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SAN FRANCISCO, Calif. AND BELLEVUE, Wash. – January 11, 2005 – LoanPerformance, the leader in residential mortgage data and analytics, and Intelligent Results, a leader in customer relationship analytics solutions, today jointly announced the availability of ScoreText, a new predictive modeling solution with wide application across the mortgage industry. ScoreText integrates structured data with unstructured text data, enabling mortgage servicers to better understand and more accurately predict customer behavior in servicing and collection efforts.
Historically, mortgage servicers have identified and prioritized potential problem loans using structured numeric data. However, until now, they have not been able to systematically access information and observations generated by other forms of interaction with borrowers. This mixed-data analytics approach combines free-form text from lender's and collector's notes, call centers, CRM systems and borrower emails, with structured data from traditional sources including credit scores, payment histories, loan balances, customer demographics, property record information, collections systems, and account master files.
"Today's best predictive models only incorporate about 20% of the available data," said Richard Harmon, Ph. D., senior vice president, scoring analytics and services at LoanPerformance. "By exploiting the available unstructured data that makes up the other 80%, mortgage servicers can not only improve their ability to predict customer behavior, but can have a much better understanding of the key factors differentiating behavior. We believe many servicers will be able to improve their current loss mitigation performance by as much as 50%."
"Extracting this textual data and incorporating it into predictive loss mitigation models provides servicers with a much more powerful tool to determine which loans will cure themselves and which should be handled using forbearance, short sales, foreclosure or other methods," added Harmon.
The ScoreText solution was jointly developed by LoanPerformance, which currently tracks the monthly prepayment and delinquency performance of 46 million loans, and Intelligent Results, a company that has already successfully utilized a mixed-data approach to improve the servicing and collection functions in the credit card, auto and other consumer lending industries. For example, one credit card issuer improved annual returns by $4.2 million using Intelligent Results' mixed-data models to target early stage settlement offers.
"Current collections and recovery processes utilize both automated systems and human interaction to determine a delinquent borrower's ability to repay and the type of workout the servicer should pursue," said Craig Focardi, senior analyst at TowerGroup. "But important information is locked in unstructured memo fields and it is expensive to add new data fields to servicing systems. This unique offering can help mortgage servicers by adding greater predictive ability to existing collections and loss mitigation systems and processes. This helps keep more borrowers in their homes and lower mortgages delinquencies and foreclosures."
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