VantageScore Solutions, the company behind the VantageScore credit scoring models, announced today that it has published its latest model performance assessment of VantageScore 4.0, the company’s most recently introduced consumer credit scoring model.
Data scientists at VantageScore Solutions examined the performance of VantageScore 4.0 in three specific areas:
1. Predictiveness for both the mainstream consumer population (i.e., those who are conventionally scored by generic scoring models) and the approximately 40 million consumers who are considered “Newly Scored” (i.e., those who can be scored with VantageScore 4.0 but fail to meet the criteria required by conventional models).
2. Consistency of VantageScore 4.0, based on the credit scores obtained from the three national credit reporting companies (CRCs) in terms of the variance between the scores and predictive performance.
3. Statistical bias, if any, related to the ethnicity of various consumer groups.
The model performance assessment, conducted in 2019, represents the second annual assessment of VantageScore 4.0 (which had been launched in April 2017). The model is the first and only tri-bureau credit scoring model to incorporate trended credit data and leverage machine learning for superior predictive insights and inclusivity.
Highlights of the 2019 model performance assessment include:
PROMOTING TRANSPARENCY AND MODEL GOVERNANCE
As part of its mission, VantageScore Solutions annually assesses all VantageScore models and shares the results publicly with market participants and regulators to promote transparency and support model governance activities.
The model performance assessment is based on a sample of 45 million anonymized and randomly selected U.S. consumer credit files obtained from the databases of each CRC (15 million files from each of the three CRCs), which were analyzed over a standard two-year timeframe (2016-2018) using standard statistical tests.
TRENDED CREDIT DATA
The VantageScore 4.0 model has implemented first-to-market innovations, such as the use of trended credit data, which captures the trajectory of borrower behaviors over time.
To measure the performance benefits provided by trended credit data, VantageScore 4.0’s prediction results were compared to those of previously developed credit scoring models that contain only static (point in time) credit attributes. Test results show an improvement in predictive performance as a result of the trended credit data, particularly in the higher credit score ranges where risk of default can be more difficult to identify and assess. As a result, users of VantageScore 4.0 will benefit by their ability to more finely tune credit offers to these higher scoring, lower risk consumers.
VantageScore 4.0 also is the first and only tri-bureau credit scoring model to leverage machine learning techniques in the development of scorecards for those who cannot be scored by conventional credit scoring models (i.e., the Newly Scored population segment).
Within this population of consumers, the performance assessment shows that VantageScore 4.0 outperforms VantageScore 3.0 in new account originations (7.4%) and existing accounts (2.3%).
Moreover, the assessment showed no statistically significant difference in the default rates observed within each VantageScore 4.0 score band for Mainstream consumers as compared with the Newly Scored consumers. This finding demonstrates that the VantageScore model, while adhering to safety and soundness principles, is still able to assign an accurate score to those who cannot get a credit score when conventional models are used.
“Despite the healthy economy, it is important to continually assess the performance of credit score models using a number of parameters to ensure safety and soundness, as well as fairness. While as a model developer, we seek to ensure the model is properly rank ordering, lenders should monitor for absolute risk,” said Barrett Burns, president & CEO of VantageScore Solutions. “Inevitably, the credit cycle will change and lenders must be diligent about performing frequent testing and validation of their existing models to guard against the potential that risk will increase and cause an unexpected uptick in defaults.”
While we always share the results of our models’ performance assessments publicly online, we encourage lenders to perform their own model assessments within their lending environment when considering whether to upgrade to new models. This year’s assessment can be found at www.vantagescore.com/ModelPerformanceAssessment19.
About VantageScore Solutions
Credit scores can impact many aspects of your life, everything from whether you are able to get a loan and how much interest you will have to pay to whether you are able to rent an apartment. At VantageScore, we understand the impact credit scores have and we take it seriously.
VantageScore Solutions, LLC (www.VantageScore.com) is the independently managed company that owns the intellectual property rights to the VantageScore credit scoring models and is the leader in scoring innovation. Recently introduced VantageScore models score approximately 40 million more consumers who typically are not scored by conventional models – without sacrificing predictiveness.
VantageScore credit scores are used by lenders, landlords, utility companies, telecom companies, and many others to determine creditworthiness. In fact, a recent study found that more than 10.5 billion VantageScore credit scores were used in June 2017-July 2018. Of those, approximately 6 billion scores were used by nearly 2,500 financial intuitions and over two billion VantageScore credit scores were provided directly to consumers through dozens of websites and lenders who provide their users and customers with their credit scores for free. By using the VantageScore model, these enterprises have access to many more consumers, and in turn, consumers have greater access to mainstream credit.
While there are many credit scoring models in the industry, the “win-win” for VantageScore is its innovative, highly predictive, patent-protected, tri-bureau scoring methodology that provides lenders and consumers with more consistent credit scores across all three national credit reporting companies.