Predictive modeling to combat collections

Neil Skilling's picture

I was recently interviewed by Tom Groenfeldt of Banking Technology about an application for modeling techniques that isn’t usually front of mind: bank collections. Unfortunately, (and not entirely surprisingly) in the midst of the recent financial crisis, banks have seen an increase of late loan payments, bankruptcy, and home foreclosures. As a result, banks are paying closer attention to their customers’ borrowing and payment patterns, not only for their customers’ benefit, but also for their companies’ protection.

Predictive modeling is well understood in financial risk communities. In this instance, modeling long term outcomes and measuring certain behaviors and factors, such as changes in how customers use their overdrafts, can help identify those who may be at risk. With predictive information, banks can offer their customers help, like setting up automatic transfers from checking to credit cards to avoid late payment fees. Additionally, banks that are equipped with this insight can avoid annoying customers who are likely to resolve their issues on their own, which can help prevent costly customer churn.

This is an interesting application for predictive modeling, and it’s one that’s seeing increasing use as of late. To read the whole article, click here



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