Forecasting Consumer Credit Recovery Failure: Classification Approaches

24 Pages Posted: 21 Oct 2021

See all articles by Hyeongjun Kim

Hyeongjun Kim

Yeungnam University - Yeungnam University

Hoon Cho

Korea Advanced Institute of Science and Technology (KAIST)

Doojin Ryu

Sungkyunkwan University

Date Written: October 25, 2020

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.

Keywords: classification; consumer credit recovery; credit risk; machine learning; nonperforming loans

Suggested Citation

Kim, Hyeongjun and Cho, Hoon and Ryu, Doojin, Forecasting Consumer Credit Recovery Failure: Classification Approaches (October 25, 2020). Journal of Credit Risk, Vol. 17, No. 3, Available at SSRN: https://ssrn.com/abstract=3946304

Hyeongjun Kim

Yeungnam University - Yeungnam University ( email )

Daehak-ro 280, Gyeongsan-si, Gyeongsangbuk-do Gyeo
Republic of Korea
KS 38541
Korea, Republic of (South Korea)

Hoon Cho

Korea Advanced Institute of Science and Technology (KAIST) ( email )

373-1 Kusong-dong
Yuson-gu
Taejon 305-701, 130-722
Korea, Republic of (South Korea)

Doojin Ryu (Contact Author)

Sungkyunkwan University ( email )

53 Myeongnyun-dong 3-ga Jongno-ju
Guro-gu
Seoul, 110-745
Korea, Republic of (South Korea)

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