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Are lenders missing out on Millennials?

One of the fundamental truths about credit score models is that they must periodically be refreshed in order to maintain a peak level of predictiveness. Not only does the data available to modelers get better and model building techniques improve, but the mix of credit products changes, as do consumer behaviors and the way they treat their financial obligations.

For example, let’s take a look at the massive increase in student debt. Models built prior to the Recession didn’t factor in how consumers with large student debt obligations would influence consumer credit behavior.

For this reason, our data scientists took a deep dive into the credit usage patterns of different generations and they layered on extra factors indicative of financial health (e.g., income and assets). By doing this, we found that there may be a paradigm shift that methodologies underpinning older scoring models are not considering.

Included in this newsletter is a deeper dive, but I’ll topline some of the important findings:

  1. Historically speaking, the assumption was that those with higher income and assets were associated with thicker credit files and more credit usage (i.e., thick file consumers have three or more credit accounts reported and thin file consumers have two or fewer credit accounts). Relatedly, lenders often viewed thin file consumers as more risky than those with thick files and they are often placed into the highest risk products (think: high interest rates and modest loan limits).
  2. However, what we are now seeing is that Millennials with thin files – unlike any other generations before them – on average have income and asset levels consistent with their thick file counterparts.
  3. Notwithstanding this, conventional models and lending strategies might actually be penalizing them simply because that’s historically how thin file consumers have been treated.
  4. Accordingly, users of credit scores for lending decisions should carefully assess whether they should reconsider models based on legacy beliefs.

This is an opportunity for lenders to lean into the Millennial generation and get a firmer understanding of how they are handling their credit health. From a credit scoring standpoint, it also speaks to the importance of trended credit data.

Here’s why: trended credit data examines the longer term trajectory of credit behaviors as opposed to a snapshot or single point in time from the prior month. A model that uses trended credit data (like VantageScore 4.0) is better able to understand more recent credit behaviors and relies less on some of the more conventional attributes used by models that focused on the tenure, breadth and depth of credit usage – which, by definition, negatively impacts those who are new to credit.

In other words, the richness of trended credit data allows our data scientists to extrapolate predictive behaviors from consumers who choose to open less credit accounts.

We’ll share more of these types of insights in the coming weeks and months. For now, we’ve highlighted some insights here, but I also encourage you to follow us on LinkedIn where we can continue the conversation.

As in prior years, we won’t be putting out a December newsletter so this will be the last one for 2018. It was a great year for VantageScore in our 12th year of continuous growth, and we thank you for supporting our mission and reading our newsletter.

Happy holidays to all,

Barrett Burns

CEO and President, VantageScore Solutions

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