Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?

Qi Xu, Caixia Liu, Jing Luo and Feng Liu

Pacific-Basin Finance Journal, 2024, vol. 88, issue C

Abstract: The coronavirus disease (COVID-19) has led to a persistent increase in the volatility of the credit market and triggered a series of financial distress and bankruptcy. To investigate whether there are differences in loan default determinants before and during COVID-19 and to identify the most effective predictors of loan default during COVID-19, this study employs machine learning methods to establish a comprehensive loan default prediction model for Peer-to-peer (P2P) lending based on four perspectives: loan characteristics, credit transaction history, personal information, and macroeconomic environment. The results show that the EXtreme Gradient Boosting (XGBoost) outperforms the other models and that credit transaction history plays a vital role in forecasting loan default over the two periods. We also find discrepancies between the effects of consumer price index, purchasing manager’ index, and the number of bidders on loan default before and during the pandemic. Our study contributes to related research fields on loan default prediction by identifying loan default determinants that are more applicable to unstable periods and investigating the impact of COVID-19 on default predictions. Meanwhile, our findings can provide P2P lending investors, platforms, and policymakers with practical implications to reduce uncertainty and losses that result from similar black swan events.

Keywords: Peer-to-peer lending; Credit risk; Loan default; Machine learning; COVID-19 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24003020

DOI: 10.1016/j.pacfin.2024.102550

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