FIS Blog

Debt Buyers Taking Debt Buying to the Next Level with Statistical Models


Mike Kresse | Thursday, March 3, 2016

In order to calculate the optimum price for a given portfolio, debt buyers are increasingly turning to solutions based on statistical modeling. Drawing upon hundreds of millions of observations, this type of solution can help debt buyers make better, smarter decisions by enabling them to understand the value of a portfolio and the likelihood of payment more effectively. In some cases, debt buyers may conclude that there is not enough value for a particular portfolio to be worthy of consideration. In other cases, the value may indicate that a positive ROI is likely and they should raise their bid to ensure a purchase.

As well as helping debt buyers determine the right price for a portfolio, statistical modeling can also play a key role in the debt collection process once a portfolio has been purchased or placed for collections. The challenge here is to decide how to allocate and prioritize individual accounts within the portfolio for the proper collection strategy. If 30 percent of a company’s portfolio is not likely to pay, you don’t want collectors wasting time and money calling into those 30 percent. But how can you identify that 30 percent?

Instead of using data from credit bureaus, statistical modeling solutions use sophisticated scoring models which draw upon industry-specific data and payment behavior in order to assign a value to debts within the portfolio. The resulting score will indicate not only the likelihood of payment, but also the expected value of any given account.

Armed with this information, debt buyers and collectors gain a better understanding of the best approach to take for specific debts. Statistical scoring can be used to decide whether debts should be collected in-house, passed on to a collection agency or sold off. Statistical scoring can also be used to decide the type of approach that should be taken by the collection team – and even which collector should be allocated the account. The accuracy of these statistical models continues to improve as more data is incorporated.

If this sounds too good to be true, providers may utilize a retrospective study based upon previous purchases by the debt buyer or collector. By producing a report based on a portfolio which has already been worked through, the provider can demonstrate the accuracy of the statistical modeling compared to the actual outcome of a previous portfolio.

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Tagged in: Credit and Collections, Corporate Solutions

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