LoanPerformance Mines Unstructured Data For Collections.

San Francisco -- Increasingly, credit-card lenders are using "unstructured data" to help guide collection and default management decisions, and now the trend is making some inroads in the mortgage arena as well.

LoanPerformance here, in conjunction with Intelligent Results, recently launched ScoreText, a product that allows lenders to turn textual information gleaned from conversations between borrowers and collection counselors into data that can be used to estimate the risk of default or foreclosure. While default management has been the prime use of text scoring, the data can be used for a host of other purposes, such as customer-relationship management, portfolio retention and marketing campaigns.

Richard Harmon, senior vice president for scoring analytics and services at LoanPerformance, said that most scoring systems that analyze borrower behavior rely upon structured numeric data, such as credit scores and payment information.

ScoreText allows lenders to augment these traditional systems with information and observations generated by other forms of interaction with the borrower, including free-form text and collectors' notes as well as e-mails from the borrower.

These "mixed data analytics" can help lenders refine collection and default management decision making, Mr. Harmon said in a recent interview. While traditional, numerical data models have been improved, those improvements yield "significantly diminishing returns because we are basically extracting all of the signals out of the data that we can get."

That unstructured, textual data allows lenders to incorporate what the borrower is actually saying to collection counselors into the scoring methodology. It also allows the collection agent's notes, such as indications that the borrower is lying, to be incorporated into the proprietary scores as well.

"It gives you insight into what the customer is saying as well as what they are doing," he said.