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Recovery process optimization using survival regression

Author

Listed:
  • Jiří Witzany

    (Prague University of Business and Economics)

  • Anastasiia Kozina

    (Prague University of Business and Economics)

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.

Suggested Citation

  • Jiří Witzany & Anastasiia Kozina, 2022. "Recovery process optimization using survival regression," Operational Research, Springer, vol. 22(5), pages 5269-5296, November.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00703-3
    DOI: 10.1007/s12351-022-00703-3
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    More about this item

    Keywords

    Decision support systems; Credit risk modeling; Survival analysis; Scoring; Debt recovery;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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