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

Catalina Sánchez, Sebastián Maldonado, Carla Vairetti*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number113812
JournalDecision Support Systems
Volume159
DOIs
StatePublished - Aug 2022

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from ANID PIA/BASAL AFB180003 and FONDECYT-Chile, grants 1200221 (Sebastián Maldonado) and 12200007 (Carla Vairetti). Catalina Sánchez also acknowledges a grant provided by UANDES-FAI for her Ph.D. studies. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions for improving the quality of the paper.

Funding Information:
The authors gratefully acknowledge financial support from ANID PIA/BASAL AFB180003 and FONDECYT -Chile, grants 1200221 (Sebastián Maldonado) and 12200007 (Carla Vairetti). Catalina Sánchez also acknowledges a grant provided by UANDES-FAI for her Ph.D. studies. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions for improving the quality of the paper.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Call center
  • Contact center
  • Data integration
  • Debt collection
  • Predictive analytics

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