BasePoint's DefaultRisk model uniquely leverages learned risk characteristics using pattern recognition technology for default risk and has the ability to apply this technology to extremely large portfolios with sub-second loan scoring. The statistical models are based on delinquency and default data across more than 12 million loans in BasePoint's unique mortgage loan consortium. This solution provides fast and precise risk scoring and classification of loan populations. When combined, the solutions accurately separate the highest risk loans for further diligence to help guide loss mitigation and pool valuation efforts. These scores can further be used to quickly and systematically identify the best loans for modification or refinance.

"The mortgage investment community is under tremendous pressure to quickly evaluate loan and borrower risk in an effort to maximize performance and reduce the increasing rate of delinquency and default within loan portfolios," said Tim Grace, CEO and president of BasePoint Analytics. "The DefaultRisk solution enables investors to quickly expose potential risk and delivers portfolio transparency to reduce loss. This solution will further help to stem the current default and foreclosure cycle by increasing the efficiency and success rates of modification and re-finance programs."

The DefaultRisk model produces score-based rank ordering of loans identifying the likelihood that a loan contains predictive characteristics indicative of future loss. These scores are based on the confirmed risk patterns that are captured and updated daily as part of BasePoint's growing data consortium. Accompanying the risk score are likely risk indicators for the potential high risk loans. Lower scoring loans can be utilized for prioritizing loss mitigation efforts, enabling the lowest risk loans to get processed more quickly, providing efficiency, expense reduction, and meaningful savings to the investors and borrowers in the modification and refinance process. DefaultRisk score is based on statistical models, and is more precise than traditional index-based or valuation tools. Clients realize a large return on investment with proven low false positives.

Proven to identify 40-50% of default risk within the top 10% of the scores, the DefaultRisk model provides superior mortgage default risk identification. Traditional credit risk scores do not take into account the breadth of borrower and loan information that the BasePoint predictive model uses, enabling DefaultRisk to provide a more precise assessment of the default risk for loans and portfolios. The DefaultRisk model is complementary to the FraudMark for Mortgage Investors solution which specifically evaluates the risk of fraud, enabling targeted reviews to identify the misrepresentation. This set of solutions provides comprehensive risk management for servicers and investors.