Millions More Scored
Traditional credit scoring models exclude many consumers, often leaving them unable to get credit. For example, other models do not score consumers who are new to the credit market or who use credit infrequently. The VantageScore 3.0 model, which is the most recently introduced model, provides a score to 30–35 million adult consumers who otherwise would be virtually invisible to mainstream lenders. So when lenders use the VantageScore model, they can provide credit to more consumers at the most appropriate terms.
Easier to Understand
When consumers buy their credit scores or get them from their lenders, they receive “reason codes.” Reason codes are explanations for why a consumer’s credit score is not higher. Not only did we simplify the explanation for each reason code, we reduced the number of codes provided. Fewer codes, written in clear, plain English, help consumers take action and improve their credit scores.
In an effort to assist consumers and provide more accurate information about reason codes, we created a free website, ReasonCode.org. When borrowers are better informed, they can manage their credit more wisely. Using the information and recommendations available at ReasonCode.org, consumers can have more confidence in their credit decisions, knowing that the information is coming from a reputable, transparent, and unbiased source.
More Score Consistency
All VantageScore models can be used by lenders across all three credit reporting companies (CRCs). Other score providers develop a specific model for each CRC at different time frames and using different data. This leads to the output of different scores. Because the same VantageScore model can be used at all three CRCs, the only score difference that may exist for a particular consumer is solely attributed to data differences within the three credit files. The data difference could be associated with when the lender “reports” or provides the piece of data (such as a loan’s current balance) to the CRC versus when the lender reports the data to another CRC.
Matching Credit with Borrowers
Because the VantageScore model is so highly predictive and consistent, consumers are more appropriately matched with the types of credit and the right terms for credit. With less predictive models, consumers could be charged a higher interest rate than they deserve.
The information on this page is direct from VantageScore.com We (TCS) did not create the formula within the actual credit scoring algorithms. Our formula is specifically geared toward maximizing credit reports and credit scores within all credit scoring systems. VantageScore 3.0 is one of many different credit scoring models.
Maximizing the Credit Universe
One result of more conservative lending practices following the Great Recession has been the incorporation of judgmental criteria in lending strategies which give greater scrutiny of any previous bankruptcy and that require consumers to have a “thick file.”1 The implicit assumption of the criteria is that more information on the consumer credit file enables a better assessment of the consumer’s risk level.
This paper will show that qualitative criteria of this nature may not only fail to reduce origination risk, but can also substantially reduce the accessible lending universe for reasons unrelated to consumer risk assessment.
This paper contains two sections. First, a review of the data and analytic processing elements, such as the composition of the credit file, consistency of file composition across the primary three national reporting companies (CRCs) and credit score model design. Each element will be discussed and its impact on the accessible universe evaluated. The review will show that for certain judgmental criteria lending strategies, they can reduce the lending universe by as many as 60 million consumers.
Second, a case study analyzes the impact of these elements on a sample mortgage originations strategy. The case study demonstrates how the strategy can be enhanced to expand the universe by 19 million consumers and simultaneously lower the overall risk of the originated loan portfolio.
“A consumer with less than three credit accounts or “trades” in his or her credit file is defined as having a “thin file” where as a consumer with three or more accounts/trades is defined as having a “thick file.”