Predicting the Repayment Decisions of Korean Vulnerable Debtors: Evidence from an Empirical Study Utilizing a Stacking Algorithm

Youngwoo Jeong ()
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Youngwoo Jeong: KAMCO Research Institute, Korea Asset Management Corporation

Computational Economics, 2025, vol. 66, issue 6, No 25, 5133-5153

Abstract: Abstract The objective of this study is to anticipate the repayment behaviors of financially vulnerable debtors in Korea using a prediction model employing a stacking algorithm. To accomplish this goal, this study is conducted on 1,171,204 debtors with delinquent loans acquired by KAMCO since 2018. Following this analysis, the meta-model successfully predicted 87.7% of vulnerable debtors’ repayment decisions, thus validating its efficacy in discerning debtors with high repayment potential. In addition, it is confirmed that a stacking algorithm can be utilized to construct a prediction model that can be universally applied across a wider range, resulting in a substantial increase in accuracy. This study is significant as it empirically predicts the repayment likelihood of vulnerable debtors in Korea through the utilization of data regarding large-scale non-performing loans. Moreover, the findings of this analysis suggest that financial institutions can enhance their proficiency in loan management by screening and overseeing debtors based on their repayment potential.

Keywords: Household debt; Repayment decisions; Stacking algorithm; Korean vulnerable debtors (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-025-10874-8

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