Improving debt collection via contact center information: A predictive analytics framework

Debt collection is a very important business application of predictive analytics. This task consists of foreseeing repayment chances of late payers. In this sense, contact centers have a central role in debt collection since it improves profitability by turning monetary losses into a direct benefit to banks and other financial institutions. In this paper, we study the influence of contact center variables in predictive models for debt collection, which are combined with the financial information of late payers. We explore five different variants of three predictive analytics tasks: (1) the probability of successfully contacting a late payer, (2) the probability of achieving a contact that results in a promise to pay a debt, and (3) the probability that a defaulter repays his/her arrears. Four research questions are developed in the context of debt collection analytics and empirically discussed using data from a Chilean financial institution. Our results show the positive impact of the combination of the two data sources in terms of predictive performance, confirming that valuable information on late payers can be collected from contact centers.