Recovery process optimization using survival regression

Jiří Witzany and Anastasiia Kozina
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Anastasiia Kozina: Prague University of Business and Economics

Operational Research, 2022, vol. 22, issue 5, No 19, 5269-5296

Abstract: Abstract The goal of this paper is to propose, empirically test and compare different logistic and survival analysis techniques in order to optimize the debt collection process. This process uses various actions, such as phone calls, mails, visits, or legal steps to recover past due loans. We focus on the soft collection part, where the question is whether and when to call a past-due debtor with regards to the expected financial return of such an action. We propose to use the survival analysis technique, in which the phone call can be compared to a medical treatment, and repayment to the recovery of a patient. We show on a real banking dataset that, unlike ordinary logistic regression, this model provides the expected results and can be efficiently used to optimize the soft collection process.

Keywords: Decision support systems; Credit risk modeling; Survival analysis; Scoring; Debt recovery (search for similar items in EconPapers)
JEL-codes: C14 G21 G28 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12351-022-00703-3

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