Scoring Solutions
I recently had the pleasure of speaking at the Philadelphia Federal Reserve Bank’s Community Development Studies and Education Department’s conference “The Impact of Workout Options on Borrower’s Credit Reports and Scores.” The conference was well attended by regulators, lenders and consumer credit and housing counselors. FICO’s research on the score impact of mortgage delinquencies and default greatly interested the home preservation and credit counselor participants. During the event, I was also asked, “Why are credit scores different for the same consumer across the different CRAs?” We get that question a lot, and here’s what I shared with them.
To be most valuable to a lender, a credit score must extract all the predictive power from all of the credit data about a consumer that is stored at that consumer reporting agency (CRA). This provides the most comprehensive and reliable view of the consumer’s credit risk.
That sounds challenging enough, but consider that lenders contribute data voluntarily to the CRAs. As a result, if there is a score difference across bureaus for a given consumer, then that score difference is 85-90% driven by data differences in the underlying credit. For example, differences could include:
- A tradeline may be reported to one CRA and not another;
- Reported delinquency status may differ between CRAs;
- Outstanding balance on a tradeline may differ between CRAs.
Often this is caused by the lender reporting data to CRAs at different times. Or the lender might report only to one or two CRAs, but not all three. Consumers obviously benefit when their positive data is in the files of all three CRAs. Consumers also would seem to benefit if their negative data is missing from one or two CRAs because the lender doesn’t report to all three. Lenders can make the best decisions when complete account data has been contributed to all three CRAs.
Another potential contributor to a difference in scores is how each of the CRAs structure, maintain and define the data that lenders have contributed. The CRAs consider data management and data classification to be a competitive differentiator. For example, at one of the bureaus, the definition of “industry type” is not as granular as it is at the other two bureaus. This could lead to potential inconsistencies in the classification of consumer finance company accounts across the bureaus. Differing data management practices also can lead to certain credit tradelines not being represented at a particular bureau. For example, one CRA has a policy to not display authorized user tradelines with negative information on their reports; the other two bureaus do. These differences in data management are well known to lenders that leverage the data from multiple CRAs, and to FICO which has partnered with each of the CRAs for more than two decades to build and distribute consumer credit scores.
When the credit file data is essentially the same between the CRAs, FICO’s scoring logic yields equivalent results. When the credit file data differs, the resulting FICO® Scores will likely differ at least slightly but will still accurately represent the data available at each CRA. Because the CRAs define, structure and maintain data differently, FICO develops its scoring models for each CRA to get the maximum predictive value from that CRA’s unique data. This process yields a more valuable score for lenders and borrowers compared to the alternative approach of bypassing data differences and focusing only on those data characteristics that are common to all three CRAs. A score based only on the lowest common denominator between CRAs cannot extract the full predictive value from each CRA’s data.
