VantageScore Solutions is releasing a new credit scoring model which it says will enable lenders and others using the system to pull scores on 27 to 30 million previously unscorable consumers.  The company claims its model, called VantageScore 3.0 provides up to 25 percent better predictiveness and uses the consumer-familiar scoring range of 300 to 850.  The model was built using a sample of 45 million anonymous credit files.

Testing of its new model shows that it provides improves predictive strength across all industries and applications but in particular within the key prime and near-prime consumer populations, the most sought after group among mainstream lenders. 

The new model is said to be able to score more individuals than traditional scoring models through introduction of a thirteenth scorecard to generate a predictive score where an individual has little or no recent credit activity.  The model factors in non-tradeline credit data such as collections, public records, and inquiries when active tradeline data is not available and utilizes tradeline data that is aged more than 24 months but remains predictive.  The model also uses rent, utility, and telecom data when it is available.

The improved predictiveness, which is present within the same population for originations in the real estate, auto, and bankcard segments, is demonstrated by nearly identical risk alignment across all three credit reporting companies (CRC); maximum predictive performance for both originations and account management, and provides consumer scores which are within 20 points across all three CRCs for 80 percent of accounts. The latter feature will allow lenders to have confidence in default probabilities regardless of which credit reporting company pulls the score.

The company said the key reasons for the improved performance is using more granular data from all three CRCs which allowed the model's designers to select 150 of the most predictive characteristics from among around 900 that were tested.  This allows the user to separate first mortgage from other mortgage related transactions, facilitating greater intelligence with regard to a borrower's mortgage-related debt.  It also allows more distinct definitions of data, such as the ability to identify student loan accounts from other types of installment accounts and gives more specific measurement of delinquency and default timeframes.

The data sample used to build the VantageScore 3.0 model was developed on consumer behavior from two different two-year timeframes: 2009-2011 (during the economic crisis) and 2010-2012. Each performance timeframe contributed 50 percent of the model's development.  This reduces the model's sensitivity to consumer behavioral shifts over different volatile periods.  The model also sets negative information to neutral if it is coded as occurring during a natural disaster but allows positive information to retain its positive impact.

As part of the product launch VantageScore Solutions has introduce a consumer oriented website,  The site provides a primer on what reason codes are and how they are used, searchable and interactive reason code definitions and explanations, and a glossary of common reason code terms