VantageScore Solutions, LLC, developer of the VantageScore® credit scoring model, released the first of two new white papers that demonstrate superior predictive performance when using one generic credit score to verify an initial risk assessment made by another credit score. “The Predictive Value of Credit Score Consistency” white paper also demonstrates that verifying VantageScore 3.0 credit scores specifically at two separate nationwide Credit Reporting Companies (CRCs) yields a higher percentage (75 percent) of score alignment than using two proprietary credit scoring models because the VantageScore model yields more consistent credit scores.
The white paper examines the practice of verification – the alignment of risk assessments from two credit scoring models – to ensure consistency and accuracy in the assessment of a population, which ultimately can result in minimizing default rates for lenders. In this study, VantageScore data scientists sought to answer two questions when validating the need for verification:
1) Was there predictive performance improvement (i.e., Gini1 improvement) when verifying any credit score with another, different brand of credit score from a separate CRC?
2) How often do credit scores “verify” risk assessment?
Through testing, the study found that verified credit scores (regardless of the brand or version) deliver Gini improvements of at least two points higher on the verified population than if only one credit score is used on that same population. Conversely, the population with credit scores that are not verified by the credit score (i.e., those with conflicting verification evaluations) shows a drop in Gini performance of four to five points, meaning risk assessment for these consumers is less reliable. Thus, consumers with unverified credit scores should be managed with greater scrutiny than consumers with verified credit scores.
Additionally, the study uncovered that when two VantageScore 3.0 scores (from two independent CRCs) were used, the credit scores of 75 percent of the consumers in the population were verified. However, when the verification was conducted using two proprietary CRC credit scores from two independent CRCs, just 30 percent of the consumers’ credit scores were verified. The improved verification performance using VantageScore 3.0 is a function of the consistent algorithm used across the three CRCs that is a patented signature feature of all VantageScore models.
“Consistency, accuracy and predictiveness are at the forefront of everything we do at VantageScore Solutions, which is why we have a natural desire to explore how we can further help our end users achieve more profitable outcomes and improve how consumers handle their credit health,” said Sarah Davies, senior vice president, research, analytics and product management, VantageScore Solutions. “The verification approach can further limit default risk and have a positive impact on a lender’s bottom line.”
A second, related white paper will soon be available that examines the upside of a verification process for those consumers with limited credit histories.
While VantageScore 3.0 was used for this study, results would be similar if other versions of VantageScore were used, including VantageScore 4.0.
For more details on “The Predictive Value of Credit Score Consistency” white paper, visit: www.vantagescore.com/consistencyWP.
1 A Gini coefficient compares the distribution of defaulting consumers with the distribution of non-defaulting consumers across the credit score model’s range. A coefficient value of 100 indicates that the model has successfully assigned all defaulting consumers to the lowest score possible and all non-defaulting consumers to the highest score possible. A Gini coefficient of 45 or greater is considered a good result by industry standards.