Utilizing the KXEN Analytic Framework, PreTell provides a numeric score on an index of zero to 1,000 that predicts the likelihood of an individual loan prepaying within a six-month forecast window.

"KXEN's proven technology platform enabled us to exploit our databases and analytic expertise to maximum advantage delivering a powerful solution to our clients," says Richard Harmon, Ph. D., senior vice-president, scoring analytics and services at LoanPerformance. "For the first time, institutions will have a common method for evaluating and quantifying near-term, loan-level prepayment risk embedded in their servicing and investment portfolios. This is a tremendous competitive advantage."

In addition to predicting the overall likelihood for a loan to payoff, PreTell provides Refinance and Mover scores that measure the likelihood for the payoff being a refinance or a home sale, respectively. Because different factors drive the decision to move or refinance, mortgage lenders and servicers can more precisely target their retention marketing efforts and internal models can more accurately reflect borrower behaviour.

PreTell scores differ from traditional prepayment models by the fact that they take into account a much wider, deeper and robust level of information about the household, the property and the loan performance history.

"LoanPerformance provides an optimal environment for KXEN to show its value – an organisation that's business is based on the acquisition, simplification and dissemination of large volumes of data," said Ken Bendix, president, North America, KXEN. "Society continues to record information electronically at a staggering rate.  As the trend continues, the value of predictive analytics technologies such as KXEN increases exponentially, as we’re seeing with the increasing number of organisations from a wide variety of industries integrating our technology."