How do you identify which accounts have a higher likelihood to pay? Every collections firm wants to get better at answering this question. TransUnion claims that firms can – with better, fresher account data. Scoring accounts with a broader data set and more historical data can help firms better separate the uncollectable accounts from those that might pay, according to the credit scoring company.

TransUnion claims that with an expanded data set, its own, new model, the CreditVision Recovery Model, yields a 3% increase in the number of payers, a 9% increase in the number of borrowers, and 13% more dollars recovered from payers in the top tier.

TransUnion’s proprietary model was designed to incorporate new data types more representative of recent economic conditions. For the analysis, TransUnion uses 30 months of account history and nine separate data elements, including balance, payment due, payment made, high credit, past due and credit limit. For the analysis, TransUnion looked at several forms of debt, including medical, credit card and student loan debt. The company then analyzed more than 40 million accounts from 15 collection agencies and debt buyers.

The economy is changing and collections firms can’t rely on aging data if they want to remain effective, says TransUnion’s specialized risk group president Peter Ghiselli.

“As the economy continues to recover, collection agencies and debt buyers need a broader and fresher data set that is representative of recent economic conditions,” Ghiselli says. “This new recovery model is built on current consumer credit data to incorporate the evolving credit landscape, allowing collectors to see a substantial improvement in the number of payers and dollars recovered.”

“For collection agencies and debt buyers, every dollar recovered is important, but collectors need to prioritize accounts that have a higher likelihood to pay,” he adds.

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