Many financial institutions and collection agencies now find it more challenging and expensive to collect debt than ever before. Consumers direct their financial resources to necessary living expenses, which have increased, meaning credit and mortgage lenders are taking a back seat. To get a share of the shrinking pie, collectors must selectively target the right debtors with the right strategies at the right time to help increase unit yields, improve productivity and reduce costs.
This paper examines three methods of analyzing, prioritizing and managing delinquent accounts and implementing strategies to optimize collection results while minimizing expenses. These three techniques are the expert method, traditional predictive analytics and strategic predictive analytics.
The paper will illustrate why strategic predictive analytics is emerging as a game changer in collections—showing how financial institutions and collection agencies that employ strategic predictive analytics can better target the right debtors at the right time with the most cost-effective collection strategies to improve collection rates and reduce costs.
It takes money to collect money; focusing the most cost-effective strategies on those accounts that are more likely to pay can generate millions in annual returns.
Furthermore, the paper will outline how financial institutions and collection agencies can apply the right analytic models around data and leverage that information through the entire collections life cycle. This exploration will also demonstrate how strategic predictive analytics can be used to fine-tune collection strategies to get the best results.