Trended credit data represents an important change in the way credit behaviors are reported to the three nationwide credit reporting companies (CRCs), Equifax, Experian and TransUnion. Now, instead of providing a static snapshot of credit activity — the traditional way that credit data has been interpreted — trended credit data offers a view over time. It provides a more complete picture of a consumer’s credit profile and helps to assess the trajectory of credit behaviors when determining a credit score.
The result: greater predictiveness, particularly among lower-risk populations.
What is trended credit data?
Trended credit data builds from the data fields already included in consumers’ monthly credit files, information provided by credit reporting companies for decades. Now, however, the data reveals patterns in credit behaviors over time — i.e., the number of balance decreases or increases in a borrower’s utilization. These behaviors are especially predictive when seeking a clearer separation among lower-risk populations like the Prime and Superprime consumer segments, for both new originations and existing accounts.
How using trended credit data affects credit scores
To demonstrate how trended credit data improves credit scoring models, consider two consumers whose scores were identical when using a credit scoring model without trended credit data. (Let’s call them Bob and Bill.) They both have the same outstanding balance at the time of the credit check, the same credit utilization rate, and they both always pay on time.
But with trended credit data, we are able to see that “identical” isn’t really identical. A much sharper picture emerges. By looking at their behaviors over time, the model shows that Bob’s balance has increased significantly in just the last four months, and that he always pays just the minimum each month.
Bill, on the other hand, has the same balance as Bob, but he’s reduced it significantly in the last four months. Further still, trended credit data recognizes that Bill has recently been paying more than the minimum monthly required payment. A model using trended credit data will reward Bill with a higher score than Bob.
Result: A 20% lift in predictive performance
Trended credit data can provide up to a 20% improvement in predictive performance when compared to a generic scoring model that relies only on traditional static attributes. Importantly, these gains occur with Prime and Superprime consumers (i.e., those who have great credit and pose little risk to lenders and creditors), providing a capability for further reducing risk in these lower-risk populations. The performance increase represents an innovative way to gain additional insight into consumer credit behaviors.
Firsts for the industry: VantageScore 4.0
Recently launched, VantageScore 4.0 is the first and only tri-bureau credit scoring model to incorporate trended credit data. While each CRC may have unique fields in its products, VantageScore 4.0 uses a patent-protected, characteristic leveling process that makes attributes consistent across all three CRCs. This means that when a lender uses a VantageScore credit score from one CRC, it will be highly aligned with the other two scores.
Optimizing trended credit data is just one VantageScore 4.0 innovation. It is also the first and only tri-bureau credit-scoring model to be built in anticipation of public record and collection trade suppression associated with the National Consumer Assistance Plan (NCAP). Furthermore, the model leverages machine- learning techniques in the development of scorecards for consumers with dormant credit histories; this approach strengthens VantageScore’s ability to accurately score approximately 30 million consumers who cannot be scored using traditional scoring models.
As credit scoring models attempt to squeeze more predictive strength out of consumer credit files, innovative approaches like the use of trended credit data provide a trustworthy path to increasing the utility of credit scores. Incorporating trended credit data leads the way to new accuracy, stability and consistency in credit scoring.