data considers a consumer’s recent credit management behaviors as major factors in determining credit risk.
Conventional models, or static models, never had the ability to see past the present state of accounts and can only determine the most recent signs of performance on a consumer.
The newest users of credit, Millennials, choose to use credit more prudently. Are they being treated fairly by older static models and by lenders who often only give them credit cards with low limits and high interest rates?
Or, should more emphasis be placed on credit behaviors and recent changes in credit management to determine the risk of these consumers?
To be sure, lenders have a unique opportunity to better understand these consumers and be their champion. By showing them you’re “not their Mom and Dad’s lender,” financial institutions can achieve higher levels of brand loyalty and product satisfaction.