The Relationship Between Credit Scores & Default Probability
By: Geff Woodward
The relationship between credit scores and default probability is a fundamental concept in the field of credit risk assessment. Credit scores are numerical representations of an individual's, or entity's, creditworthiness, and they are used by lenders to predict the likelihood of a borrower defaulting on their loans or credit obligations. Default probability refers to the likelihood that a borrower will fail to make required payments on their debt obligations, leading to default.
The relationship between credit scores and default probability is generally inverse, meaning that as a person's credit score improves, their likelihood of defaulting decreases, and vice versa. For this reason, one could think that the higher the credit score, the less likely the loan default rate.
Unfortunately, this is not an entirely accurate assessment. Recent analyses demonstrate the ineffectiveness of the conventional scoring mechanism. For example, during the 2008 subprime mortgage crisis, default rates for all borrowers increased precipitously, regardless of credit score. Even worse, one study found that “higher FICO scores have been associated with bigger increases in default rates over time.”
In the above figure it is shown that the higher the credit score, the larger the increase in serious delinquency rates between 2005, 2006 and 2007. For example, for borrowers with the lowest credit scores (FICO scores between 500 and 600), the serious delinquency rate in 2007 was 2X (twice) as large as in 2005—an increase of nearly 100 percent over the two years. For borrowers with the highest credit scores (FICO scores above 700), the serious delinquency rate in 2007 was almost 4X (four times) as large as in 2005—an increase of nearly 400 percent. In addition, the serious delinquency rate in 2007 for the best-FICO group was almost the same as the rate in 2005 for the worst-FICO group.
In summary, the urge to renovate the current credit scoring model is higher than ever. If we want to accurately and correctly assess consumers’ financial health, and mitigate future lending crises, lending institutions must adopt new algorithms and methods to evaluate individual consumers' solvency. Types of credit is not a predictor, but rather a means for credit bureaus to push consumers to have many different types of debt/credit and hence different ways to potentially get into financial trouble. The more “diverse” types of credit a consumer has, the better the consumer will score for this factor. Having a diverse portfolio of credit is, by no stretch of the imagination, a predictor for future credit behavior or credit risk.
VeraScore’s patented platform is fundamentally different to the legacy credit rating model. While credit scores are a one-time snapshot that rely on weeks-old reporting from lenders, VeraScore delivers a more accurate and detailed (holistic, objective, transparent, and ultimately predictive) real-time analysis of a consumer’s financial health. As a result, a more realistic picture of the borrower is presented and default risk is considerably lowered and mitigated.