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.

债务催收是预测分析的一个非常重要的业务应用。这项任务包括预测迟交者的还款机会。从这个意义上说，联络中心在收债方面发挥着核心作用，因为它通过将货币损失转化为银行和其他金融机构的直接利益来提高盈利能力。在本文中，我们结合延迟付款人的财务信息，研究联络中心变量对收债预测模型的影响。我们探索了三个预测分析任务的五种不同变体：（1）成功联系迟付款人的概率，（2）实现联系并承诺偿还债务的概率，以及（3）违约者偿还他/她的欠款。在收债分析的背景下提出了四个研究问题，并使用智利金融机构的数据进行了实证讨论。我们的结果显示了两种数据源的组合在预测性能方面的积极影响，证实了可以从联络中心收集关于迟交者的有价值信息。