Forecasting consumer credit recovery failure: classification approaches

Hyeongjun Kim, Hoon Cho and Doojin Ryu

Journal of Credit Risk

Abstract: This study proposes an advanced credit evaluation method for nonperforming consumer loans, which may serve as a new investment opportunity in the post-pandemic era. Our results, based on both a unique account-level data set and machine learning techniques, imply that the artificial neural network algorithm with demographic and account-related variables performs the best in terms of predicting consumer credit recovery failure within 24 months. We also find that the key determinants of such failures are the total amount of delinquent debt, the applicant’s age and the maximum length of the overdue period. A forecasting model using the random forest algorithm can also be improved by using additional information that is determined after a debtor applies for the credit recovery program. Our findings have practical implications for banks, financial institutions and investors who need to manage and evaluate nonperforming loans.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-credit-risk/788114 ... ification-approaches (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ1:7881146

Access Statistics for this article

More articles in Journal of Credit Risk from Journal of Credit Risk
Bibliographic data for series maintained by Thomas Paine ().